An ergonomic chair forming parameter dynamic correction method based on real-time quality feedback and an ergonomic chair

Abstract

This invention discloses a method for dynamically correcting molding parameters of an ergonomic chair based on real-time quality feedback, and an ergonomic chair itself, relating to the field of production parameter control technology. The method includes: labeling the upstream and downstream relationships between equipment, as well as the autonomous adjustment parameters and adjustable ranges of the equipment; collecting historical autonomous adjustment parameters; setting a dominant parameter, determining the corresponding passive adjustment parameters for each dominant parameter, and classifying the passive adjustment parameters into levels; adjusting the passive adjustment parameters; and after adjustment, recording the data from this adjustment, and correcting the adjustment range when adjusting the same dominant parameter again. This invention, through parameter adjustment, correlates the parameters of multiple devices, ensuring that when the dominant parameter changes, other autonomous adjustment parameters change accordingly, guaranteeing overall production quality, eliminating the need for individual equipment debugging, and improving both production quality and efficiency.

Description

Technical Field

[0001] This invention relates to the field of production parameter control technology, specifically to a method for dynamic correction of molding parameters of an ergonomic chair based on real-time quality feedback, and an ergonomic chair. Background Technology

[0002] Ergonomic chair production process: First, the steel frame of the chair is cut and precisely welded according to the design drawings to ensure structural stability. Simultaneously, soft materials such as foam are cut to specifications for use in the seat cushions and backrest filling. Next, leather or mesh fabric is wrapped around the filled components and meticulously sewn. Then, the various components are assembled with the electric adjustment mechanism to complete the main body of the chair. Afterwards, armrests, casters, and other accessories are installed, and the chair undergoes rigorous quality inspection, such as load-bearing tests and adjustment function tests. Once qualified, it is packaged and shipped from the factory.

[0003] Strict parameter control is required in the production process of lounge chairs and ergonomic chairs. For example, the intelligent production method and control system for lounge chairs described in patent publication number CN116610080A involves cutting, bending, and drilling raw materials to obtain processed lounge chair raw materials; assembling the processed lounge chair raw materials and fixing them to obtain an assembled lounge chair; performing surface treatment on the assembled lounge chair to obtain a surface-reinforced lounge chair; and then performing defect detection on the surface-reinforced lounge chair, adjusting and repairing the defects to obtain a finished lounge chair. This allows for accurate detection of molding defects in lounge chairs, enabling appropriate processing of defective lounge chairs to optimize production efficiency and product quality.

[0004] The production quality of ergonomic chairs has a significant impact on the user experience. In existing ergonomic chair production methods or the aforementioned methods for manufacturing leisure chairs, the production equipment uses simple self-feedback adjustment. For example, when producing five-star chairs, temperature sensors are used to detect temperature and control the flow rate of coolant. However, the parameters between different processes do not have an automatic feedback adjustment effect, making it difficult to ensure the product adaptability of the entire production process. When production parameters change, each piece of equipment needs to be adjusted individually, resulting in low production efficiency. Summary of the Invention

[0005] The purpose of this invention is to provide a method for dynamic correction of ergonomic chair molding parameters based on real-time quality feedback, and an ergonomic chair, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for dynamic correction of ergonomic chair molding parameters based on real-time quality feedback, comprising: Establish a network of equipment relationships, mark the upstream and downstream relationships between equipment, and mark the autonomous adjustment parameters and the adjustable range of each autonomous adjustment parameter for each equipment; Collect historical autonomous adjustment parameters of each device and divide the samples according to time. Based on the historical autonomous adjustment parameters and upstream and downstream relationships of each device, any autonomous adjustment parameter item is set as the dominant parameter item. The corresponding passive adjustment parameter item is determined by the correlation selection method. Then, the passive adjustment parameter items are classified by level sorting method. Obtain the dominant parameter and its adjustment range, and adjust the driven adjustment parameter according to the parameter adjustment method in descending order of level; After adjustment, record the dominant and subordinate adjustment parameters. When adjusting the same dominant parameter again, correct the adjustment range of the subordinate adjustment parameter using a secondary correction method.

[0007] Preferably, the associative selection method includes: Calculate the values ​​of the dominant parameter and other parameter items for each sample; Calculate the dependency constants between the dominant parameter term and each other parameter term, specifically: ; ; ; in This represents a dependent constant of the dominant parameter term and another selected parameter term. This represents the entropy of the change in the dominant parameter X. Represents the entropy of the change of another selected parameter Y, and and The calculation method is the same. Represents joint entropy, The parameter value of the dominant parameter item is... The probability, The parameter value of the dominant parameter item is... The value of the other selected parameter item is The probability of.

[0008] Parameters whose subordinate constants to the dominant parameter are greater than 0.26 are defined as subordinate adjustment parameters.

[0009] Preferably, the ranking method includes: Determine the minimum number of steps in the equipment relationship network between the equipment to which the dominant parameter item belongs and the equipment to which the driven adjustment parameter item belongs. The step type includes upstream, downstream, and parallel. Determine whether the minimum step count indicates a single upstream / downstream relationship or a parallel relationship, and then calculate the rank factor of the driven adjustment parameter item according to the formula, specifically: ; in This represents the rank factor of the driven adjustment parameter term. This represents the minimum number of steps. This indicates the number of steps in the minimum number of steps that are of the same type. The subordinate constant and the rank factor are weighted and summed to obtain the rank ordinal number, where the weight of the subordinate constant is 0.77 and the weight of the rank factor is 0.23. The driven adjustment parameters are classified into levels according to their level ordinal numbers.

[0010] Preferably, the parameter adjustment method includes: The adjustment range is calculated based on the adjustable range of the self-adjusting parameter and the amplitude determination method; The self-regulating parameters currently in use and the self-regulating parameters in each sample are labeled according to the median of their adjustable range. Self-regulating parameters above the median are labeled as high-order parameters, and self-regulating parameters below the median are labeled as low-order parameters. Select the two samples whose labels differ least from the self-regulating parameter terms currently in use; If the dominant parameter and the autonomously regulating parameter change in the same direction in both samples, then the direction of adjustment of the autonomously regulating parameter in this adjustment will be the same as the direction of change of the dominant parameter. If the dominant parameter and the autonomously regulating parameter change in the same direction in both samples, then the direction of adjustment of the autonomously regulating parameter in this adjustment will be opposite to the direction of change of the dominant parameter.

[0011] Preferably, the amplitude determination method includes: Obtain the change amount and adjustable range of the dominant parameter item, calculate the proportion of the change amount in the adjustable range, and obtain the active change ratio; The active change ratio is converted into the adjustable range of the passive adjustment parameter to obtain the adjustment amplitude of the passive adjustment parameter.

[0012] Preferably, the secondary correction method includes: Select the first three adjustment records with the same dominant parameter, and calculate the average adjustment value of the three autonomous adjustment parameters in the three adjustments; And calculate the ratio between the adjusted average value of the dominant parameter term and the adjusted average value of the driven parameter term; Then calculate the ratio of the adjustment range of the dominant parameter to the adjustment range of the driven parameter in this adjustment; If the change in the calculated ratio relative to the average value exceeds 20%, the adjustment range of the passive adjustment parameter will be corrected so that the change between the two ratios does not exceed 20%. If the change in the calculated ratio relative to the average value does not exceed 20%, the adjustment range of the passive adjustment parameter will be maintained.

[0013] Preferably, the ranking method includes: Determine the minimum number of steps in the equipment relationship network from the equipment to the equipment to the equipment to the equipment of the driven regulation parameter item. The step type includes upstream, downstream, and parallel. Delete any slave adjustment parameter items of the upstream step type from the minimum number of steps from the device to the device of the master parameter item; Determine whether the minimum number of steps indicates a downstream relationship or a parallel relationship, and then calculate the rank factor of the driven adjustment parameter item according to the formula, specifically: ; in This represents the rank factor of the driven adjustment parameter term. This represents the minimum number of steps. This indicates the number of steps in the minimum number of steps that are of the same type. The subordinate constant and the rank factor are weighted and summed to obtain the rank ordinal number, where the weight of the subordinate constant is 0.77 and the weight of the rank factor is 0.23. The driven adjustment parameters are classified into levels according to their level ordinal numbers.

[0014] Compared with the prior art, the beneficial effects of the present invention are: By using parameter adjustment methods, parameters of multiple devices can be adjusted in a correlated manner. When the dominant parameter changes, other autonomously adjustable parameters can follow suit, ensuring overall production quality and preventing situations where upstream products are difficult to use downstream. Compared to the self-feedback adjustment of a single device, it eliminates the need for individual device debugging, thereby improving production quality and efficiency.

[0015] Meanwhile, by using the correlational selection method and the registration and sorting method, parameters that are related to the dominant parameter are selected and sorted, which can reduce the number of parameters to be adjusted and determine the order of adjustment, thereby improving the efficiency and accuracy of autonomous adjustment.

[0016] Furthermore, by using secondary correction methods, historical adjustment data can be applied to the current adjustment. This not only preserves the results of the current adjustment calculation but also optimizes the current adjustment range using historical data, making autonomous adjustment more stable and efficient and reducing the occurrence of production accidents. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the parameter dynamic correction method of the present invention; Figure 2 This is a flowchart illustrating the ranking method in this invention; Figure 3 This is a flowchart illustrating the parameter adjustment method in this invention; Figure 4 This is a flowchart illustrating the secondary correction method in this invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1: When a single device is adjusted by self-feedback or manually, other related devices can be adjusted accordingly to form an overall autonomous control system. This eliminates the need for individual device debugging and improves production quality and efficiency.

[0020] like Figures 1-4 As shown, the present invention provides a technical solution: a method for dynamic correction of ergonomic chair molding parameters based on real-time quality feedback, comprising: Establish a network of equipment relationships, mark the upstream and downstream relationships between equipment, and mark the autonomous adjustment parameters and the adjustable range of each autonomous adjustment parameter for each equipment; Collect historical autonomous adjustment parameters of each device and divide the samples according to time. Based on the historical autonomous adjustment parameters and upstream and downstream relationships of each device, any autonomous adjustment parameter item is set as the dominant parameter item. The corresponding passive adjustment parameter item is determined by the correlation selection method. Then, the passive adjustment parameter items are classified by level sorting method. Obtain the dominant parameter and its adjustment range, and adjust the driven adjustment parameter according to the parameter adjustment method in descending order of level; After adjustment, record the dominant and subordinate adjustment parameters. When adjusting the same dominant parameter again, correct the adjustment range of the subordinate adjustment parameter using a secondary correction method.

[0021] It is important to note that the self-adjusting parameters are parameters that the equipment is allowed to self-correct. These are set by technicians and represent parameters that can be changed in the automatic control and adjustment system. The upstream and downstream relationships of the equipment represent the production process of the product. In actual production, the upstream and downstream relationships of the equipment are not linear but have a network-like relationship of branching and merging. The closer the processes of the equipment, the closer the parameter items are, such as the cutting and grinding of raw materials. In addition, the dominant parameter item represents the parameter item that changes first. This parameter item can be changed manually by technicians or through the self-feedback adjustment of the equipment itself. For example, manually changing the diameter of the mounting hole of the five-star base or the temperature detection system automatically changing the flow rate of the coolant when it detects that the temperature is too high during cutting.

[0022] Associative selection methods include: Calculate the values ​​of the dominant parameter and other parameter items for each sample; Calculate the dependency constants between the dominant parameter term and each other parameter term, specifically: ; ; ; in This represents a dependent constant of the dominant parameter term and another selected parameter term. This represents the entropy of the change in the dominant parameter X. Represents the entropy of the change of another selected parameter Y, and and The calculation method is the same. Represents joint entropy, The parameter value of the dominant parameter item is... The probability, The parameter value of the dominant parameter item is... The value of the other selected parameter item is The probability of.

[0023] Parameters whose subordinate constants to the dominant parameter are greater than 0.26 are defined as subordinate adjustment parameters.

[0024] It should be noted that, for ease of understanding, the following simulated data is used: Suppose that temperature is selected as the dominant parameter in the historical autonomous adjustment parameters, and other parameters include speed, pressure, etc.

[0025] Taking rotational speed as an example, calculate the dependent constants of the selected parameter (rotational speed) and the dominant parameter (temperature): Assume that in the historical autonomous control parameter items, the temperature value has two possible values: 25 (unit: °C, omitted below) and 30, and the probability of each value occurring is 0.5. Assume that in the historical autonomous control parameter items, the speed value has two possible values: 1200 (unit: RPM, omitted below) and 1300, and the probability of each value occurring is 0.5 (in actual production, there are multiple possible values ​​for temperature and speed; simplified data is used here to reduce the amount of calculation). In addition, the probability of a temperature of 25 and a speed of 1200 is 0.4, the probability of a temperature of 25 and a speed of 1300 is 0.1, the probability of a temperature of 35 and a speed of 1200 is 0.1, and the probability of a temperature of 35 and a speed of 1300 is 0.4 (these probabilities are the probability of the corresponding parameter appearing in all historical control parameters, calculated by dividing the number of samples in which the corresponding parameter appears by the total number of samples).

[0026] Entropy of change of the dominant parameter (temperature) =-0.5log20.5-0.5log20.5=1, similarly, the entropy of the selected parameter (rotation speed) change. =1; Joint Entropy =-0.4log20.4-0.1log20.1-0.1log20.1-0.4log20.4≈1.722; This allows you to select the dependent constants for the parameter (speed) and the dominant parameter (temperature). =0.278.

[0027] The dependent constant represents the probability that the selected parameter (speed) will change when the dominant parameter (temperature) changes. It is different from the concept of strict probability and does not represent a probability of 0.278. The closer the dependent constant is to 1, the greater the probability that the selected parameter needs to be adjusted, and the closer it is to 0, the smaller the probability that it needs to be adjusted.

[0028] Following the same method, after calculating the dependent constants of other parameter terms and the dominant parameter term, parameter terms with dependent constants higher than 0.26 are identified as the driven adjustment parameter terms of the dominant parameter term. Furthermore, a calculation needs to be performed for each parameter term as the dominant parameter term.

[0029] like Figure 2 As shown, the ranking methods include: Determine the minimum number of steps in the equipment relationship network between the equipment to which the dominant parameter item belongs and the equipment to which the driven adjustment parameter item belongs. The step type includes upstream, downstream, and parallel. Determine whether the minimum step count indicates a single upstream / downstream relationship or a parallel relationship, and then calculate the rank factor of the driven adjustment parameter item according to the formula, specifically: ; in This represents the rank factor of the driven adjustment parameter term. This represents the minimum number of steps. This indicates the number of steps in the minimum number of steps that are of the same type. The subordinate constant and the rank factor are weighted and summed to obtain the rank ordinal number, where the weight of the subordinate constant is 0.77 and the weight of the rank factor is 0.23. The driven adjustment parameters are classified into levels according to their level ordinal numbers.

[0030] It should be noted that, for ease of understanding, the following simulated data is used: Assume that the dominant parameter belongs to device D, device A is the direct upstream of device D (minimum step count is 1), device B is parallel to device D (minimum step count is 1, and the step count type is parallel with 1 step count), and device C is the direct downstream of device B (minimum step count with device D is 2, and the step count type is parallel with 1 step count).

[0031] For device A: =1 / (1+0.5×1)≈0.667; For device B: =1 / (1+0.5×1)×0.6×1 / (1+0.5×1)≈0.267; For device C: =1 / (1+0.5×2)×0.6×1 / (1+0.5×1)=0.2; Assuming that the driven adjustment parameter a belongs to the parameters of equipment A, and the subordinate constant of driven adjustment parameter a is 0.53, then the ordinal number of driven adjustment parameter a is 0.667×0.23+0.53×0.77≈0.562. Following the same method, after calculating the ordinal numbers of all driven adjustment parameters, they are sorted according to the size of the ordinal number. Assuming that there is another driven adjustment parameter b with an ordinal number of 0.6, and there are only these two driven adjustment parameters, then the level of driven adjustment parameter a is level one, and the level of driven adjustment parameter b is level two.

[0032] like Figure 3 As shown, the parameter adjustment methods include: The adjustment range is calculated based on the adjustable range of the self-adjusting parameter and the amplitude determination method; The self-regulating parameters currently in use and the self-regulating parameters in each sample are labeled according to the median of their adjustable range. Self-regulating parameters above the median are labeled as high-order parameters, and self-regulating parameters below the median are labeled as low-order parameters. Select the two samples whose labels differ least from the self-regulating parameter terms currently in use; If the dominant parameter and the autonomously regulating parameter change in the same direction in both samples, then the direction of adjustment of the autonomously regulating parameter in this adjustment will be the same as the direction of change of the dominant parameter. If the dominant parameter and the autonomously regulating parameter change in the same direction in both samples, then the direction of adjustment of the autonomously regulating parameter in this adjustment will be opposite to the direction of change of the dominant parameter.

[0033] It should be noted that, for ease of understanding, the following simulated data is used: Assume the autonomously adjustable parameters include temperature (adjustable range 20-30, median 25), speed (adjustable range 1200-1400, median 1300), and pressure (unit: kPa, omitted below, adjustable range 100-200, median 150).

[0034] There are three samples as follows: Sample 1: Temperature 23 (low), Rotation speed 1368 (high), Pressure 133 (low); Sample 2: Temperature 24 (low), Rotation speed 1305 (high), Pressure 134 (low); Sample 3: Temperature 26 (high), Rotation speed 1302 (high), Pressure 141 (low).

[0035] The currently used self-regulating parameters are: temperature 22 (low), speed 1343 (high), and pressure 138 (low).

[0036] Compared to the current label, the labels of samples 1 and 2 are exactly the same as the current label, but the label of sample 3 has a difference. Therefore, the two samples selected are samples 1 and 2 (assuming there are multiple samples with the same label, the two samples with the closest dominant parameter terms are selected).

[0037] If the temperature rises from 22 to 26, then temperature is the dominant parameter.

[0038] First, adjust the rotational speed (assuming temperature is the dominant parameter, and the rotational speed is higher than the pressure in the passive adjustment parameters): In Sample 1 and Sample 2, when the temperature increases (from Sample 1 to Sample 2), the rotation speed decreases, and the direction of change is opposite. Therefore, in this adjustment, the rotation speed needs to be reduced (the adjustment range is calculated by the range determination method).

[0039] Then adjust the pressure: In both Sample 1 and Sample 2, the pressure increases as the temperature rises (from Sample 1 to Sample 2), and the direction of change is the same. Therefore, the pressure needs to be increased in this adjustment (the adjustment range is calculated by the range determination method).

[0040] like Figure 3 As shown, the amplitude determination methods include: Obtain the change amount and adjustable range of the dominant parameter item, calculate the proportion of the change amount in the adjustable range, and obtain the active change ratio; The active change ratio is converted into the adjustable range of the passive adjustment parameter to obtain the adjustment amplitude of the passive adjustment parameter.

[0041] It should be noted that, for ease of understanding, the following simulated data is used: Assume the adjustable range of the dominant parameter (temperature) is 20-30, and the current temperature increases from 22 to 26; the adjustable range of the rotation speed is 1200-1400, and the current rotation speed is 1343.

[0042] The temperature change is 26-22=4, the adjustable range is 30-20=10, and the proportion is 4 / 10=0.4.

[0043] The adjustable speed range is 1400 - 1200 = 200, so the speed adjustment increment is 0.4 × 200 = 80. If the speed is adjusted upwards, the speed will be adjusted to 1400 (if it exceeds the adjustable range, it will be set according to the boundary of the adjustable range). If the speed is adjusted downwards, the speed will be adjusted to 1263. In actual use, specific adjustments will be made in conjunction with parameter adjustment methods.

[0044] like Figure 4 As shown, the secondary correction method includes: Select the first three adjustment records with the same dominant parameter, and calculate the average adjustment value of the three autonomous adjustment parameters in the three adjustments; And calculate the ratio between the adjusted average value of the dominant parameter term and the adjusted average value of the driven parameter term; Then calculate the ratio of the adjustment range of the dominant parameter to the adjustment range of the driven parameter in this adjustment; If the change in the calculated ratio relative to the average value exceeds 20%, the adjustment range of the passive adjustment parameter will be corrected so that the change between the two ratios does not exceed 20%. If the change in the calculated ratio relative to the average value does not exceed 20%, the adjustment range of the passive adjustment parameter will be maintained.

[0045] It should be noted that, for ease of understanding, the following simulated data is used: Assume that the dominant parameter in this adjustment is temperature, and in the previous three adjustment records with temperature as the dominant parameter, the average temperature change is 3. The average speed of one of the driven adjustment parameters is 75, and the ratio is 3 / 75.

[0046] In this adjustment, the temperature increases by 4, and the calculated adjustment range of the speed is 80, with a ratio of 4 / 80. The change range of the ratio is (4 / 80-3 / 75)÷(3 / 75)=25%>20%, so the adjustment range needs to be corrected to 83 to ensure (4 / 83-3 / 75)÷(3 / 75)≈20%.

[0047] Example 2: In Example 1, the ranking method uses the minimum number of steps for calculation. However, in actual production, downstream equipment rarely affects upstream equipment, and downstream equipment can change according to the parameters of upstream equipment without needing to adjust the parameters of upstream equipment. The ranking method in Example 1 needs to fully consider upstream equipment, resulting in a large amount of computation. Therefore, this example provides another ranking method to reduce the amount of computation.

[0048] like Figure 2 As shown, the ranking methods include: Determine the minimum number of steps in the equipment relationship network from the equipment to the equipment to the equipment to the equipment of the driven regulation parameter item. The step type includes upstream, downstream, and parallel. Delete any slave adjustment parameter items of the upstream step type from the minimum number of steps from the device to the device of the master parameter item; Determine whether the minimum number of steps indicates a downstream relationship or a parallel relationship, and then calculate the rank factor of the driven adjustment parameter item according to the formula, specifically: ; in This represents the rank factor of the driven adjustment parameter term. This represents the minimum number of steps. This indicates the number of steps in the minimum number of steps that are of the same type. The subordinate constant and the rank factor are weighted and summed to obtain the rank ordinal number, where the weight of the subordinate constant is 0.77 and the weight of the rank factor is 0.23. The driven adjustment parameters are classified into levels according to their level ordinal numbers.

[0049] It should be noted that, for ease of understanding, the following simulated data is used: Continue using devices A, B, C, and D from Example 1 for calculation: For device A: When device A is present, it becomes the upstream device of device D. Therefore, the driven adjustment parameter item belonging to device A is directly deleted. For device B: =1 / (1+0.5×1)×0.6×1 / (1+0.5×1)≈0.267 (calculation method remains unchanged); For device C: =1 / (1+0.5×2)×0.6×1 / (1+0.5×1)=0.2 (calculation method remains unchanged); Assuming that the driven adjustment parameter a belongs to the parameters in device A, the driven adjustment parameter a can be directly deleted to reduce the amount of calculation. Other calculation steps are the same as in Example 1 and will not be repeated.

[0050] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended embodiments and their equivalents.

Claims

1. An ergonomic chair forming parameter dynamic correction method based on real-time quality feedback, characterized in that: include: Establish a network of equipment relationships, mark the upstream and downstream relationships between equipment, and mark the autonomous adjustment parameters and the adjustable range of each autonomous adjustment parameter for each equipment; Collect historical autonomous adjustment parameters of each device and divide the samples according to time. Based on the historical autonomous adjustment parameters and upstream and downstream relationships of each device, any autonomous adjustment parameter item is set as the dominant parameter item. The corresponding passive adjustment parameter item is determined by the correlation selection method. Then, the passive adjustment parameter items are classified by level sorting method. Obtain the dominant parameter and its adjustment range, and adjust the driven adjustment parameter according to the parameter adjustment method in descending order of level; After adjustment, record the dominant and subordinate adjustment parameters. When adjusting the same dominant parameter again, correct the adjustment range of the subordinate adjustment parameter using a secondary correction method.

2. The method of claim 1, wherein the method is a method of dynamically correcting the forming parameters of an ergonomic chair based on real-time quality feedback. The associative selection method includes: Calculate the values ​​of the dominant parameter and other parameter items for each sample; Calculate the dependency constants between the dominant parameter term and each other parameter term, specifically: ； ； ； in This represents a dependent constant of the dominant parameter term and another selected parameter term. This represents the entropy of the change in the dominant parameter X. Represents the entropy of the change of another selected parameter Y, and and The calculation method is the same. Represents joint entropy, The parameter value of the dominant parameter item is... The probability, The parameter value of the dominant parameter item is... The value of the other selected parameter item is The probability of; Parameters whose subordinate constants to the dominant parameter are greater than 0.26 are defined as subordinate adjustment parameters.

3. The method of claim 2, wherein the method is a method of dynamically modifying the forming parameters of an ergonomic chair based on real-time quality feedback, characterized in that: The ranking method includes: Determine the minimum number of steps in the equipment relationship network between the equipment to which the dominant parameter item belongs and the equipment to which the driven adjustment parameter item belongs. The step type includes upstream, downstream, and parallel. Determine whether the minimum step count indicates a single upstream / downstream relationship or a parallel relationship, and then calculate the rank factor of the driven adjustment parameter item according to the formula, specifically: ； wherein denotes a rank factor of the driven regulation parameter term, denotes a minimum number of steps, denotes a number of steps of the minimum number of steps, wherein the number of steps is of the type parallel. The subordinate constant and the rank factor are weighted and summed to obtain the rank ordinal number, where the weight of the subordinate constant is 0.77 and the weight of the rank factor is 0.

23. The driven adjustment parameters are classified into levels according to their level ordinal numbers.

4. The method of claim 1, wherein the method is characterized by: The parameter adjustment method includes: The adjustment range is calculated based on the adjustable range of the self-adjusting parameter and the amplitude determination method; The self-regulating parameters currently in use and the self-regulating parameters in each sample are labeled according to the median of their adjustable range. Self-regulating parameters above the median are labeled as high-order parameters, and self-regulating parameters below the median are labeled as low-order parameters. Select the two samples whose labels differ least from the self-regulating parameter terms currently in use; If the dominant parameter and the autonomously regulating parameter change in the same direction in both samples, then the direction of adjustment of the autonomously regulating parameter in this adjustment will be the same as the direction of change of the dominant parameter. If the dominant parameter and the autonomously regulating parameter change in the same direction in both samples, then the direction of adjustment of the autonomously regulating parameter in this adjustment will be opposite to the direction of change of the dominant parameter.

5. The method for dynamic correction of ergonomic chair molding parameters based on real-time quality feedback according to claim 4, characterized in that: The amplitude determination method includes: Obtain the change amount and adjustable range of the dominant parameter item, calculate the proportion of the change amount in the adjustable range, and obtain the active change ratio; The active change ratio is converted into the adjustable range of the passive adjustment parameter to obtain the adjustment amplitude of the passive adjustment parameter.

6. The method for dynamic correction of ergonomic chair molding parameters based on real-time quality feedback according to claim 1, characterized in that: The secondary correction method includes: Select the first three adjustment records with the same dominant parameter, and calculate the average adjustment value of the three autonomous adjustment parameters in the three adjustments; And calculate the ratio between the adjusted average value of the dominant parameter term and the adjusted average value of the driven parameter term; Then calculate the ratio of the adjustment range of the dominant parameter to the adjustment range of the driven parameter in this adjustment; If the change in the calculated ratio relative to the average value exceeds 20%, the adjustment range of the passive adjustment parameter will be corrected so that the change between the two ratios does not exceed 20%. If the change in the calculated ratio relative to the average value does not exceed 20%, the adjustment range of the passive adjustment parameter will be maintained.

7. The method of claim 1, wherein the method is a method of dynamically modifying ergonomic chair forming parameters based on real-time quality feedback. The ranking method includes: Determine the minimum number of steps in the equipment relationship network from the equipment to the equipment to the equipment to the equipment of the driven regulation parameter item. The step type includes upstream, downstream, and parallel. Delete any slave adjustment parameter items of the upstream step type from the minimum number of steps from the device to the device of the master parameter item; Determine whether the minimum number of steps indicates a downstream relationship or a parallel relationship, and then calculate the rank factor of the driven adjustment parameter item according to the formula, specifically: ； wherein represents a rank factor of a driven adjustment parameter term, represents a minimum number of steps, represents a number of steps in which the step type is parallel in the minimum number of steps; The subordinate constant and the rank factor are weighted and summed to obtain the rank ordinal number, where the weight of the subordinate constant is 0.77 and the weight of the rank factor is 0.

23. The driven adjustment parameters are classified into levels according to their level ordinal numbers.

8. An ergonomic chair characterized by: The method for dynamic correction of ergonomic chair molding parameters based on real-time quality feedback, as described in any one of claims 1-7, was used.

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