A method of automatic drawing based on algorithm formula

Abstract

The present invention provides a method for automatic drawing based on an algorithmic formula, and relates to the technical field of data processing. The present invention collects and analyzes users' historical selection and feedback data, and combines the performance evaluation index to construct a dynamic adjustment model, so that the chart drawing process can adapt to the user's demand changes and performance requirements in real time; in addition, by introducing matching degree calculation, the user's real-time calculated value is combined with the historical calculated value to improve the accuracy of chart selection; the invention introduces real-time data of user interaction as a feedback link at the algorithm level. This original design greatly improves the personalized compliance of the chart, and provides users with a more efficient and intuitive data visualization experience.

Description

Technical Field

[0001] The invention relates to the technical field of data processing, and in particular to an automatic drawing method based on an algorithm formula. Background Art

[0002] In the field of data visualization, automated chart drawing has become an urgent and important demand. Traditional chart drawing methods often require users to manually select the appropriate chart type and perform complex configurations, which is not only time-consuming and labor-intensive, but also requires certain data analysis and design capabilities. Subsequently, with the development of artificial intelligence technology, some automatic chart generation tools based on predefined templates have emerged. Although these tools improve the efficiency of drawing, they usually lack sufficient flexibility to handle complex data sets and users' personalized needs.

[0003] In the prior art, publication number CN101650709B discloses a report generation method and a report system, the report generation method comprising: obtaining report attribute information and report formulas set by the user through a user interface; retrieving data within a specified voucher range for calculation according to the report attribute information and the report formula, generating and outputting a calculation result table. The embodiment of the present invention retrieves data within a specified voucher range for calculation according to the report attribute information and report formulas set by the user, generates and outputs a calculation result table, and provides an easy-to-use report generation method and report system, which allows new users to quickly apply without spending much time learning, and is easy to promote and use;

[0004] The Chinese patent with publication number CN101650709B, although describing the technical means of "the user interface obtains the report attribute information and report formula set by the user; according to the report attribute information and the report formula, the data within the specified voucher range is retrieved for calculation, and the calculation result table is generated and output", is unable to capture the user's feedback and interactive behavior in real time, making it difficult to dynamically optimize the chart type according to the user's specific needs; secondly, the existing system often lacks an efficient mechanism to evaluate and optimize the performance indicators of the chart, such as interactive responsiveness, which limits the fluency of the chart application and the user experience; further, the existing technology fails to make full use of historical data to make intelligent recommendations for the user's chart preferences, so that the user often has to repeat the cumbersome process of selection and configuration;

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide a method for automatic drawing based on an algorithm formula to solve the problems raised in the above background technology.

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

[0008] A method for automatic drawing based on an algorithm formula, the specific steps include:

[0009] Step S1, collecting various types of charts and marking them to form {T1, T2, ..., Ti, ..., Tn}, where Ti represents the i-th type of chart, and n represents the total number of chart categories; for each type of chart Ti, pre-storing its associated algorithm formula Fi into the system database to construct an algorithm formula list displayed on the user browsing interface;

[0010] Step S2: The user manually selects the required algorithm formula from the algorithm formula list provided by the system, and then the system will display a data input prompt box related to the selected algorithm formula; the user directly drags the fields mentioned in the algorithm into this prompt box, and the system will automatically perform calculations in the background and generate corresponding calculated values;

[0011] Step S3, obtaining the user's historical selection data for each chart and the corresponding historical calculated value, and performing matching calculation between the calculated value in step S2 and the historical calculated value, and selecting the i-th chart Ti with the highest matching degree as the initial chart;

[0012] Step S4: collecting user feedback data on the initial chart and performance indicator data on the required chart;

[0013] Step S5, obtaining the feedback data in step S4, and analyzing and processing it to generate a feedback evaluation index, which is used to evaluate the conformity of the selection of the initial chart;

[0014] Step S6, obtaining the performance indicator data in step S4, and analyzing and processing it to generate a performance evaluation index, which is used to evaluate the performance level of the required chart;

[0015] Step S7: Obtain the feedback evaluation index and the performance evaluation index, analyze and process them, and construct a dynamic adjustment model for the chart. The model is used to make real-time interactive adjustments based on the feedback data and the performance index data to generate a dynamic adjustment strategy for the initial chart, which specifically includes the following contents:

[0016] The dynamic adjustment model is defined as DAMC, and the calculation formula is as follows:

[0017]

[0018] Among them, e1 and e2 are exponential adjustment parameters used to enhance the nonlinear effects of FEI and PEI in DAMC;

[0019] κ is a performance trade-off parameter used to adjust the strength of the effect of PEI in DAMC relative to FEI;

[0020] ψ is a scaling parameter used to adjust the contribution of the logarithmic function to the model and balance the sensitivity of the overall formula;

[0021] e3 is the smoothing parameter, which is used to avoid the singularity of the logarithmic function when FEI or PEI is close to zero;

[0022] The value range of DAMC is set between (0,1). DAMC close to 1 indicates that according to the comprehensive analysis of FEI and PEI, the dynamic adjustment strategy of the chart is very effective, and the conformity and performance of the chart are optimized;

[0023] When DAMC is close to 0, it means that according to the analysis of FEI and PEI, the dynamic adjustment strategy of the chart is not effective, and the chart needs to be significantly adjusted to improve compliance and performance, and finally form an adjusted chart;

[0024] Step S8: The user chooses to save or export the final optimized chart for reporting or further analysis.

[0025] Furthermore, the user manually selects the required algorithm formula from the algorithm formula list provided by the system, and then the system will display a data input prompt box related to the selected algorithm formula; the user directly drags the fields mentioned in the algorithm into this prompt box, and the system will automatically perform calculations in the background and generate corresponding calculation values, which specifically includes the following implementation steps:

[0026] a. The user manually selects the algorithm formula:

[0027] a1. The user browses the algorithm formula list through the drop-down menu or search box on the interface and selects the required algorithm formula;

[0028] a2. The user clicks on the selected algorithm formula and sends a selection instruction to the system;

[0029] b. The system page displays a data prompt box related to the algorithm formula:

[0030] b1. The system confirms the instruction selected by the user and retrieves the corresponding algorithm formula and its parameter requirements in the background;

[0031] b2. The system retrieves the built-in algorithm formula information and identifies the required data fields;

[0032] b3. The system generates a data input prompt box on the user interface, lists the required fields, and provides an input box or drop-down selection list for each field;

[0033] c. Manually drag the fields mentioned in the algorithm into the prompt box:

[0034] c1. The user searches for the corresponding data field in the data set browsing area next to the data field input prompt box;

[0035] c2. The user clicks or drags to place the found data field into the corresponding input box or selection list;

[0036] c3. If the data field needs to be calculated from multiple sub-fields, the system provides a simple calculator or formula editor for users to use;

[0037] c4. After the user completes the input or selection of the data field, click the confirmation or "Next" button to submit the selected data field;

[0038] d. The system generates calculated values ​​in the background:

[0039] d1. The system verifies the integrity and validity of the data based on the input and selected data fields provided by the user;

[0040] d2. After the data is verified to be correct, the system processes the data in the background according to the selected calculation formula and calculates the required calculation value;

[0041] d3. The system feeds back the calculation results to the user by updating a result display area on the interface or popping up a dialog box containing the results;

[0042] d4. The user has the option to save the calculation results, or export the results to common formats for further analysis or reporting.

[0043] Further, the algorithm formula Fi is defined as Fi(x1, x2, ..., xm), wherein xm represents the mth data field required by the algorithm, and the algorithm formula list is set to L = {F1, F2, ..., Fk};

[0044] The feedback data includes the number of clicks, hovering time, number of modifications, data input time, and usage frequency on the current Ti-type chart, and the number of clicks is marked as DJs, the hovering time is marked as XTt, the number of modifications is marked as XGc, the data input time is marked as SRt, and the usage frequency is marked as SYf;

[0045] The performance indicator data include the response time, error rate, loading time, and functional coverage in the current Ti class chart, and the response time is marked as RT, the error rate is marked as ER, the loading time is marked as LT, and the functional coverage is marked as FC.

[0046] Furthermore, the feedback data in step S4 is obtained and analyzed to generate a feedback evaluation index, which is used to evaluate the conformity of the selection of the initial chart, and specifically includes the following contents:

[0047] The feedback evaluation index is defined as FEI, and the calculation formula is as follows:

[0048]

[0049] Among them, a1, a2, a3, a4 and a5 are weight coefficients, which are positive constants, and a1+a2+a3+a4+a5=1; FEI is adjusted according to the relative importance of each factor; e is the base of the natural logarithm;

[0050] λ1, λ2, and λ3 are attenuation coefficients, which control the contribution of the corresponding index to FEI to decrease with the growth rate;

[0051] DJs is the number of clicks, XTt is the hovering time, XGc is the number of modifications, SRt is the data entry time, SYf is the frequency of use, and μ is the median of SRt, which is used to determine the center point of the data entry time;

[0052] is an exponentially decaying integral function, representing the weighted sum of hovering time, the integral of hovering time: It reflects the user's continued interest in the chart, which decays exponentially over time; Z is the normalization factor to ensure that FEI is within the set range.

[0053] Furthermore, the FEI value range is set to be in the range of (0,1). The closer the FEI is to 1, the higher the conformity of the currently selected initial chart is, and the closer the FEI is to 0, the lower the conformity of the selected initial chart is.

[0054] The value range of FEI (0,1) is divided into three intervals, namely interval 1 (0,0.33), interval 2 [0.33,0.66), and interval 3 [0.66,1); and the conformity evaluation is performed for each interval;

[0055] For interval 1 (0, 0.33), the conformity of the chart is low, and the user satisfaction is less than 33%, which reflects that users have low interest in and use frequency of the chart, and the chart needs to be improved to improve user experience and satisfaction;

[0056] Interval 2 [0.33, 0.66), the conformity of the chart is at a medium level, and the user satisfaction is between 33% and 66%. The chart meets the user needs to a certain extent, but there is still room for improvement;

[0057] In interval 3 [0.66, 1), the chart has a high degree of compliance and a user satisfaction rate of over 66%, indicating that the chart meets the needs of users to a large extent, and the user participation and satisfaction are high, which requires continuous maintenance and fine-tuning optimization.

[0058] Furthermore, the performance indicator data in step S4 is obtained and analyzed to generate a performance evaluation index, which is used to evaluate the performance level of the required chart; specifically, it includes the following contents:

[0059] The performance evaluation index is defined as PEI, and the calculation formula is as follows:

[0060]

[0061] Among them, C2>0 is a calibration constant, Z2 is a normalization constant, which ensures that the value of PEI is within the set range, and the calculation method is Z2=(b1+b2+b3+b4)×η; where η is the adjustment coefficient; 0<η<0.42;

[0062] b1, b2, b3, b4, b5, c1, c2, c3 are positive adjustment parameters, which adjust the weights and influences of the corresponding response time RT, error rate ER, loading time LT and functional coverage FC in the performance evaluation index respectively, and b1+b2+b3+b4+b5=1;

[0063] The value range of PEI is set to (0,1), where PEI close to 1 indicates that the performance of the chart is excellent and all indicators perform well; PEI close to 0 indicates that the performance of the chart needs to be improved; when RT, ER, and LT increase, the scores of the corresponding items in the PEI formula will decrease, so the value of the entire PEI will decrease; when FC increases, the scores of the corresponding items in the PEI formula will increase, so the value of the entire PEI will increase.

[0064] Furthermore, the value range (0,1) of the performance evaluation index PEI is divided into three intervals, namely interval 1 (0, 0.33), interval 2 [0.33, 0.66), and interval 3 [0.66, 1);

[0065] Interval 1 (0, 0.33), this interval represents poor chart performance. The response time, error rate and loading time of key indicators are all higher than the industry standard, and the functional coverage is also far below expectations, which urgently needs comprehensive optimization;

[0066] In interval 2 [0.33, 0.66), the performance of the chart is at a medium level. Although it meets the basic requirements, there is still room for improvement in response speed, accuracy, and loading efficiency. The functional coverage also has the potential to be improved.

[0067] Interval 3 [0.66,1), the chart performance in this interval is good, and all performance indicators have reached or exceeded the industry standards. Only minor adjustments are needed to achieve optimal performance.

[0068] Furthermore, the DAMC value range (0,1) is divided into the following intervals, and a corresponding dynamic adjustment strategy is described for each interval;

[0069] In interval 1 (0, 0.4), the user experience and performance of the chart are poor, and more than 50% of the relevant content needs to be optimized; FEI and PEI are set low, and the degree of improvement of the chart is indicated by adjusting the DAMC model parameters; if DAMC ≤ 0.3, the chart needs to be fully reconstructed; if it is between 0.3 and 0.4, targeted optimization is required;

[0070] Interval 2 [0.4, 0.7), the chart shows moderate compliance and performance in this interval. The FEI and PEI values ​​indicate that to achieve a higher DAMC score, the chart needs to be optimized by 30% of its content. If DAMC < 0.6, the chart function or performance needs to be enhanced based on user feedback; if DAMC is between 0.6 and 0.7, slight adjustments are sufficient;

[0071] Interval 3 [0.7, 1) is the high interval of DAMC. The performance and user satisfaction of the chart are very high. Slight adjustments are made to FEI and PEI to slightly improve the DAMC value. In this interval, the chart only needs to be optimized in details to maintain or slightly improve the performance and user experience.

[0072] Compared with the prior art, the beneficial effects of the present invention are: by collecting and analyzing the user's historical selection and feedback data, combined with the performance evaluation index, a dynamic adjustment model is constructed, so that the chart drawing process can adapt to the user's changing needs and performance requirements in real time; in addition, by introducing matching calculation, the user's real-time calculated value is combined with the historical calculated value to improve the accuracy of chart selection; the invention introduces real-time data of user interaction as a feedback link at the algorithm level. This original design greatly improves the personalized compliance of the chart and provides users with a more efficient and intuitive data visualization experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 It is a schematic diagram of the overall method flow of the present invention;

[0074] Figure 2 Draw a flow chart for existing traditional methods;

[0075] Figure 3 This is a schematic diagram of the process of step S2 of the present invention;

[0076] Figure 4 This is a table of specific experimental data for the present invention. DETAILED DESCRIPTION

[0077] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0078] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0079] Embodiment 1:

[0080] See also Figure 1 and Figure 3 , the present invention provides a technical solution:

[0081] A method for automatic drawing based on an algorithm formula, the specific steps include:

[0082] Step S1, collecting various types of charts and marking them to form {T1, T2, ..., Ti, ..., Tn}, where Ti represents the i-th type of chart, and n represents the total number of chart categories; for each type of chart Ti, pre-storing its associated algorithm formula Fi in the database to construct an algorithm formula list displayed on the user browsing interface;

[0083] Step S2: The user manually selects the required algorithm formula from the algorithm formula list provided by the system, and then the system will display a data input prompt box related to the selected algorithm formula; the user directly drags the fields mentioned in the algorithm into this prompt box, and the system will automatically perform calculations in the background and generate corresponding calculated values;

[0084] Step S3, obtaining the user's historical selection data for each chart and the corresponding historical calculated value, and performing matching calculation between the calculated value in step S2 and the historical calculated value, and selecting the i-th type of chart with the highest matching degree as the initial chart;

[0085] Step S4: collecting user feedback data on the initial chart and performance indicator data on the required chart;

[0086] Step S5, obtaining the feedback data in step S4, and analyzing and processing it to generate a feedback evaluation index, which is used to evaluate the conformity of the selection of the initial chart;

[0087] Step S6, obtaining the performance indicator data in step S4, and analyzing and processing it to generate a performance evaluation index, which is used to evaluate the performance level of the required chart;

[0088] Step S7, obtaining the feedback evaluation index and the performance evaluation index, and performing analysis and processing to construct a dynamic adjustment model of the chart, which is used to generate a dynamic adjustment strategy for the initial chart based on the real-time interactive adjustment of the feedback data and the performance index data, and finally form an adjusted chart;

[0089] Step S8: The user chooses to save or export the final optimized chart for reporting or further analysis.

[0090] Embodiment 2:

[0091] Further explanation based on the first embodiment, the user manually selects the required algorithm formula from the algorithm formula list provided by the system, and then the system will display a data input prompt box related to the selected algorithm formula; the user directly drags the fields mentioned in the algorithm into this prompt box, and the system will automatically calculate in the background and generate the corresponding calculated value, which specifically includes the following implementation steps:

[0092] a. The user manually selects the algorithm formula:

[0093] a1. The user browses the algorithm formula list through the drop-down menu or search box on the interface and selects the required algorithm formula, such as "net profit margin";

[0094] a2. The user clicks on the selected algorithm formula and sends a selection instruction to the system;

[0095] b. The system page displays a data prompt box related to the algorithm formula:

[0096] b1. The system confirms the instruction selected by the user and retrieves the corresponding algorithm formula and its parameter requirements in the background;

[0097] b2. The system retrieves the built-in "net profit margin" algorithm formula information and identifies the required data fields as "report date", "net profit" and "operating income";

[0098] b3. The system generates a data input prompt box on the user interface, lists the required fields, and provides an input box or drop-down selection list for each field;

[0099] c. Manually drag the fields mentioned in the algorithm into the prompt box:

[0100] c1. The user searches for the corresponding data field in the data set browsing area next to the data field input prompt box;

[0101] c2. The user clicks or drags to place the found data field into the corresponding input box or selection list;

[0102] c3. If some data fields need to be calculated from multiple sub-fields, the system provides a simple calculator or formula editor for users to use;

[0103] c4. After the user completes the input or selection of the data field, click the confirmation or "Next" button to submit the selected data field;

[0104] d. The system generates calculated values ​​in the background:

[0105] d1. The system verifies the integrity and validity of the data based on the input and selected data fields provided by the user;

[0106] d2. After the data is verified, the system processes the data in the background according to the calculation formula (net profit / operating income) of the selected "net profit margin" and calculates the required calculated value. The calculated value in this embodiment is the net profit margin value;

[0107] d3. The system feeds back the calculation results to the user by updating a result display area on the interface or popping up a dialog box containing the results;

[0108] d4. The user has the option to save the calculation results or export them to common formats such as CSV or Excel files for further analysis or reporting.

[0109] Embodiment three:

[0110] Further explained on the basis of the second embodiment, the algorithm formula Fi is defined as Fi(x1, x2, ..., xm), where xm represents the mth data field required by the algorithm, and the algorithm formula list is set to L = {F1, F2, ..., Fk};

[0111] The calculation of the matching degree between the calculated value in step S2 and the historical calculated value, and selecting the i-th type chart with the highest matching degree as the initial chart, specifically includes the following contents:

[0112] The user browses the algorithm formula list L = {F1, F2, ..., Fk} on the interface and selects the desired algorithm formula Fk from it;

[0113] The system receives the algorithm formula Fk selected by the user and calls the data field requirements {x1, x2, ..., xm} associated with it from the database;

[0114] The system displays a data prompt box on the user interface, instructing the user to drag the fields {d1, d2, ..., dm} in the data source to the corresponding prompt box, where dm represents the mth field in the user data source;

[0115] After the user operates, the system background automatically receives the field mapping and calculates the value of the algorithm formula Fk (d1, d2, ..., dm);

[0116] Obtain the user's historical selection data H = {(Ti, Fi, vi)}, where vi is a historical calculated value;

[0117] The new value v calculated by the user in step S2 new With the historical calculated value v i To calculate the matching degree, use the cosine similarity formula:

[0118]

[0119] Where "·" represents the vector dot product, and "||" represents the vector modulus;

[0120] Select the chart Ti with the highest matching degree as the user's initial chart. The system will recommend this chart and preview its style.

[0121] The user makes a selection based on the chart Ti recommended by the system. After confirmation, the system draws the chart according to the algorithm formula Fk and field mapping {d1, d2, ..., dm} selected by the user;

[0122] The system generates an initial chart in the background based on the user's customized options and provides a preview;

[0123] The feedback data includes the number of clicks, hovering time, number of modifications, data input time, and usage frequency on the current Ti-type chart, and the number of clicks is marked as DJs, the hovering time is marked as XTt, the number of modifications is marked as XGc, the data input time is marked as SRt, and the usage frequency is marked as SYf;

[0124] Click count DJs means recording the number of clicks of users on the chart to evaluate the user's attention to the chart. The calculation formula is as follows:

[0125]

[0126] Among them, δ click,k1 is the indicator function of the k1th click event, which takes the value of 1 when a click occurs and 0 otherwise; N1 is the total number of click events during the observation period;

[0127] The hover time XTt is the time that the user's mouse hovers over the chart, reflecting the user's interest in the chart. The calculation formula is:

[0128]

[0129] Among them, T hover,k2 is the duration of the k2th hovering event, M1 is the total number of hovering events during the observation period;

[0130] The modification times XGc is the number of times the user modifies the chart, reflecting the accuracy and satisfaction of the initial chart. The calculation formula is:

[0131]

[0132] Among them, δ modify,k3 is the indicator function of the k3th modification event, which takes the value 1 when a modification occurs and 0 otherwise; Q is the total number of modification events during the observation period;

[0133] Data input time SRt means the time required for users to complete data field input to evaluate the convenience of operation; the calculation formula is:

[0134]

[0135] Among them, t input,j1 is the duration of the jth data input, and P is the total number of data input events during the observation period;

[0136] The usage frequency SYf is used to record the frequency of a certain type of chart being selected and used by users, reflecting its popularity;

[0137]

[0138] Among them, C use In the time period T period The number of times the inner chart is used;

[0139] The performance indicator data includes the response time, error rate, loading time, and functional coverage in the current Ti class chart, and the response time is marked as RT, the error rate is marked as ER, the loading time is marked as LT, and the functional coverage is marked as FC;

[0140] The response time RT calculation formula is as follows:

[0141] RT = t end -t start

[0142] Among them, t start is the time point when the user operation starts, t end It is the time point when the system completes the operation and presents the result; this indicator reflects the system's response speed to user operations;

[0143] The error rate ER calculation formula is as follows:

[0144]

[0145] Among them, N error is the number of errors that occurred during the operation, and N total is the total number of operations, which measures the system stability and accuracy;

[0146] The calculation formula of the loading time LT is as follows:

[0147] LT=t display -t load

[0148] Among them, t load is the time point when the chart is loaded, t display It is the point in time when the chart is fully rendered on the user interface; this metric measures the speed and efficiency of chart display;

[0149] The function coverage FC calculation formula is as follows:

[0150]

[0151] Among them, N covered is the number of functions actually used, N total It is the total number of functions provided by the system; this indicator evaluates the practicality and completeness of the system functions.

[0152] Embodiment 4:

[0153] Further explanation based on the third embodiment, the feedback data in step S4 is obtained and analyzed to generate a feedback evaluation index, which is used to evaluate the conformity of the selection of the initial chart, and specifically includes the following contents:

[0154] The feedback evaluation index is defined as FEI, and the calculation formula is as follows:

[0155]

[0156] Among them, a1, a2, a3, a4 and a5 are weight coefficients, which are positive constants and are adjusted according to the relative importance of each factor to FEI; e is the base of the natural logarithm;

[0157] λ1, λ2, and λ3 are attenuation coefficients, which control the contribution of the corresponding index to FEI to decrease with the growth rate;

[0158] DJs is the number of clicks, XTt is the hovering time, XGc is the number of modifications, SRt is the data entry time, SYf is the frequency of use, and μ is the median of SRt, which is used to determine the center point of the data entry time;

[0159] is an exponentially decaying integral function, which represents the weighted sum of the hover time, and reflects the long-term impact of the user's interest in the chart; the integral of the hover time: It reflects the user's continued interest in the chart, which decays exponentially over time;

[0160] Z is the normalization factor, which ensures that FEI is within the set range and sets the FEI value range to (0,1). The weight coefficients a1, a2, a3, a4 and a5, the attenuation coefficients λ1, λ2, λ3, and the median μ all need to be adjusted according to the actual data to achieve the best evaluation effect. Different FEI values ​​will reflect the user's comprehensive evaluation of different charts.

[0161] For a1, when the number of clicks DJs has a greater impact on FEI, such as frequent user interactions, indicating a high degree of attention to the chart, the value range of weight a1 is 0.30 to 0.35; in this scenario, if a1 is 0.35, it means that the change in the number of clicks will have a more significant impact on FEI, while the values ​​of other weights are relatively reduced to ensure that the total FEI remains unchanged; relatively speaking,

[0162] If the number of clicks has a smaller impact on FEI, that is, there is less user interaction, the value range of a1 is adjusted to 0.15 to 0.20; in this scenario, if a1 is 0.15, the weights of hover time, number of modifications, data input time, and frequency of use will be increased accordingly to balance the total impact of FEI;

[0163] For a2, when the hovering time XTt has a greater impact, users will look at the chart for a longer time when making decisions, which indicates that the users are thoughtful and carefully process the information. The value range of a2 is set from 0.25 to 0.30. In this case, if a2 is set to 0.30, a small change in the hovering time will have a greater impact on the FEI;

[0164] On the other hand, when the hover time has a smaller impact, reflecting that the user is highly familiar with the chart or makes quick decisions, the value range of a2 is 0.10 to 0.15. If a2 is 0.10, the weights of other parameters will be increased accordingly to ensure that FEI reflects the user's overall evaluation of the chart;

[0165] For a3, when the number of modifications XGc has a greater impact, the user needs to frequently adjust the chart to achieve the required accuracy, indicating that the initial setting of the chart does not fully meet the user's needs. At this time, the value range of a3 is 0.20 to 0.25; if a3 is 0.25, the impact of each modification on FEI will be enhanced;

[0166] On the contrary, if the number of modifications has little impact on FEI, it means that the user is relatively satisfied with the chart and only needs less adjustment. In this case, the value range of a3 is 0.05 to 0.10. When a3 is 0.05, other weights will be adjusted to maintain the overall balance of FEI.

[0167] For a4, the data input time SRt has a greater impact. When the data volume is large or the data complexity is high, the user needs more time to input the data. The weight range of a4 is 0.15 to 0.20. If a4 is 0.20, the contribution of SRt to FEI increases;

[0168] On the contrary, when the impact of data input time is small, indicating that the input process is more convenient, the weight range of a4 is 0.05 to 0.10. If a4 is 0.05, the weights of clicks, hovering time, modification times, and usage frequency increase accordingly;

[0169] For a5, when the frequency of use SYf has a greater impact on FEI, a certain type of chart is frequently used, indicating that it is popular and practical. At this time, the weight range of a5 is 0.20 to 0.25; if a5 is 0.25, FEI emphasizes the frequency of use more; on the contrary, when the impact of the frequency of use is small, because users are equally interested in a variety of charts or the application scenarios are more scattered, the weight range of a5 is adjusted to 0.05 to 0.10; if a5 is 0.05, the influence of other factors will occupy a larger proportion in FEI;

[0170] The attenuation coefficients λ1, λ2, and λ3 depend on the specific distribution of the data and the characteristics of user behavior. In a highly interactive environment, a smaller attenuation coefficient, such as 0.5 to 0.7, is more appropriate, reflecting that the impact of user behavior on FEI lasts longer.

[0171] In a fast decision-making environment, a larger decay coefficient, such as a range of 0.8 to 1.0, is more appropriate, indicating that the impact of user behavior on FEI decreases rapidly;

[0172] The median μ should be determined based on the actual distribution median of SRt, which requires statistical analysis of historical data to obtain an accurate value. In the absence of data, a commonly used operation time is preset as the median, such as 30 seconds. This value is then adjusted based on actual usage data to reflect the actual situation.

[0173] The closer FEI is to 1, the higher the conformity of the currently selected initial chart is, and the closer it is to 0, the lower the conformity of the selected initial chart is;

[0174] The FEI value range (0,1) is divided into three intervals: interval 1 (0,0.33), interval 2 [0.33,0.66), interval 3 [0.66,1); and the conformity evaluation is performed for each interval;

[0175] For interval 1 (0, 0.33), the chart's compliance is low, and user satisfaction is less than 33%, reflecting that users have low interest in and use frequency of the chart. The chart needs to be improved to enhance user experience and satisfaction; the details are as follows:

[0176] When the FEI value is in interval 1 (0, 0.33), it indicates that the initial chart has a low degree of compliance and the user's satisfaction with the chart is less than 33%. In this interval, the values ​​of click count DJs, hovering time XTt, modification count XGc, data input time SRt and usage frequency SYf are all low, reflecting that users are not very interested in the chart and use it frequently. At this time, DJs is lower than 20% of the average value, XTt is lower than 30% of the average hovering time, XGc is lower than 15% of the average modification count, SRt is higher than 25% of the average data input time, and SYf is lower than 20% of the average usage frequency. These values ​​indicate that the chart fails to effectively attract users and needs to be improved.

[0177] In interval 1, a threshold of 0.25 is set to judge the improvement needs of the chart. When the FEI value is lower than 0.25, the improvement needs of the chart are high and need to be focused on and optimized. When the FEI value is between 0.25 and 0.33, the improvement needs of the chart are medium and need to be adjusted by 20%. When the FEI value is 0.2, the focus should be on optimizing the interactivity and information display of the chart to improve user participation and satisfaction.

[0178] In interval 1, the interaction rules of each parameter are as follows: when DJs increases by 10%, FEI increases by 5%; when XTt increases by 15%, FEI increases by 7%; when XGc decreases by 10%, FEI increases by 6%; when SRt decreases by 20%, FEI increases by 8%; when SYf increases by 10%, FEI increases by 4%. These changes show that by optimizing the interactivity of the chart and reducing the user's operation time, the chart's compliance and user satisfaction are significantly improved;

[0179] Interval 2 [0.33, 0.66), the conformity of the chart is at a medium level, and the user satisfaction is between 33% and 66%. The chart meets the user needs to a certain extent, but there is still room for improvement;

[0180] The initial chart compliance in this interval is at a medium level, and the user satisfaction is between 33% and 66%. In this interval, the values ​​of various indicators show that the chart meets the user's needs to a degree of 35% to 45%, but there is still room for improvement; DJs is between 40% and 60% of the average, XTt is between 50% and 70% of the average hovering time, XGc is between 30% and 50% of the average number of modifications, SRt is between 80% and 100% of the average data input time, and SYf is between 40% and 60% of the average frequency of use;

[0181] In interval 2, the threshold is set to 0.5 to evaluate the optimization potential of the chart. When the FEI value is lower than 0.5, the optimization potential of the chart is medium, and further analysis of user feedback is needed to determine the direction of improvement. When the FEI value is between 0.5 and 0.66, the optimization potential of the chart is high, and the focus is on optimization to improve the user experience. When the FEI value is 0.45, it is necessary to increase the interactivity and information richness of the chart to improve user engagement.

[0182] In interval 2, the interaction rules of each parameter are as follows: when DJs increases by 15%, FEI increases by 8%; when XTt increases by 20%, FEI increases by 10%; when XGc decreases by 15%, FEI increases by 9%; when SRt decreases by 25%, FEI increases by 11%; when SYf increases by 15%, FEI increases by 6%; these changes show that by further optimizing the interactivity of the chart and reducing the user's operation time, the chart's compliance and user satisfaction can be effectively improved;

[0183] In interval 3 [0.66, 1), the chart has a high degree of compliance and user satisfaction exceeds 66%, indicating that the chart meets the needs of users to a large extent, and the user participation and satisfaction are high, which requires continuous maintenance and fine-tuning optimization;

[0184] When the FEI value is in interval 3, the initial chart has a high degree of compliance and the user satisfaction exceeds 66%. In this interval, the values ​​of various indicators show that the chart meets the needs of users to a large extent, and the user participation and satisfaction are high; at this time, DJs is higher than 70% of the average value, XTt is higher than 80% of the average hovering time, XGc is lower than 20% of the average number of modifications, SRt is lower than 70% of the average data input time, and SYf is higher than 70% of the average frequency of use;

[0185] In interval 3, the threshold is set to 0.8 to evaluate the maintenance needs of the chart. When the FEI value is lower than 0.8, the chart has a medium maintenance need and needs to be regularly checked and updated to maintain its performance. When the FEI value is between 0.8 and 1, the chart has a low maintenance need, but user feedback is still needed for continuous optimization. When the FEI value is 0.75, feedback needs to be collected through regular user surveys to ensure continuous optimization of the chart.

[0186] In interval 3, the interaction rules of each parameter are as follows: when DJs increases by 20%, FEI increases by 10%; when XTt increases by 25%, FEI increases by 12%; when XGc decreases by 20%, FEI increases by 11%; when SRt decreases by 30%, FEI increases by 13%; when SYf increases by 20%, FEI increases by 7%; These changes show that although the compliance of the chart is already high, the user satisfaction and chart performance can still be slightly improved by further optimizing the interactivity of the chart and reducing the user's operation time.

[0187] Embodiment five:

[0188] Further explanation based on the fourth embodiment, the performance indicator data in step S4 is obtained and analyzed to generate a performance evaluation index, which is used to evaluate the performance level of the required chart; specifically includes the following contents:

[0189] The performance evaluation index is defined as PEI, and the calculation formula is as follows:

[0190]

[0191] Among them, C2>0 is a calibration constant to ensure the special value processing in the performance index, which is used to ensure that when RT, ER, LT or FC is 0, the formula can avoid producing unreasonable evaluation results; this formula solves the special case of parameter 0 by introducing C2 in each performance index;

[0192] Z2 is the normalization constant, which ensures that the PEI value is within the set range. The calculation method is Z2 = (b1 + b2 + b3 + b4) × η; where η is the adjustment coefficient; 0 < η < 0.42;

[0193] b1, b2, b3, b4, b5, c1, c2, c3 are positive adjustment parameters, which respectively adjust the weights and influences of response time RT, error rate ER, loading time LT and function coverage FC in the performance evaluation index; the values ​​of b1, b2, b3, b4, b5, c1, c2, c3 are determined by an expert group based on experimental data, and the judgment logic is the same as that of FEI in Example 4, which will not be described in detail;

[0194] The range of PEI is set to (0,1), where PEI close to 1 indicates that the performance of the chart is excellent and all indicators perform well; PEI close to 0 indicates that the performance of the chart needs to be improved; when RT, ER, and LT increase, the scores of their corresponding items in the PEI formula will decrease, so the value of the entire PEI will decrease; when FC increases, the scores of its corresponding items in the PEI formula will increase, so the value of the entire PEI will increase;

[0195] The value range (0,1) of the performance evaluation index PEI is divided into three intervals, namely interval 1 (0, 0.33), interval 2 [0.33, 0.66), and interval 3 [0.66, 1);

[0196] Interval 1 (0, 0.33), this interval represents poor chart performance. The response time, error rate and loading time of key indicators are all higher than the industry standard, and the functional coverage is also far below expectations, which urgently needs comprehensive optimization;

[0197] In this range, the performance evaluation of the chart is low, indicating that the chart performs poorly in terms of response time RT, error rate ER, loading time LT, and functional coverage FC; specifically, there are cases where RT exceeds 150% of the average, ER is as high as 10%, LT exceeds 200% of the average, and FC is lower than 70% of the average; these values ​​indicate that the chart has significant problems in processing speed, stability, and functional practicality, and needs to be optimized immediately;

[0198] To further refine the performance evaluation, a threshold is set: when the PEI value is less than 0.2, the chart performance is judged as "severely insufficient"; under this threshold, if RT exceeds 200% of the average, ER exceeds 12%, LT exceeds 250% of the average, or FC is less than 65%, immediate measures need to be taken for optimization; these thresholds are set based on industry standards and performance expectations to ensure that the minimum requirements for chart performance are met;

[0199] In this range, the interactive changes of each parameter are significant. When RT increases by 10%, PEI decreases by 5%; ER increases by 5%, PEI decreases by 3%; LT increases by 10%, PEI decreases by 4%; and FC decreases by 5%, PEI decreases by 2%. These changes show that RT and LT have a greater impact on PEI, while FC has a relatively smaller impact. Therefore, optimizing RT and LT should be the top priority for improving chart performance.

[0200] In interval 2 [0.33, 0.66), the performance of the chart is at a medium level. Although it meets the basic requirements, there is still room for improvement in response speed, accuracy, and loading efficiency. The functional coverage also has the potential to be improved.

[0201] In this range, the performance of the chart is rated as medium, indicating that the chart has some room for improvement in performance indicators; specifically, RT is close to 120% of the average, ER is 8%, LT is close to 150% of the average, and FC is 80%; these values ​​indicate that the chart is lacking in performance, but there is still room for improvement;

[0202] Set a threshold: When the PEI value is between 0.45 and 0.55, the chart performance is judged as "needs improvement"; within this threshold range, if RT exceeds 130% of the average, ER exceeds 9%, LT exceeds 170% of the average, or FC is less than 75%, targeted optimization is required; these thresholds are set to identify those charts whose performance is not seriously insufficient, but there is still room for improvement;

[0203] In this range, the interactive changes of various parameters are relatively balanced; when RT increases by 5%, PEI decreases by 2%; when ER increases by 3%, PEI decreases by 1.5%; when LT increases by 5%, PEI decreases by 2%; and when FC decreases by 3%, PEI decreases by 1%. These changes show that the effects of various parameters on PEI are relatively balanced, and optimizing any one indicator will help improve the overall performance evaluation.

[0204] Interval 3 [0.66,1), the chart performance in this interval is good, and all performance indicators meet or exceed industry standards. Only minor adjustments are needed to achieve optimal performance;

[0205] In this range, the performance evaluation of the chart is high, indicating that the chart performs well in response time RT, error rate ER, loading time LT, and functional coverage FC; specifically, RT is lower than 110% of the average, ER is lower than 6%, LT is lower than 130% of the average, and FC is higher than 90% of the average; these values ​​indicate that the chart is close to or reaches the industry-leading level in performance;

[0206] Set a threshold: When the PEI value is above 0.8, the chart performance is judged as "excellent"; within this threshold range, if RT is less than 105% of the average, ER is less than 5%, LT is less than 120% of the average, or FC is above 95%, the chart performance is considered excellent; these thresholds are set to identify those charts with excellent performance and provide a reference standard for the industry;

[0207] In this range, the interactive changes of each parameter are small. When RT increases by 2%, PEI decreases by 1%; ER increases by 1%, PEI decreases by 0.5%; LT increases by 2%, PEI decreases by 1%; and FC decreases by 1%, PEI decreases by 0.5%. These changes indicate that the chart performance is close to the optimal level, and a small optimization can maintain or improve the performance evaluation.

[0208] Embodiment six:

[0209] Further explanation based on Example 5, the feedback evaluation index and the performance evaluation index are obtained, analyzed and processed, and a dynamic adjustment model of the chart is constructed. The model is used for real-time interactive adjustment based on the feedback data and the performance index data to generate a dynamic adjustment strategy for the initial chart, which specifically includes the following contents:

[0210] The dynamic adjustment model is defined as DAMC, and the calculation formula is as follows:

[0211]

[0212] Among them, e1 and e2 are exponential adjustment parameters used to enhance the nonlinear effects of FEI and PEI in DAMC;

[0213] κ is a performance trade-off parameter used to adjust the strength of the effect of PEI in DAMC relative to FEI;

[0214] ψ is a scaling parameter used to adjust the contribution of the logarithmic function to the model and balance the sensitivity of the overall formula;

[0215] e3 is the smoothing parameter, which is used to avoid the singularity of the logarithmic function when FEI or PEI is close to zero;

[0216] e1 is the FEI index adjustment parameter, which is used to adjust the influence of FEI in DAMC. If the change of FEI has a greater impact on the adjustment strategy of the chart, when the change of FEI exceeds 10%, the adjustment strategy of the chart needs to be significantly changed, and e1 needs to be set to a larger value, e1 takes a value of 1.5. On the other hand, if the change of FEI has a smaller impact on the adjustment strategy of the chart, the change of FEI within 10% does not require significant adjustment of the chart, and e1 is set to a smaller value, e1 takes a value of 0.5;

[0217] e2 is the PEI index adjustment parameter, which is used to adjust the influence of PEI in DAMC. Similarly, if the change of PEI has a greater impact on the adjustment strategy of the chart, when the change of PEI exceeds 20%, the adjustment strategy of the chart needs to be changed, and e2 needs to be set to a larger value, e2 takes a value of 2.0. On the other hand, if the change of PEI has a smaller impact on the adjustment strategy of the chart, the change of PEI within 20% does not require significant adjustment of the chart, and e2 is set to a smaller value, e2 takes a value of 0.8;

[0218] κ is a performance trade-off parameter used to balance the relative importance of FEI and PEI. When the impact of PEI is more important than FEI, the user is more concerned about the performance of the chart rather than the feedback evaluation. κ is set to a larger value, κ is 1.2. Conversely, if the impact of FEI is more important than PEI, κ is set to a smaller value, κ is 0.8.

[0219] ψ is a scaling parameter used to adjust the influence of the logarithmic term in the model. If the influence of the logarithm of the product of FEI and PEI on the model needs to be strengthened, the model is more sensitive to changes in smaller FEI and PEI, and ψ needs to be set to a larger value, ψ takes a value of 10; conversely, if it is believed that the influence of the logarithmic term should be weakened, ψ needs to be set to a smaller value, ψ takes a value of 1;

[0220] e3 is a smoothing parameter used to avoid the singularity of the logarithmic function when FEI or PEI is close to zero. e3 is a decimal close to but greater than zero. The value of e3 is 0.01 to avoid mathematical problems. However, if there are stricter requirements on the smoothness of the model, or the data allow FEI and PEI to take smaller positive values, e3 is set to a smaller positive number. The value of e3 is 0.001.

[0221] The value range of DAMC is set between (0,1). DAMC close to 1 indicates that according to the comprehensive analysis of FEI and PEI, the dynamic adjustment strategy of the chart is very effective, and the conformity and performance of the chart are optimized;

[0222] When DAMC is close to 0, it means that according to the analysis of FEI and PEI, the dynamic adjustment strategy of the chart is not effective, and the chart needs to be significantly adjusted to improve compliance and performance;

[0223] The DAMC value range (0,1) is divided into the following intervals, and the corresponding dynamic adjustment strategy is described for each interval;

[0224] Interval 1 (0, 0.4), the user experience and performance of the chart are poor and significant optimization is required; set FEI and PEI low, and by adjusting the DAMC model parameters, indicate the degree to which the chart needs improvement; if DAMC ≤ 0.3, a complete reconstruction of the chart is required; if it is between 0.3 and 0.4, targeted optimization is needed; specifically as follows:

[0225] In interval 1 (0, 0.4), the chart needs to implement significant optimization to improve the user experience and performance. Assume that the change ranges of FEI and PEI in this interval are FEI ∈ (0.1, 0.3), PEI ∈ (0.1, 0.3). Set the exponential adjustment parameters e1 = 1.5, e2 = 1.5, the performance trade-off parameter κ = 0.6, the scaling parameter ψ = 2, and the smoothing parameter e3 = 0.05; the DAMC values obtained from these parameters will indicate the degree to which the chart needs improvement; when FEI = 0.25 and PEI = 0.25, the calculated DAMC is 0.35, indicating insufficient identification compliance and performance;

[0226]

[0227] In this interval, set the judgment threshold to 0.3 to ensure the basic usability of the chart. If DAMC ≤ 0.3, a fundamental reconstruction of the chart is required; if 0.3 < DAMC < 0.4, it indicates that the chart needs to be optimized for specific deficiencies to improve the visual clarity of the data or simplify the user operation process;

[0228] In this interval 1, if FEI is increased to 0.3, DAMC is increased to 0.38 due to the influence of κ. If PEI is simultaneously increased to 0.3, DAMC is increased to 0.4 due to the scaling effect of ψ; this change indicates that in the low compliance and low performance interval, more sensitive improvement of DAMC can be achieved by adjusting κ and ψ, thereby significantly improving the overall performance of the chart;

[0229] Interval 2 [0.4, 0.7), the chart shows medium compliance and performance in this interval. The FEI and PEI values indicate that to achieve a higher DAMC score, the chart needs to be optimized to a certain extent. If DAMC < 0.6, the chart function or performance needs to be enhanced according to user feedback; if DAMC is between 0.6 and 0.7, minor adjustments are sufficient;

[0230] In the adjusted interval 2 [0.4, 0.7), the chart shows moderate compliance and performance. Assuming that the range of FEI and PEI is FEI∈(0.4, 0.6), PEI∈(0.4, 0.6), with the parameter settings of e1=1.2, e2=1.2, κ=0.7, ψ=1.5, e3=0.05, the DAMC score indicates that the performance of the chart and user satisfaction are at an acceptable level; when FEI=0.5 and PEI=0.5, the DAMC is calculated to be 0.55;

[0231]

[0232] The judgment threshold of interval 2 is set to 0.6. If the value exceeds this value, the chart is considered to need minor adjustments. When DAMC is between 0.4 and 0.6, the chart is adjusted according to user feedback to enhance functions or improve performance; when DAMC is between 0.6 and 0.7, the chart only needs to optimize some user interface details or improve data loading speed;

[0233] In this interval, when FEI and PEI each increase by 5%, DAMC increases by 3% and 3.5% respectively due to the gradual increase of κ; this nonlinearity reflects the impact of the relative change of each indicator on DAMC at moderate compliance and performance levels;

[0234] Interval 3 [0.7, 1) is the high interval of DAMC. The performance and user satisfaction of the chart are very high. Slight adjustments are made to FEI and PEI to slightly improve the DAMC value. In this interval, the chart only needs to be optimized in details to maintain or slightly improve the performance and user experience.

[0235] In the adjusted interval 3[0.7,1), the chart’s compliance and performance are very high. Considering the range of FEI and PEI as FEI∈(0.7,0.9), PEI∈(0.7,0.9), e1=0.8, e2=0.8, κ=0.8, ψ=1, e3=0.05, the DAMC value obtained will be close to the optimization; when FEI=0.85 and PEI=0.85, DAMC is close to 0.88, showing good user experience and high performance;

[0236]

[0237] The judgment threshold for interval 3 is set to 0.9. If DAMC ≥ 0.9, the chart does not need to be further optimized. When DAMC is between 0.7 and 0.9, performance reviews should be conducted regularly to maintain compliance and performance;

[0238] In this range, even if the improvement of FEI and PEI is 10%, due to the adjustment of kappa and psi, the increase in DAMC is less than 2%; this indicates that it is more difficult to achieve further growth at already high satisfaction and performance levels, and innovative ideas and detailed optimizations are required.

[0239] Example Seven:

[0240] Based on Example Six, to verify the effectiveness and innovation of the DAMC model, a series of experiments were designed. The purpose was to dynamically optimize charts at different performance and user experience levels by adjusting model parameters; eight groups of datasets were selected, representing different levels of user experience and performance metrics, to simulate charts in actual application scenarios;

[0241] The experimental steps are as follows:

[0242] Parameter initialization: Set the initial FEI and PEI values for each dataset, and according to the invention content, set the values of e1, e2, κ, ψ, and e3 respectively;

[0243] Interval allocation: Allocate the DAMC value of each dataset to the corresponding interval (1, 2, or 3) according to the calculation results of its FEI and PEI;

[0244] Adjustment strategy application: Set different adjustment strategies for each interval; in interval 1, if DAMC ≤ 0.3, execute a comprehensive reconstruction strategy; if 0.3 < DAMC < 0.4, execute targeted optimization;

[0245] In interval 2, if DAMC < 0.6, enhance the chart function or performance according to user feedback; if 0.6 ≤ DAMC < 0.7, make minor adjustments;

[0246] For interval 3, when DAMC is between 0.7 and 0.9, conduct regular performance reviews; if DAMC ≥ 0.9, maintain the current state;

[0247] Data recording and analysis: After executing the adjustment strategy, re-evaluate FEI and PEI, calculate the new DAMC value, and record the results after each adjustment;

[0248] Result comparison: Compare the DAMC values before and after adjustment to evaluate the optimization effect of the model;

[0249] During the experiment, minor changes in FEI and PEI were introduced, and the parameters were fine-tuned in 1% steps to observe their sensitivity to DAMC; in addition, subjective evaluations of data visualization effects were introduced as part of the adjustment strategy to ensure that the optimization strategy is not only reasonable numerically but also in line with the intuitive experience of users;

[0250] Through the above experiments, a set of detailed data records can be obtained. These data will show the performance of the DAMC model in different scenarios, thereby proving its advantages and innovations. The following table shows the FEI, PEI and DAMC values ​​of different data sets before and after the implementation of the DAMC model adjustment strategy, and shows the initial matching degree and the matching degree after adjustment in relative values; For specific experimental data, please refer to Figure 4 Table in;

[0251] The chart dynamic adjustment model DAMC constructed by the present invention uses feedback data and performance indicator data to make real-time interactive adjustments and generate a dynamic adjustment strategy for the initial chart, thereby optimizing the final performance of the chart;

[0252] Beneficial effect analysis:

[0253] Dynamic adjustment improves matching: By comparing the "initial matching" and "adjusted matching" in the table, we can see that the matching of each data set has improved after the model adjustment. This shows that the DAMC model can intelligently select a chart type that is more suitable for the user's historical preferences based on real-time feedback and performance data, thereby improving the user experience;

[0254] Analysis of the impact of parameter adjustment: The design of parameters e1, e2, κ, ψ, and e3 in the DAMC model allows the model to be flexibly adjusted in different situations to adapt to different performance requirements and user experience. For example, the initial conditions of dataset A and dataset B are relatively close, but by slightly adjusting the model parameters κ and ψ, the matching degree is significantly improved from 0.70 to 0.90 and from 0.65 to 0.88;

[0255] Quantification of interval-specific effects:

[0256] Interval 1 (0, 0.4): In this interval, the DAMC values ​​of datasets A and B are significantly improved after adjustment, reflecting that the matching degree is improved from 0.70 to 0.90 and from 0.65 to 0.88 respectively through the comprehensive reconstruction strategy, which means that the charts with poor user experience and performance have improved by about 28.5% and 35.3% after adjustment;

[0257] Interval 2 (0.4, 0.7): Dataset C to Dataset F are in this interval. By taking targeted optimization and function enhancement strategies, the matching degree can be improved by 17% to 21%. The charts showing medium matching and performance can still effectively improve user satisfaction after appropriate adjustments.

[0258] Interval 3 (0.7, 1): Datasets Geng and Xin show that the already high matching scores (0.88 and 0.90) remain unchanged after slight adjustments or keeping the current state, which means that further improvement is more difficult at high satisfaction and performance levels, but can be maintained through fine-tuning.

[0259] Dynamic integration of real-time feedback and performance data: By integrating feedback data and performance data in real time, the chart display strategy is dynamically adjusted, which is very different from traditional static chart design;

[0260] Flexible application of parameterized models: The parameterized design of the DAMC model enables flexible adjustment of model parameters e1, e2, κ, ψ, and e3 to achieve customized chart optimization strategies based on different user experience and performance requirements;

[0261] Matching optimization algorithm: Use the matching calculation formula to select the chart type that best suits the user's historical preferences. This intelligent recommendation based on historical data and real-time calculation results provides users with a more personalized and accurate data visualization experience.

[0262] In summary, the dynamic adjustment model of the present invention not only improves the matching degree of the chart and optimizes the user experience, but also demonstrates creativity and novelty in the field of data visualization; through intelligent adjustment of parameters and optimization of matching degree, the present invention can bring significant technical effects and improve user experience in practical applications;

[0263] Easy to operate: Through the intuitive interface, users can easily select algorithm formulas and input data without additional search and learning;

[0264] Efficient and accurate: The system automatically processes data and draws pictures, greatly improving work efficiency while reducing the possibility of human error;

[0265] Intuitive display: The generated statistical charts are intuitive and easy to understand, helping users quickly understand the data analysis results.

[0266] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0267] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0268] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0269] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be covered by the protection scope of the present application.

Claims

1. A method for automatic drawing based on an algorithmic formula, characterized in that: The specific steps include: Step S1, collecting various types of charts and marking them to form {T1, T2, ..., Ti, ..., Tn}, where Ti represents the i-th type of chart, and n represents the total number of chart categories; for each type of chart Ti, pre-storing its associated algorithm formula Fi into the system database to construct an algorithm formula list displayed on the user browsing interface; Step S2: The user manually selects the required algorithm formula from the algorithm formula list provided by the system, and then the system will display a data input prompt box related to the selected algorithm formula; the user directly drags the fields mentioned in the algorithm into this prompt box, and the system will automatically perform calculations in the background and generate corresponding calculated values; Step S3, obtaining the user's historical selection data for each chart and the corresponding historical calculated value, and performing matching calculation between the calculated value in step S2 and the historical calculated value, and selecting the i-th chart Ti with the highest matching degree as the initial chart; Step S4: collecting user feedback data on the initial chart and performance indicator data on the required chart; Step S5, obtaining the feedback data in step S4, and analyzing and processing it to generate a feedback evaluation index, which is used to evaluate the conformity of the selection of the initial chart; Step S6, obtaining the performance indicator data in step S4, and analyzing and processing it to generate a performance evaluation index, which is used to evaluate the performance level of the required chart; Step S7: Obtain the feedback evaluation index and the performance evaluation index, analyze and process them, and construct a dynamic adjustment model for the chart. The model is used to make real-time interactive adjustments based on the feedback data and the performance index data to generate a dynamic adjustment strategy for the initial chart, which specifically includes the following contents: The dynamic adjustment model is defined as DAMC, and the calculation formula is as follows: Among them, FEI refers to the feedback evaluation index, PEI refers to the performance evaluation index, and e1 and e2 are index adjustment parameters used to enhance the nonlinear effects of FEI and PEI in DAMC; κ is a performance trade-off parameter used to adjust the strength of the effect of PEI in DAMC relative to FEI; ψ is a scaling parameter used to adjust the contribution of the logarithmic function to the model and balance the sensitivity of the overall formula; e3 is the smoothing parameter, which is used to avoid the singularity of the logarithmic function when FEI or PEI is close to zero; The value range of DAMC is set between (0,1). DAMC close to 1 indicates that according to the comprehensive analysis of FEI and PEI, the dynamic adjustment strategy of the chart is very effective, and the conformity and performance of the chart are optimized; When DAMC is close to 0, it means that according to the analysis of FEI and PEI, the dynamic adjustment strategy of the chart is not effective, and the chart needs to be significantly adjusted to improve compliance and performance, and finally form an adjusted chart; Step S8: The user chooses to save or export the final optimized chart for reporting or further analysis.

2. The method for automatic drawing based on an algorithm formula according to claim 1, characterized in that: The user manually selects the required algorithm formula from the algorithm formula list provided by the system, and then the system will display a data input prompt box related to the selected algorithm formula; the user directly drags the fields mentioned in the algorithm into this prompt box, and the system will automatically perform calculations in the background and generate corresponding calculated values, which specifically includes the following implementation steps: a. The user manually selects the algorithm formula: a1. The user browses the algorithm formula list through the drop-down menu or search box on the interface and selects the required algorithm formula; a2. The user clicks on the selected algorithm formula and sends a selection instruction to the system; b. The system page displays a data prompt box related to the algorithm formula: b1. The system confirms the instruction selected by the user and retrieves the corresponding algorithm formula and its parameter requirements in the background; b2. The system retrieves the built-in algorithm formula information and identifies the required data fields; b3. The system generates a data input prompt box on the user interface, lists the required fields, and provides an input box or drop-down selection list for each field; c. Manually drag the fields mentioned in the algorithm into the prompt box: c1. The user searches for the corresponding data field in the data set browsing area next to the data field input prompt box; c2. The user clicks or drags to place the found data field into the corresponding input box or selection list; c3. If the data field needs to be calculated from multiple sub-fields, the system provides a simple calculator or formula editor for users to use; c4. After the user completes the input or selection of the data field, click the confirmation or "Next" button to submit the selected data field; d. The system generates calculated values ​​in the background: d1. The system verifies the integrity and validity of the data based on the input and selected data fields provided by the user; d2. After the data is verified to be correct, the system processes the data in the background according to the selected calculation formula and calculates the required calculation value; d3. The system feeds back the calculation results to the user by updating a result display area on the interface or popping up a dialog box containing the results; d4. The user has the option to save the calculation results, or export the results to common formats for further analysis or reporting.

3. The method for automatic drawing based on an algorithm formula according to claim 2, characterized in that: The algorithm formula Fi is defined as Fi(x1, x2, ..., xm), where xm represents the mth data field required by the algorithm, and the algorithm formula list is set to L = {F1, F2, ..., Fk}; The feedback data includes the number of clicks, hovering time, number of modifications, data input time, and usage frequency on the current Ti-type chart, and the number of clicks is marked as DJs, the hovering time is marked as XTt, the number of modifications is marked as XGc, the data input time is marked as SRt, and the usage frequency is marked as SYf; The performance indicator data include the response time, error rate, loading time, and functional coverage in the current Ti class chart, and the response time is marked as RT, the error rate is marked as ER, the loading time is marked as LT, and the functional coverage is marked as FC.

4. The method for automatic drawing based on an algorithm formula according to claim 3, characterized in that: The feedback data in step S4 is obtained and analyzed to generate a feedback evaluation index, which is used to evaluate the conformity of the selection of the initial chart, and specifically includes the following contents: The feedback evaluation index is defined as FEI, and the calculation formula is as follows: Among them, a1, a2, a3, a4 and a5 are weight coefficients, which are positive constants, and a1+a2+a3+a4+a5=1; FEI is adjusted according to the relative importance of each factor; e is the base of the natural logarithm; λ1, λ2, and λ3 are attenuation coefficients, which control the contribution of the corresponding index to FEI to decrease with the growth rate; DJs is the number of clicks, XTt is the hovering time, XGc is the number of modifications, SRt is the data entry time, SYf is the frequency of use, and μ is the median of SRt, which is used to determine the center point of the data entry time; is an exponentially decaying integral function, representing the weighted sum of hovering time, the integral of hovering time: It reflects the user's continued interest in the chart, which decays exponentially over time; Z is the normalization factor to ensure that FEI is within the set range.

5. The method for automatic drawing based on an algorithm formula according to claim 4, characterized in that: Set the FEI value range to (0,1). The closer the FEI is to 1, the higher the conformity of the currently selected initial chart is. The closer the FEI is to 0, the lower the conformity of the selected initial chart is. The value range of FEI (0,1) is divided into three intervals, namely interval 1 (0,0.33), interval 2 [0.33,0.66), and interval 3 [0.66,1); and the conformity evaluation is performed for each interval; For interval 1 (0, 0.33), the conformity of the chart is low, and the user satisfaction is less than 33%, which reflects that users have low interest in and use frequency of the chart, and the chart needs to be improved to improve user experience and satisfaction; Interval 2 [0.33, 0.66), the conformity of the chart is at a medium level, and the user satisfaction is between 33% and 66%. The chart meets the user needs to a certain extent, but there is still room for improvement; In interval 3 [0.66, 1), the chart has a high degree of compliance and a user satisfaction rate of over 66%, indicating that the chart meets the needs of users to a large extent, and the user participation and satisfaction are high, which requires continuous maintenance and fine-tuning optimization.

6. The method for automatic drawing based on an algorithm formula according to claim 5, characterized in that: The performance indicator data in step S4 is obtained and analyzed to generate a performance evaluation index, which is used to evaluate the performance level of the required chart; specifically, the following contents are included: The performance evaluation index is defined as PEI, and the calculation formula is as follows: Among them, C2>0 is a calibration constant, Z2 is a normalization constant, which ensures that the value of PEI is within the set range, and the calculation method is Z2=(b1+b2+b3+b4)×η; where η is the adjustment coefficient; 0<η<0.42; b1, b2, b3, b4, b5, c1, c2, c3 are positive adjustment parameters, which adjust the weights and influences of the corresponding response time RT, error rate ER, loading time LT and functional coverage FC in the performance evaluation index respectively, and b1+b2+b3+b4+b5=1; The value range of PEI is set to (0,1), where PEI close to 1 indicates that the performance of the chart is excellent and all indicators perform well; PEI close to 0 indicates that the performance of the chart needs to be improved; when RT, ER, and LT increase, the scores of the corresponding items in the PEI formula will decrease, so the value of the entire PEI will decrease; when FC increases, the scores of the corresponding items in the PEI formula will increase, so the value of the entire PEI will increase.

7. The method for automatic drawing based on an algorithm formula according to claim 6, characterized in that: The value range (0,1) of the performance evaluation index PEI is divided into three intervals, namely interval 1 (0, 0.33), interval 2 [0.33, 0.66), and interval 3 [0.66, 1); Interval 1 (0, 0.33), this interval represents poor chart performance. The response time, error rate and loading time of key indicators are all higher than the industry standard, and the functional coverage is also far below expectations, which urgently needs comprehensive optimization; In interval 2 [0.33, 0.66), the performance of the chart is at a medium level. Although it meets the basic requirements, there is still room for improvement in response speed, accuracy, and loading efficiency. The functional coverage also has the potential to be improved. Interval 3 [0.66,1), the chart performance in this interval is good, and all performance indicators have reached or exceeded the industry standards. Only minor adjustments are needed to achieve optimal performance.

8. The method for automatic drawing based on an algorithm formula according to claim 1, characterized in that: The DAMC value range (0,1) is divided into the following intervals, and a corresponding dynamic adjustment strategy is described for each interval; In interval 1 (0, 0.4), the user experience and performance of the chart are poor, and more than 50% of the relevant content needs to be optimized; FEI and PEI are set low, and the degree of improvement of the chart is indicated by adjusting the DAMC model parameters; if DAMC ≤ 0.3, the chart needs to be fully reconstructed; if it is between 0.3 and 0.4, targeted optimization is required; Interval 2 [0.4, 0.7), the chart shows moderate compliance and performance in this interval. The FEI and PEI values ​​indicate that to achieve a higher DAMC score, the chart needs to be optimized by 30% of its content. If DAMC < 0.6, the chart function or performance needs to be enhanced based on user feedback; if DAMC is between 0.6 and 0.7, slight adjustments are sufficient; Interval 3 [0.7, 1) is the high interval of DAMC. The performance and user satisfaction of the chart are very high. Slight adjustments are made to FEI and PEI to slightly improve the DAMC value. In this interval, the chart only needs to be optimized in details to maintain or slightly improve the performance and user experience.

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