Seat comfort evaluation method and system based on entropy weight method-TOPSIS

Abstract

The embodiment of the invention provides a seat comfort evaluation method and system based on entropy weight method-TOPSIS, and the method comprises the steps: designing a seat comfort evaluation table, and collecting the test data of a tested person after a seat experience test; constructing a decision matrix based on evaluation objects and evaluation indexes in the test data; standardizing the decision matrix to obtain a standardized matrix, calculating the ratio of the evaluation object in the standardized matrix under the evaluation index, calculating an entropy value according to the ratio, and further calculating the weight of the evaluation index; normalizing the decision matrix to obtain a scoring matrix, and calculating an optimal value and a worst value of an evaluation index in the scoring matrix; and calculating an optimal value distance between the evaluation object and the optimal value and a worst value distance between the evaluation object and the worst value based on the weight, the optimal value and the worst value of the evaluation index, and calculating a comfort result of the evaluation object according to the optimal value distance and the worst value distance.

Description

Technical Field

[0001] The present invention relates to the technical field of seat system comfort, and in particular to a seat comfort evaluation method and system based on an entropy weight method-TOPSIS. Background Art

[0002] With the rapid increase in the number of cars, car ride comfort has become an important manifestation of people's pursuit of high-quality life and an important factor affecting consumers' purchasing habits. The quality of passenger comfort not only directly affects the subjective feelings of vehicle drivers and passengers, but also affects driving fatigue and driving safety. Therefore, more and more automakers and suppliers have invested a lot of resources to improve the comfort of car seats.

[0003] In the process of improving seat comfort, subjective evaluation tools play an important role. Subjective evaluation mainly scores the subjects' subjective feelings about different parts of the seat, and the final comprehensive score is determined based on experience to evaluate the seat comfort.

[0004] However, this evaluation method is relatively subjective, and the weight distribution in each evaluation sub-item is not accurate enough. Therefore, there is currently a lack of scientific and efficient calculation methods for the seat comfort evaluation process, making it difficult to achieve an accurate evaluation of seat comfort. Therefore, it is urgent to develop a seat comfort evaluation method to solve the problems in the existing evaluation method and achieve seat comfort evaluation. Summary of the invention

[0005] In view of the problems existing in the prior art, an embodiment of the present invention provides a seat comfort evaluation method and system based on the entropy weight method-TOPSIS.

[0006] The embodiment of the present invention provides a seat comfort evaluation method based on entropy weight method-TOPSIS, the method comprising: Designing a seat comfort evaluation form to collect test data from test subjects after the seat experience test, wherein the test data includes the evaluation objects and evaluation indicators in the seat comfort evaluation form; Constructing a decision matrix based on the evaluation object and the evaluation index; The decision matrix is ​​standardized to obtain a standardized matrix, the ratio of the evaluation object under the evaluation index in the standardized matrix is ​​calculated, the entropy value is calculated according to the ratio, and the weight of the evaluation index is further calculated; Normalizing the decision matrix to obtain a scoring matrix, and calculating the optimal value and the worst value of the evaluation index in the scoring matrix; Based on the weight, optimal value and worst value of the evaluation index, the optimal value distance between the evaluation object and the optimal value, and the worst value distance between the evaluation object and the worst value are calculated, and the comfort result of the evaluation object is calculated according to the optimal value distance and the worst value distance.

[0007] In one embodiment, the method further comprises: Among them, A is the decision matrix, n is the number of evaluation objects, m is the number of evaluation indicators, is the mth evaluation index of the ith evaluation object.

[0008] In one embodiment, the method further comprises: Determine the positivity or negativity of the evaluation index in the decision matrix, convert the negative evaluation index into a positive evaluation index, convert the absolute value of the evaluation index into a relative value, and obtain a standardized matrix; The standardized calculation formula of the standardized matrix includes: When it is a positive evaluation indicator: When it is a negative evaluation indicator: in, is the standardized value of the mth evaluation index of the ith evaluation object, and .

[0009] In one embodiment, the method further comprises: The calculation formula of the ratio of the evaluation object under the evaluation index includes: in, is the ratio of the i-th evaluation object to the j-th evaluation index in the standardized matrix; The calculation formula of the entropy value includes: in, is the entropy value of the jth evaluation index, and ≥0; The information entropy redundancy of the rating indicator is calculated based on the entropy value, and the calculation formula includes: in, is the information entropy redundancy of the j-th evaluation index; The weight of the evaluation index is calculated based on the information entropy redundancy, and the calculation formula includes: in, is the weight of the jth evaluation index, and and .

[0010] In one embodiment, the normalization process includes: Performing positive processing on the negative evaluation indicators in the decision matrix to obtain a positive matrix, and performing dimensionless processing on the positive matrix to obtain a scoring matrix; The forward processing obtains the forward matrix The calculation formula includes: or The dimensionless processing obtains the scoring matrix The calculation formula includes: Optimal value and the worst value The calculation formula includes: Among them, the optimal value of the jth evaluation index is , the worst value of the jth evaluation index .

[0011] In one embodiment, the method further comprises: in, is the distance between the ith evaluation object and the optimal value, is the distance between the i-th evaluation object and the worst value; The calculation formula of the comfort result includes: in, is the relative proximity of the i-th evaluation object, and , based on the relative proximity value, reflects the comfort result of the evaluation object.

[0012] In one embodiment, the method further comprises: Acquire evaluation indicators related to seat comfort, design a seat comfort evaluation table based on the evaluation indicators, and set scoring results of different levels for the evaluation indicators; Select test subjects with different physical information, who are greater than the number required for the sample, to conduct seat experience tests and collect their test data.

[0013] The embodiment of the present invention provides a seat comfort evaluation system based on entropy weight method-TOPSIS, the system comprising: A test module, used to design a seat comfort evaluation form and collect test data after the test subjects have conducted a seat experience test, wherein the test data includes evaluation objects and evaluation indicators in the seat comfort evaluation form; A construction module, used to construct a decision matrix based on the evaluation object and the evaluation index; An entropy weight method module is used to perform standardization processing on the decision matrix to obtain a standardized matrix, calculate the ratio of the evaluation object under the evaluation index in the standardized matrix, calculate the entropy value according to the ratio, and further calculate the weight of the evaluation index; A TOPSIS module is used to normalize the decision matrix to obtain a scoring matrix, and calculate the optimal value and the worst value of the evaluation index in the scoring matrix; A calculation module is used to calculate the optimal value distance between the evaluation object and the optimal value, and the worst value distance between the evaluation object and the worst value based on the weight, optimal value and worst value of the evaluation index, and calculate the comfort result of the evaluation object according to the optimal value distance and the worst value distance.

[0014] An embodiment of the present invention provides an electronic device, including a processor and a memory; The processor is connected to the memory; The memory is used to store executable program code; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the method described in one or more embodiments.

[0015] An embodiment of the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the seat comfort evaluation method based on the entropy weight method-TOPSIS are implemented.

[0016] In view of the above, in one or more embodiments of the present specification, a seat comfort evaluation table is designed to collect test data of test subjects after the seat experience test; a decision matrix is ​​constructed based on the evaluation objects and evaluation indicators in the test data; the decision matrix is ​​standardized to obtain a standardized matrix, the ratio of the evaluation objects under the evaluation indicators in the standardized matrix is ​​calculated, the entropy value is calculated based on the ratio, and the weight of the evaluation indicators is further calculated; the decision matrix is ​​normalized to obtain a scoring matrix, and the optimal and worst values ​​of the evaluation indicators in the scoring matrix are calculated; based on the weight, optimal value and worst value of the evaluation indicators, the optimal value distance between the evaluation object and the optimal value and the worst value distance between the evaluation object and the worst value are calculated, and the comfort result of the evaluation object is calculated based on the optimal value distance and the worst value distance. In this way, the subjective seat comfort evaluation can be objectively calculated for the comfort value, and the accurate calculation result of the seat comfort of the test subjects can be obtained, thereby providing data reference for subsequent seat comfort evaluation and improvement. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 It is a flow chart of a seat comfort evaluation method based on the entropy weight method-TOPSIS provided in one embodiment of this specification.

[0019] Figure 2 It is a flowchart of a seat comfort evaluation method based on the entropy weight method-TOPSIS provided in another embodiment of this specification.

[0020] Figure 3 It is a structural schematic diagram of a seat comfort evaluation system based on the entropy weight method-TOPSIS provided in an embodiment of this specification.

[0021] Figure 4 It is a structural schematic diagram of an electronic device provided by an embodiment of this specification. DETAILED DESCRIPTION

[0022] The subject matter described herein will now be discussed with reference to example embodiments. It should be understood that the discussion of these embodiments is only to enable those skilled in the art to better understand and implement the subject matter described herein, and is not a limitation of the scope of protection, applicability or examples set forth in the claims. The functions and arrangements of the elements discussed can be changed without departing from the scope of protection of the contents of this specification. Various examples can omit, replace or add various processes or components as needed. For example, the described method can be performed in an order different from the described order, and various steps can be added, omitted or combined. In addition, the features described relative to some examples can also be combined in other examples.

[0023] As used herein, the term "including" and its variations represent open terms, meaning "including but not limited to". The term "based on" means "based at least in part on". The terms "one embodiment" and "an embodiment" mean "at least one embodiment". The term "another embodiment" means "at least one other embodiment". The terms "first", "second", etc. may refer to different or the same objects. Other definitions may be included below, whether explicit or implicit. Unless the context clearly indicates otherwise, the definition of a term is consistent throughout the specification.

[0024] like Figure 1 As shown, the embodiment of the present invention provides a seat comfort evaluation method based on the entropy weight method-TOPSIS, including: Step S101, designing a seat comfort evaluation table, collecting test data after test subjects have performed a seat experience test, wherein the test data includes evaluation objects and evaluation indicators in the seat comfort evaluation table.

[0025] Specifically, in this embodiment, when evaluating the comfort of the member seat system, it is necessary to first determine the evaluation indicators that affect the seat comfort, wherein the evaluation indicators may be, for example, seat height, cushion width, cushion depth, cushion hardness, cushion support, backrest height, backrest width, backrest hardness, etc. A seat comfort evaluation table can be designed based on the above evaluation indicators, and each evaluation indicator can be set with different levels of scoring results. For example, according to the subjective feeling of comfort, 5 levels can be set, including poor, poor, general, good, and excellent. After designing the seat comfort evaluation table, a seat experience test is conducted, and the test data after the test subjects conduct the seat experience test is collected. The test data is the seat comfort evaluation table filled in by the test subjects for different seats to be evaluated (evaluation objects) after the test. Among them, the number of test subjects needs to be greater than the minimum sample size requirement, and the number of people with different physical information must also meet the quantity requirements. For example, there must be a certain number of test subjects of different genders, heights, weights, and ages to ensure the comprehensiveness of the test subjects' subjective evaluation and improve the accuracy of the subsequent evaluation method.

[0026] Step S102: constructing a decision matrix based on the evaluation object and the evaluation index.

[0027] Specifically, after collecting the evaluation data of the test subjects, that is, the evaluation indicators (various data of the seat comfort evaluation table) of the test subjects for the evaluation objects (different seats to be evaluated), a decision matrix is ​​constructed based on the evaluation data and evaluation indicators. Assuming that the objects of the seat experience test are n seats to be evaluated and the evaluation indicators of the seat comfort evaluation table are m, the constructed decision matrix is ​​A= , the decision matrix is ​​the entropy weight method and the preceding step of TOPSIS in the subsequent steps. The specific decision matrix is: Among them, A is the decision matrix, n is the number of evaluation objects, m is the number of evaluation indicators, is the mth evaluation index of the ith evaluation object.

[0028] Step S103, normalizing the decision matrix to obtain a standardized matrix, calculating the ratio of the evaluation object under the evaluation index in the standardized matrix, calculating the entropy value according to the ratio, and further calculating the weight of the evaluation index.

[0029] Specifically, the entropy weight method can be used to objectively assign weights to various evaluation indicators, and the weights can be determined by calculating the information entropy of each evaluation indicator. The smaller the information entropy, the greater the amount of information provided by the evaluation indicator, and the corresponding weight is also greater. The decision matrix in the above steps is standardized. The standardization process is as follows: determine the positive and negative direction of each indicator, and then use the range transformation method to convert the negative indicators in the decision matrix into positive indicators, convert the absolute values ​​of the evaluation indicators into relative values, and achieve homogeneity of different indicators. Obtain the standardized matrix B = , the standardized calculation formula of the standardized matrix includes: When it is a positive evaluation indicator: When it is a negative evaluation indicator: in, is the standardized value of the mth evaluation index of the ith evaluation object, and .

[0030] After determining the annotated matrix, for all scheme values ​​of each evaluation indicator, calculate its ratio relative to the total, that is, calculate the ratio of the i-th evaluation object to the j-th indicator value based on the standardized data. The calculation formula of the ratio of the evaluation object under the evaluation indicator includes: in, is the ratio of the i-th evaluation object to the j-th evaluation index in the standardized matrix; Then the entropy value is calculated according to the ratio of each evaluation object under each evaluation index, including: in, is the entropy value of the jth evaluation index, and ≥0; Then, the information entropy redundancy of the rating indicators is calculated based on the entropy value to reflect the difference in importance between the evaluation indicators. The calculation formula includes: in, is the information entropy redundancy of the j-th evaluation index; Finally, the weight of the evaluation index is calculated based on the information entropy redundancy. The calculation formula includes: in, is the weight of the jth evaluation index, and and .

[0031] Step S104, normalizing the decision matrix to obtain a scoring matrix, and calculating the optimal value and the worst value of the evaluation index in the scoring matrix.

[0032] Specifically, the TOPSIS method is a ranking technique that approximates ideal solutions. It assumes that there is an optimal solution (positive ideal solution) and a worst solution (negative ideal solution) in a finite number of solutions, and the subsequent best choice should be the one that is closest to the positive ideal solution and farthest from the negative ideal solution. The decision matrix in the above steps is normalized. The normalization process includes trending and dimensionless processing of the evaluation indicators in the decision matrix to obtain a scoring matrix, where the trending process is positive processing, that is, the negative indicators in the decision matrix A are positively processed, and the positive indicators are kept unchanged to obtain a positive matrix. The positive matrix The calculation formula includes: or Then the matrix after forward transformation is dimensionless to eliminate the influence of different data indicator dimensions and obtain the scoring matrix , calculation formula, including: From the above scoring matrix, find the maximum value of each indicator as part of the positive ideal solution and the minimum value as part of the negative ideal solution, that is, calculate the optimal value of each evaluation indicator and the worst value , calculation formula, including: Among them, the optimal value of the jth evaluation index is , the worst value of the jth evaluation index .

[0033] Step S105, based on the weight, optimal value and worst value of the evaluation index, calculate the optimal value distance between the evaluation object and the optimal value, and the worst value distance between the evaluation object and the worst value, and calculate the comfort result of the evaluation object according to the optimal value distance and the worst value distance.

[0034] Specifically, under the weight of each evaluation index, the Euclidean distance between each evaluation object and the positive and negative ideal solutions is calculated, that is, the optimal value distance between the evaluation object and the optimal value, and the worst value distance between the evaluation object and the worst value. The calculation formula includes: in, is the distance between the ith evaluation object and the optimal value, is the distance between the ith evaluation object and the worst value.

[0035] Then, the degree of closeness of each evaluation object to the positive ideal solution is calculated based on the above distance, that is, the relative closeness of each evaluation object to the optimal solution. The closer the relative closeness value is to 1, the more comfortable the seat corresponding to the evaluation object is. The calculation formula for the comfort result is as follows: in, is the relative proximity of the i-th evaluation object, and The value based on the relative proximity reflects the comfort result of the evaluation object. The closer each evaluation object is to the optimal solution 1, the more comfortable the evaluation seat object is; the closer it is to the worst solution, the less comfortable the evaluation seat object is.

[0036] In addition, after the comfort results are determined, the comfort evaluation of multiple evaluation objects can be ranked, thereby determining the comfort ranking of multiple seats. Moreover, for different evaluation objects, the evaluation preferences of people with different physical information can be determined according to the different physical information of the subjects, such as the evaluation preference tendency of male subjects, the evaluation preference tendency of elderly subjects, etc., so as to design corresponding seats for users with different physical information in a more targeted manner.

[0037] The embodiment of the present invention provides a seat comfort evaluation method based on the entropy weight method-TOPSIS, which designs a seat comfort evaluation table, collects test data after the test subjects have performed a seat experience test; constructs a decision matrix based on the evaluation object and evaluation index in the test data; standardizes the decision matrix to obtain a standardized matrix, calculates the ratio of the evaluation object under the evaluation index in the standardized matrix, calculates the entropy value based on the ratio, and further calculates the weight of the evaluation index; normalizes the decision matrix to obtain a scoring matrix, and calculates the optimal value and the worst value of the evaluation index in the scoring matrix; based on the weight, optimal value and worst value of the evaluation index, calculates the optimal value distance between the evaluation object and the optimal value, and the worst value distance between the evaluation object and the worst value, and calculates the comfort result of the evaluation object based on the optimal value distance and the worst value distance. In this way, the subjective seat comfort evaluation can be objectively calculated to obtain the comfort calculation result, thereby providing data reference for subsequent seat comfort evaluation and improvement.

[0038] In another embodiment, a specific seat comfort evaluation method based on entropy weight method-TOPSIS can be as follows: Figure 2 As shown, in Figure 2The seat comfort evaluation method based on the entropy weight method-TOPSIS includes 4 parts, including: 1. Data preparation part: including designing a subjective evaluation scale for seat comfort, recruiting subjects to conduct subjective comfort evaluation experiments, and sorting and statistically analyzing subjective scoring data; 2. Entropy weight method part: including constructing a decision matrix through the collected subject data, standardizing the evaluation indicators to obtain a standardized matrix, and calculating the weights of each evaluation indicator; 3. TOPSIS part: including trending and dimensionless processing the decision matrix to obtain a scoring matrix, calculating the optimal and worst values ​​of the evaluation indicators, and the distance between the optimal and worst values ​​with the weight of the evaluation indicators, calculating the relative closeness of each evaluation object to the optimal solution, and obtaining a comprehensive evaluation of each evaluation object; 4. Sorting part: including sorting the comfort of car seats according to the comprehensive evaluation.

[0039] See also Figure 3 , Figure 3 1 is a schematic diagram of the structure of a seat comfort evaluation system based on the entropy weight method-TOPSIS provided in an embodiment of the present application. Figure 3 As shown, the system comprises: The test module S301 is used to design a seat comfort evaluation table and collect test data after the test subjects have performed a seat experience test, wherein the test data includes evaluation objects and evaluation indicators in the seat comfort evaluation table; A construction module S302 is used to construct a decision matrix based on the evaluation object and the evaluation index; The entropy weight method module S303 is used to perform standardization processing on the decision matrix to obtain a standardized matrix, calculate the ratio of the evaluation object under the evaluation index in the standardized matrix, calculate the entropy value according to the ratio, and further calculate the weight of the evaluation index; TOPSIS module S304 is used to normalize the decision matrix to obtain a scoring matrix, and calculate the optimal value and the worst value of the evaluation index in the scoring matrix; The calculation module S305 is used to calculate the optimal value distance between the evaluation object and the optimal value, and the worst value distance between the evaluation object and the worst value based on the weight, optimal value and worst value of the evaluation index, and calculate the comfort result of the evaluation object according to the optimal value distance and the worst value distance.

[0040] Those skilled in the art can clearly understand that the technical solutions of the embodiments of the present application can be implemented with the help of software and / or hardware. The "unit" and "module" in this specification refer to software and / or hardware that can independently complete or cooperate with other components to complete specific functions, where the hardware can be, for example, a field programmable gate array (FPGA), an integrated circuit (IC), etc.

[0041] Each processing unit and / or module of the embodiments of the present application may be implemented by an analog circuit that implements the functions described in the embodiments of the present application, or may be implemented by software that executes the functions described in the embodiments of the present application.

[0042] See also Figure 4 , which shows a schematic diagram of the structure of an electronic device involved in an embodiment of the present application, the electronic device can be used to implement Figure 1 The method in the embodiment shown. Figure 4 As shown, the electronic device 400 may include: at least one processor 401 , at least one network interface 404 , a user interface 403 , a memory 405 , and at least one communication bus 402 .

[0043] The communication bus 402 is used to realize the connection and communication between these components.

[0044] The user interface 403 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 403 may also include a standard wired interface and a wireless interface.

[0045] The network interface 404 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0046] Among them, the processor 401 may include one or more processing cores. The processor 401 uses various interfaces and lines to connect various parts of the entire electronic device 400, and executes various functions and processes data of the terminal 400 by running or executing instructions, programs, code sets or instruction sets stored in the memory 405, and calling data stored in the memory 405. Optionally, the processor 401 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 401 can integrate one or a combination of a processor (Central Processing Unit, CPU), an image processor (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; and the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 401, and it can be implemented separately through a chip.

[0047] Among them, the memory 405 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 405 includes a non-transitory computer-readable storage medium. The memory 405 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 405 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 405 may also be optionally at least one storage device located away from the aforementioned processor 401. As Figure 4 As shown, the memory 405 as a computer storage medium may include an operating system, a network communication module, a user interface module, and program instructions.

[0048] exist Figure 4In the electronic device 400 shown, the user interface 403 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 401 can be used to call the interactive application based on image generation stored in the memory 405, and specifically perform the following operations: design a seat comfort evaluation table, collect test data after the subjects have undergone seat experience tests; construct a decision matrix based on the evaluation objects and evaluation indicators in the test data; standardize the decision matrix to obtain a standardized matrix, calculate the ratio of the evaluation objects under the evaluation indicators in the standardized matrix, calculate the entropy value based on the ratio, and further calculate the weight of the evaluation indicator; normalize the decision matrix to obtain a scoring matrix, and calculate the optimal value and the worst value of the evaluation indicators in the scoring matrix; based on the weight, optimal value and worst value of the evaluation indicators, calculate the optimal value distance between the evaluation object and the optimal value, and the worst value distance between the evaluation object and the worst value, and calculate the comfort result of the evaluation object based on the optimal value distance and the worst value distance.

[0049] The present application also provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps of the above method are implemented. The computer-readable storage medium may include, but is not limited to, any type of disk, including a floppy disk, an optical disk, a DVD, a CD-ROM, a micro drive, and a magneto-optical disk, a ROM, a RAM, an EPROM, an EEPROM, a DRAM, a VRAM, a flash memory device, a magnetic card or an optical card, a nanosystem (including a molecular memory IC), or any type of medium or device suitable for storing instructions and / or data.

[0050] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0051] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0052] In the several embodiments provided in the present application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0053] 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, that is, they 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.

[0054] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0055] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, disk or optical disk and other media that can store program codes.

[0056] A person skilled in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by entering a program to instruct related hardware, and the program may be stored in a computer-readable memory, which may include a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0057] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A seat comfort evaluation method based on entropy weight method-TOPSIS, the method comprising: Designing a seat comfort evaluation form to collect test data from test subjects after the seat experience test, wherein the test data includes the evaluation objects and evaluation indicators in the seat comfort evaluation form; Constructing a decision matrix based on the evaluation object and the evaluation index; The decision matrix is ​​standardized to obtain a standardized matrix, the ratio of the evaluation object under the evaluation index in the standardized matrix is ​​calculated, the entropy value is calculated according to the ratio, and the weight of the evaluation index is further calculated; Normalizing the decision matrix to obtain a scoring matrix, and calculating the optimal value and the worst value of the evaluation index in the scoring matrix; Based on the weight, optimal value and worst value of the evaluation index, the optimal value distance between the evaluation object and the optimal value, and the worst value distance between the evaluation object and the worst value are calculated, and the comfort result of the evaluation object is calculated according to the optimal value distance and the worst value distance.

2. The method according to claim 1, characterized in that The decision matrix comprises: Among them, A is the decision matrix, n is the number of evaluation objects, m is the number of evaluation indicators, is the mth evaluation index of the ith evaluation object.

3. The method according to claim 2, characterized in that The step of normalizing the decision matrix to obtain a normalized matrix includes: Determine the positivity or negativity of the evaluation index in the decision matrix, convert the negative evaluation index into a positive evaluation index, convert the absolute value of the evaluation index into a relative value, and obtain a standardized matrix; The standardized calculation formula of the standardized matrix includes: When it is a positive evaluation indicator: When it is a negative evaluation indicator: in, is the standardized value of the mth evaluation index of the ith evaluation object, and .

4. The method according to claim 3, characterized in that The calculating the ratio of the evaluation object in the standardized matrix under the evaluation index, calculating the entropy value according to the ratio, and further calculating the weight of the evaluation index includes: The calculation formula of the ratio of the evaluation object under the evaluation index includes: in, is the ratio of the i-th evaluation object to the j-th evaluation index in the standardized matrix; The calculation formula of the entropy value includes: in, is the entropy value of the jth evaluation index, and ≥0; The information entropy redundancy of the rating indicator is calculated based on the entropy value, and the calculation formula includes: in, is the information entropy redundancy of the j-th evaluation index; The weight of the evaluation index is calculated based on the information entropy redundancy, and the calculation formula includes: in, is the weight of the jth evaluation index, and and .

5. The method according to claim 4, characterized in that The step of normalizing the decision matrix to obtain a scoring matrix and calculating the optimal value and the worst value of the evaluation index in the scoring matrix includes: The standardization process includes: Performing positive processing on the negative evaluation indicators in the decision matrix to obtain a positive matrix, and performing dimensionless processing on the positive matrix to obtain a scoring matrix; The forward processing obtains the forward matrix The calculation formula includes: or The dimensionless processing obtains the scoring matrix The calculation formula includes: Optimal value and the worst value The calculation formula includes: Among them, the optimal value of the jth evaluation index is , the worst value of the jth evaluation index .

6. The method according to claim 5, characterized in that The calculation formulas for the optimal value distance and the worst value distance include: in, is the distance between the ith evaluation object and the optimal value, is the distance between the i-th evaluation object and the worst value; The calculation formula of the comfort result includes: in, is the relative proximity of the i-th evaluation object, and , based on the relative proximity value, reflects the comfort result of the evaluation object.

7. The method according to claim 1, characterized in that The seat comfort evaluation form is designed to collect test data of test subjects after the seat experience test, including: Acquire evaluation indicators related to seat comfort, design a seat comfort evaluation table based on the evaluation indicators, and set scoring results of different levels for the evaluation indicators; Select test subjects with different physical information, who are greater than the number required for the sample, to conduct seat experience tests and collect their test data.

8. A seat comfort evaluation system based on entropy weight method-TOPSIS, characterized in that: The system comprises: A test module, used to design a seat comfort evaluation form and collect test data after the test subjects have conducted a seat experience test, wherein the test data includes evaluation objects and evaluation indicators in the seat comfort evaluation form; A construction module, used to construct a decision matrix based on the evaluation object and the evaluation index; An entropy weight method module is used to perform standardization processing on the decision matrix to obtain a standardized matrix, calculate the ratio of the evaluation object under the evaluation index in the standardized matrix, calculate the entropy value according to the ratio, and further calculate the weight of the evaluation index; A TOPSIS module is used to normalize the decision matrix to obtain a scoring matrix, and calculate the optimal value and the worst value of the evaluation index in the scoring matrix; A calculation module is used to calculate the optimal value distance between the evaluation object and the optimal value, and the worst value distance between the evaluation object and the worst value based on the weight, optimal value and worst value of the evaluation index, and calculate the comfort result of the evaluation object according to the optimal value distance and the worst value distance.

9. An electronic device comprising a processor and a memory; The processor is connected to the memory; The memory is used to store executable program code; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method according to any one of claims 1 to 7.

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