An automobile maintenance and repair price evaluation method based on multi-dimensional data fusion

Abstract

The application discloses a kind of based on multi-dimensional data fusion's automobile maintenance and repair price evaluation method, specifically related to the field of automobile service, first, the price data of automobile maintenance and repair price, time cost data and maintenance quality data are collected, then the corresponding price, time cost and maintenance quality evaluation model is constructed, and two-stage adaptive weight adjustment is carried out, the multi-dimensional price comprehensive score is calculated, and the final price result is presented to the automobile user client, the deviation analysis is carried out to the maintenance merchant result selected by automobile user, and the automobile maintenance and repair price evaluation process is optimized according to the deviation analysis result.The application builds price evaluation model, time cost evaluation model and maintenance quality evaluation model by price comparison;At the same time, the multi-dimensional price comprehensive score is calculated by model adaptive weight optimization and deep optimization, so that the evaluation result is more scientific and reliable, and the owner makes a wise decision based on data.

Description

Technical Field

[0001] This invention relates to the field of automotive service technology, specifically to a method for comparative evaluation of automotive repair and maintenance prices based on multi-dimensional data fusion. Background Technology

[0002] With the continuous growth of car ownership, the car repair and maintenance market is expanding. When choosing repair and maintenance services, car owners often face problems of information asymmetry and limited evaluation methods. For car owners, how to compare prices among many car repair and maintenance service providers and make an informed choice based on the comparison and evaluation results has become a key issue.

[0003] Most existing price comparison systems only make static comparisons based on the prices of maintenance and repair items, providing quotes from various repair shops for different items, ignoring the impact of key factors such as time cost, repair quality, and repair price on the overall service value; they hardly involve the assessment of maintenance and repair time costs, nor do they involve consideration of repair quality, including the professional level of repair technicians, the quality of parts used, and the standardization of repair processes.

[0004] The current automotive repair and maintenance market suffers from numerous shortcomings in price comparison and evaluation. For example, existing systems generally employ static evaluation models, meaning data updates are often untimely and fail to reflect real-time market changes and the dynamic fluctuations in repair shop service quality. Repair time not only impacts car owners' daily travel plans but can also indirectly lead to other economic costs. Low-quality repairs may cause vehicle malfunctions to recur in a short period, forcing owners to return for further repairs, increasing both repair and time costs. Consequently, car owners cannot fully assess a repair shop's reputation and service reliability when choosing one, increasing the risk of selecting a substandard shop. Therefore, a dynamic price comparison and evaluation method that comprehensively considers multiple factors is urgently needed to meet car owners' growing demand for high-quality, efficient repair and maintenance services. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a method for evaluating the price comparison of automobile repair and maintenance based on multi-dimensional data fusion, 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 comparative evaluation of automobile repair and maintenance prices based on multi-dimensional data fusion, comprising:

[0007] S1: Collect price data, time cost data, and repair quality data for car repair and maintenance through the data acquisition interface;

[0008] S2: Analyze the collected price data, time cost data, and repair quality data respectively, and construct a price comparison evaluation model, a time cost comparison evaluation model, and a repair quality comparison evaluation model;

[0009] S3: Adaptive weight adjustment is performed on the price comparison evaluation model, the time cost comparison evaluation model, and the maintenance quality comparison evaluation model to obtain the adaptive weight adjustment results of each model;

[0010] S4: Based on the adaptive weight adjustment results, the multi-dimensional price comparison comprehensive score is calculated by integrating the price comparison evaluation model, the price comparison time cost evaluation model, and the price comparison repair quality evaluation model. The multi-dimensional price comparison comprehensive score is then corrected to generate a price comparison result with confidence interval, and the final price comparison result is presented to the car user's client.

[0011] S5: Record the repair shop selection results of the car user client based on the final price comparison results, perform deviation analysis on the selected repair shop results, and optimize the car repair and maintenance price comparison evaluation process based on the deviation analysis results.

[0012] The technical effects and advantages of this invention are as follows:

[0013] 1. This invention collects price data, time cost data, and repair quality data for car repair and maintenance through a data acquisition interface, enabling multi-dimensional factor analysis. This changes the traditional static price comparison method and allows for a comprehensive and dynamic evaluation of car repair and maintenance services in subsequent price comparison assessment steps, providing car owners with a more accurate reference for service value.

[0014] 2. This invention constructs a price comparison evaluation model, a time cost comparison evaluation model, and a repair quality comparison evaluation model through a multi-dimensional price comparison evaluation model calculation process; at the same time, it calculates a comprehensive multi-dimensional price comparison score through model adaptive weight basic optimization and deep optimization, making the evaluation results more scientific and reliable, and helping car owners make informed decisions based on data;

[0015] 3. This invention optimizes the car repair and maintenance price comparison evaluation feedback process by performing deviation analysis on the selected repair shops and optimizing the car repair and maintenance price comparison evaluation process based on the deviation analysis results. This provides car owners with the latest price comparison information, meets the personalized needs of different car owners at different times, and improves car owner satisfaction. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the overall process of the present invention.

[0017] Figure 2 This is a schematic diagram of the method flow of the present 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] Please see Figure 1 As shown, the present invention provides a car repair and maintenance price comparison evaluation system based on multi-dimensional data fusion, including a multi-dimensional price comparison data acquisition module, a multi-dimensional price comparison evaluation model calculation module, a multi-dimensional evaluation model adaptive weight adjustment module, a car repair and maintenance price comparison evaluation result presentation module, and a car repair and maintenance price comparison evaluation feedback optimization module.

[0020] The multidimensional price comparison data acquisition module is connected to the multidimensional price comparison evaluation model calculation module. The multidimensional evaluation model adaptive weight adjustment module is connected to both the multidimensional price comparison evaluation model calculation module and the automobile repair and maintenance price comparison evaluation result presentation module. The automobile repair and maintenance price comparison evaluation feedback optimization module is connected to the automobile repair and maintenance price comparison evaluation result presentation module.

[0021] Multi-dimensional price comparison data acquisition module: Through the data acquisition interface, it collects price data, time cost data, and repair quality data for car repair and maintenance, and transmits the collected data to the multi-dimensional price comparison evaluation model calculation module;

[0022] Multidimensional price comparison evaluation model calculation module: Analyzes the collected price data, time cost data and repair quality data respectively, constructs a price comparison evaluation model, a time comparison evaluation model and a repair quality comparison evaluation model, and transmits the constructed multidimensional price comparison evaluation model to the multidimensional evaluation model adaptive weight adjustment module;

[0023] The multidimensional evaluation model adaptive weight adjustment module: performs adaptive weight adjustment on the price comparison evaluation model, the time cost comparison evaluation model, and the repair quality comparison evaluation model, and transmits the adaptive weight adjustment results of each model to the automobile repair and maintenance price comparison evaluation result presentation module.

[0024] The vehicle repair and maintenance price comparison evaluation result presentation module: Based on the adaptive weight adjustment result, it integrates the price comparison evaluation model, the time cost comparison evaluation model, and the repair quality comparison evaluation model to calculate a multi-dimensional price comparison comprehensive score. Based on the multi-dimensional price comparison comprehensive score, it generates a price comparison result with confidence interval and presents the final price comparison result to the car user's client.

[0025] The car repair and maintenance price comparison evaluation feedback optimization module records the repair shop selected by the car user client based on the final price comparison results, performs deviation analysis on the selected repair shop results, and optimizes the car repair and maintenance price comparison evaluation process based on the deviation analysis results.

[0026] Please see Figure 2 As shown, a method for comparing and evaluating car repair and maintenance prices based on multi-dimensional data fusion includes: S1: collecting price data, time cost data, and repair quality data for car repair and maintenance through a data acquisition interface; S2: analyzing the collected price data, time cost data, and repair quality data respectively, and constructing a price comparison evaluation model, a time comparison evaluation model, and a repair quality comparison evaluation model; S3: adaptively adjusting the weights of the price comparison evaluation model, the time comparison evaluation model, and the repair quality comparison evaluation model to obtain the adaptive weight adjustment results for each model; S4: based on the adaptive weight adjustment results, fusing the price comparison evaluation model, the time comparison evaluation model, and the repair quality comparison evaluation model to calculate a multi-dimensional price comparison comprehensive score, correcting the multi-dimensional price comparison comprehensive score to generate a price comparison result with a confidence interval, and presenting the final price comparison result to the car user's client; S5: recording the repair shop selected by the car user's client based on the final price comparison result, performing deviation analysis on the selected repair shop result, and optimizing the car repair and maintenance price comparison evaluation process based on the deviation analysis results.

[0027] S1: Collect price data, time cost data, and repair quality data for automotive repair and maintenance through a data acquisition interface; the price data includes the price of the same maintenance item, the market average price, the price of the required replacement parts, the corresponding market average price of the required replacement parts, the labor cost in the maintenance item, and the industry benchmark labor cost; the time cost data includes the repair shop's response time, the number of orders completed on time, and the one-way driving time; the repair quality data includes the number of industry certifications held by technicians, the total number of technicians, the types of parts required for replacement, the quantity of parts from the parts source, the execution record of process steps, and standard maintenance process specifications;

[0028] This embodiment requires specific explanation of the following: Price data collection: Real-time collection of price information from various repair shops for different car brands, models, and specific maintenance items. This includes not only basic maintenance items (such as oil changes and filter replacements) but also prices for various complex repair items (such as engine overhauls and transmission repairs); Time cost data collection: Obtaining the estimated and actual completion times of different maintenance items from repair shops. Through the repair shop's work order system and car owner feedback, the time from sending the item for repair to delivery is statistically analyzed, and the time consumption of each stage of the repair process (such as diagnosis, parts preparation, repair operation, and quality inspection) is analyzed; Quality data collection: Collecting the quality information of the parts used by the repair shops, including the brand of the parts and whether they are genuine original parts. Analyzing the repair shop's repair quality records, such as return rate and first-time success rate of fault resolution.

[0029] S2: Analyze the collected price data, time cost data, and repair quality data respectively, and construct a price comparison evaluation model, a time cost comparison evaluation model, and a repair quality comparison evaluation model, including the following steps:

[0030] S2.1: Within the preset evaluation period, through the automotive repair and maintenance industry data platform, collect n1 times the price p collected for the j-th repair shop for the same maintenance item (i.e., repair and maintenance item) for the i-th time. j,i Obtain the market average price fluctuation coefficient η(p) for this maintenance and repair project. ,μ(p i The price pp represents the average market price at the time of the i-th data collection for this maintenance and repair item. This average market price is calculated by averaging the prices of registered maintenance and repair businesses with the same maintenance and repair item from the data platform. Then, the price pp of the j-th maintenance and repair business for the i-th time in the maintenance and repair item is collected n2 times. j,i The average market price μ(pp) corresponding to the required replacement parts i ), thus obtaining the component price fluctuation coefficient η(pp), The average market price of the parts refers to the average price of the parts required for maintenance and repair projects, obtained by filtering out registered repair shops from the automotive repair and maintenance industry data platform and selecting the shops that offer the parts required for maintenance and repair projects, along with their corresponding prices. Then, the j-th repair shop's labor cost (moy) is collected n3 times for the i-th time in the maintenance and repair project. j,i Compared to industry benchmark hourly rates j,0 The difference coefficient η(moy), Finally, the price comparison evaluation model P1 is obtained, P1=exp-[η(p)+η(pp)+η(moy)];

[0031] S2.2: Within the preset evaluation period, collect the time (in minutes) t from the user initiating the maintenance project request to the maintenance provider's first response during the i-th data collection for the j-th maintenance provider (n4 times). i After taking the logarithm and standardizing, the response time index η(t) is obtained. Simultaneously, the number of orders (nd) completed on time by the repair shop within the promised construction time for the n4 maintenance projects is calculated to obtain the construction completion rate η(nd), where η(nd) = nd / n4; The one-way driving time (tg) from the car user to the repair shop in the n4 maintenance projects is also calculated. i (minutes), to obtain the service efficiency attenuation coefficient η(tg), Finally, the price comparison time cost assessment model P2 is obtained, P2=η(t)+η(nd)+η(tg);

[0032] S2.3: Within a preset evaluation period, collect the ratio of the number nh of industry certifications (such as ASE for automotive repair, I-CAR, etc.) held by the technicians of the j-th repair shop to the total number of technicians T_nh, to obtain the technical certification coverage rate η(nh), η(nh) = nh / T_nh; collect the set CT of the source types of replacement parts required for the maintenance project of the j-th repair shop n2 times, CT = [ct1, ct2, ..., ctm], where m is the number of source types of replacement parts, and ctm is the source of the m-th part. The source of parts can be, for example, original parts, brand parts, aftermarket parts, etc., and then count the set n_CT of the number of parts from the m-th part source, n_CT = [n_ct1, n_ct2, ..., n_ctm], where n_ctm is the number of parts from the m-th part source, to obtain the original parts compatibility rate η(CT). Where n_ctJ represents the quantity of original parts; through the repair shop's digital management system, the execution record of the i-th process step during n5 complete maintenance processes is collected and compared with the standard maintenance process specification to obtain the maintenance process specification coefficient η(DP). I() is an exponential function, where I=1 indicates that the maintenance process conforms to the specifications, otherwise I=0, M is the total number of standard maintenance process steps, and q N and q N,0 These are the execution records of the Nth process step and the corresponding standard process step execution records, respectively; finally, the price comparison maintenance quality assessment model P3 is obtained, P3=η(nh)+η(CT)+η(DP);

[0033] S3: Adaptive weight adjustment is performed on the price comparison evaluation model, time cost comparison evaluation model, and repair quality comparison evaluation model to obtain the adaptive weight adjustment results for each model, including the following steps:

[0034] S3.1: Basic Optimization of Model Weights: Within the preset evaluation period, the i-th model weight W is first obtained through big data analysis technology. I , W I，0 P represents the base weights of the i-th model, α is the weight adjustment coefficient, and its value is [0,1]. I P I ,avg、P I ,max and P I ,min represent the current value, historical average value, historical maximum value, and historical minimum value of the I-th model, respectively. I=1 represents the price comparison evaluation model, I=2 represents the price comparison time cost evaluation model, and I=3 represents the price comparison repair quality evaluation model. The basic weights are obtained from historical data using a linear regression model to determine the basic weights of the price comparison evaluation model, the price comparison time cost evaluation model, and the price comparison repair quality evaluation model.

[0035] S3.2: Model Weight Depth Optimization: Within a preset evaluation period, based on maintenance requests initiated by car users, the proportion of orders in the car user's historical orders that selected the lowest price option is retrieved and used as the price comparison evaluation model preference strength Q1. n_dj represents the number of orders that selected the lowest price option, Tnd represents the total number of historical orders, and the percentage of car users who selected original parts is also retrieved as the preference strength Q3 of the price comparison repair quality assessment model. n_ctJ represents the quantity of original parts. Simultaneously, using a natural language processing model, the proportion of orders placed when car users inquired through the client-side interactive platform is obtained, serving as the preference strength Q2 for the price comparison time cost assessment model. Let n_tim be the number of orders at the query time, and obtain the I-th model weight W for deep optimization. I use , W I Let Q be the weight of the i-th model. I Let β be the preference strength of the i-th model. I Let be the preference coefficient of the i-th model. W I，max A preset upper limit is set for the weight of the i-th model to prevent a single indicator from monopolizing the evaluation; finally, the adaptive weight adjustment results of each model are obtained, including the weight W1 of the price comparison evaluation model. use Price comparison time cost assessment model weight W2 use Price comparison repair quality assessment model weight W3 use ;

[0036] In this embodiment, it is necessary to specifically explain that firstly, historical data is used to filter out semantic texts related to time, such as "urgent," "as soon as possible," and "what time," which are used as the training set for a natural language processing model (such as the BERT model). The trained natural language processing model is then output to identify orders that inquire about time on the client interaction platform and obtain the number of orders that inquire about time. The natural language processing model is an existing technology.

[0037] S4: Based on the adaptive weight adjustment results, a multi-dimensional price comparison comprehensive score is calculated by fusing the price evaluation model, the time cost evaluation model, and the repair quality evaluation model. The multi-dimensional price comparison comprehensive score is then corrected to generate a price comparison result with confidence intervals, and the final price comparison result is presented to the car user's client. This includes the following steps:

[0038] S4.1: Within the preset evaluation period, based on the adaptive weight adjustment results, the multi-dimensional price comparison comprehensive score S of the j-th repair shop is calculated by integrating the price comparison evaluation model, the time cost comparison evaluation model, and the repair quality comparison evaluation model. j , P I For the i-th evaluation model value, W I use The I-th model weight is the result of deep optimization.

[0039] S4.2: Within the preset evaluation period, based on the number of service orders for the j-th repair merchant, adjust the multi-dimensional price comparison comprehensive score to generate the price comparison result S for the j-th repair merchant with a confidence interval. j u , S j For the j-th repair shop, a comprehensive score is given based on multi-dimensional price comparison. j 、N0、N min and N max These represent the number of service orders for the j-th repair shop, the industry benchmark number of service orders, the minimum number of orders in the industry, and the maximum number of orders in the industry, respectively, where N0 ≥ N. min Finally, the final price comparison result S of the j-th repair shop is obtained. j u ;

[0040] S4.3: Within the preset evaluation period, based on the final price comparison result S of the j-th repair shop. j u The system iterates through price comparison results from various repair shops offering car maintenance and repair services, and then outputs the results via a visualization function r. j The standardized price comparison results are presented in chart form, sorted from largest to smallest, to the car user's client. r jFor the output price comparison result of the j-th repair shop, min(S) j u ) and max(S j u S represents the price comparison results from various repair shops. j u The minimum and maximum values, R(S) j u S represents the price comparison result. j u The preset upper limit value;

[0041] This embodiment specifically explains the use of chart formats such as bar charts and radar charts. Bar charts clearly display the final ranking of different repair shops, while radar charts intuitively reflect the performance of each repair shop in different dimensions such as price, time cost, and quality. Detailed data reports are also provided, showing the specific data and calculation process for each repair shop's various indicators, facilitating in-depth understanding for car users.

[0042] S5: Record the repair shop selections made by car users based on the final price comparison results, perform deviation analysis on the selected repair shop results, and optimize the car repair and maintenance price comparison evaluation process based on the deviation analysis results, including the following steps:

[0043] S5.1: Within the preset evaluation period, record the repair shop finally selected by the user, compare the ranking results of each repair shop obtained by the car repair and maintenance price comparison evaluation result presentation module, filter out U repair shops whose price comparison results are not the largest, and calculate the selection deviation coefficient η(de), η(de)=U / TU, where TU is the total number of car user maintenance project requests;

[0044] S5.2: Compare the calculated selection deviation coefficient η(de) with the threshold. If it is less than the threshold, it indicates that the car repair and maintenance price comparison assessment is good. Otherwise, optimize the car repair and maintenance price comparison assessment process. For example, based on the repair shop selected by the car user, analyze the three assessment models of the repair shop and dynamically adjust the weights of the three assessment models.

[0045] Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other.

[0046] In conclusion, the above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention should be considered as such.

[0047] It is included within the scope of protection of this invention.

Claims

1. A method for comparative evaluation of automobile repair and maintenance prices based on multi-dimensional data fusion, characterized in that: include: S1: Collect price data, time cost data, and repair quality data for car repair and maintenance through the data acquisition interface; S2: Analyze the collected price data, time cost data, and repair quality data respectively, and construct a price comparison evaluation model, a time cost comparison evaluation model, and a repair quality comparison evaluation model; S2 constructs a price comparison time cost assessment model: the time t from the user initiating a maintenance project request to the maintenance merchant's first response after n4 data collections for the j-th maintenance merchant. i After taking the logarithm and standardizing, the response time index η(t) is obtained. Simultaneously, the number of orders (nd) completed on time by the repair shop within the promised construction time for the n4 maintenance projects is calculated to obtain the construction completion rate η(nd), where η(nd) = nd / n4; The one-way driving time (tg) from the car user to the repair shop in the n4 maintenance projects is also calculated. i The service efficiency attenuation coefficient η(tg) is obtained. Finally, the price comparison time cost assessment model P2 is obtained, P2=η(t)+η(nd)+η(tg); S3: Adaptive weight adjustment is performed on the price comparison evaluation model, the time cost comparison evaluation model, and the maintenance quality comparison evaluation model to obtain the adaptive weight adjustment results of each model; S4: Based on the adaptive weight adjustment results, the multi-dimensional price comparison comprehensive score is calculated by integrating the price comparison evaluation model, the price comparison time cost evaluation model, and the price comparison repair quality evaluation model. The multi-dimensional price comparison comprehensive score is then corrected to generate a price comparison result with confidence interval, and the final price comparison result is presented to the car user's client. S5: Record the repair shop selection results of the car user client based on the final price comparison results, perform deviation analysis on the selected repair shop results, and optimize the car repair and maintenance price comparison evaluation process based on the deviation analysis results.

2. The method for comparative evaluation of automobile repair and maintenance prices based on multi-dimensional data fusion according to claim 1, characterized in that: In S2, a price comparison and evaluation model is constructed: Using a data platform for the automotive repair and maintenance industry, the price p collected from the j-th repair shop for the same maintenance project is analyzed n1 times, based on the i-th price collected from the j-th repair shop. j,i Obtain the market average price fluctuation coefficient η(p) for this maintenance and repair project. ,μ(p i () represents the average market price for this maintenance and repair item at the time of the i-th data collection. Then, the price pp for the j-th repair shop in the n2th iteration is collected for the i-th iteration of the price collection for the parts required to be replaced in the maintenance project. j,i The average market price μ(pp) corresponding to the required replacement parts i ), to obtain the parts price fluctuation coefficient η(pp); then collect the j-th repair shop's ith labor cost moy for the maintenance project n3 times. j,i Compared to industry benchmark hourly rates j,0 The difference coefficient η(moy) is obtained; finally, the price comparison evaluation model P1 is obtained, P1=exp-[η(p)+η(pp)+η(moy)].

3. The method for comparative evaluation of automobile repair and maintenance prices based on multi-dimensional data fusion according to claim 1, characterized in that: In S2, a price comparison repair quality assessment model is constructed: The ratio of the number of industry certifications (nh) held by the technicians of the j-th repair shop to the total number of technicians (T_nh) is collected to obtain the technical certification coverage rate η(nh); the set of source types CT for the replacement parts required for the j-th repair shop in the maintenance project is collected n2 times, CT=[ct1,ct2,...,ctm], where m is the number of source types of replacement parts, and ctm is the source of the m-th part; then, the set of part quantities n_CT for the m-th part sources is calculated, n_CT=[n_ct1,n_ct2,...,n_ctm], where n_ctm is the number of parts from the m-th part source, to obtain the original equipment manufacturer (OEM) part compatibility rate η(CT). , n_ctJ is the quantity of original parts; through the repair shop's digital management system, the execution record of the i-th process step in the n5 complete maintenance process is collected and compared with the standard maintenance process specification to obtain the maintenance process specification coefficient η(DP); finally, the price comparison repair quality evaluation model P3 is obtained, P3=η(nh)+η(CT)+η(DP).

4. The method for comparative evaluation of automobile repair and maintenance prices based on multi-dimensional data fusion according to claim 1, characterized in that: The adaptive weight adjustment results of each model obtained in S3 include: basic optimization of model weights: within a preset evaluation period, the weight W of the I-th model is first obtained through big data analysis technology. I , W I，0 P represents the base weights of the i-th model, α is the weight adjustment coefficient, and its value is [0,1]. I P I ,avg、P I ,max and P I ,min represents the current value, historical average value, historical maximum value, and historical minimum value of the I-th model, respectively. I=1 is the price comparison evaluation model, I=2 is the price comparison time cost evaluation model, and I=3 is the price comparison maintenance quality evaluation model.

5. The method for comparative evaluation of automobile repair and maintenance prices based on multi-dimensional data fusion according to claim 1, characterized in that: The adaptive weight adjustment results obtained in S3 for each model also include: deep optimization of model weights: within a preset evaluation period, based on the maintenance requests initiated by car users, the proportion of orders in the car user's historical orders that selected the lowest price option is retrieved as the price comparison evaluation model preference strength Q1. Simultaneously, the proportion of orders in which car users selected original parts is retrieved as the price comparison repair quality evaluation model preference strength Q3. Furthermore, through natural language processing technology, the proportion of orders in which car users inquired about time through the client interaction platform is obtained as the price comparison time cost evaluation model preference strength Q2. This yields the deeply optimized I-th model weight W. I use , W I Let Q be the weight of the i-th model. I Let β be the preference strength of the i-th model. I Let be the preference coefficient of the i-th model. W I，max This is the preset upper limit for the weight of the I-th model; finally, the adaptive weight adjustment results for each model are obtained, including the weight W1 of the price comparison evaluation model. use Price comparison time cost assessment model weight W2 use Price comparison repair quality assessment model weight W3 use .

6. The method for comparative evaluation of automobile repair and maintenance prices based on multi-dimensional data fusion according to claim 1, characterized in that: The final price comparison result obtained in S4 includes: S4.1: Calculate the multi-dimensional price comparison comprehensive score: Based on the adaptive weight adjustment results, the multi-dimensional price comparison comprehensive score S of the j-th repair shop is calculated by integrating the price comparison evaluation model, the time cost comparison evaluation model, and the repair quality comparison evaluation model. j , P I For the i-th evaluation model value, W I use This represents the I-th model weight for deep optimization.

7. The method for comparative evaluation of automobile repair and maintenance prices based on multi-dimensional data fusion according to claim 1, characterized in that: The final price comparison result obtained in S4 also includes: S4.2: Within the preset evaluation period, based on the number of service orders for the j-th repair merchant, adjust the multi-dimensional price comparison comprehensive score to generate the price comparison result S for the j-th repair merchant with a confidence interval. j u , S j For the j-th repair shop, a comprehensive score is given based on multi-dimensional price comparison. j 、N0、N min and N max These represent the number of service orders for the j-th repair shop, the industry benchmark number of service orders, the minimum number of orders in the industry, and the maximum number of orders in the industry, respectively; finally, the final price comparison result S for the j-th repair shop is obtained. j u ; S4.3: Within the preset evaluation period, based on the final price comparison result S of the j-th repair shop. j u The system iterates through price comparison results from various repair shops offering car maintenance and repair services, and then outputs the results via a visualization function r. j The standardized price comparison results are presented in chart form, sorted from largest to smallest, to the car user's client. r j For the output price comparison result of the j-th repair shop, min(S) j u ) and max(S j u S represents the price comparison results from various repair shops. j u The minimum and maximum values, R(S) j u S represents the price comparison result. j u The preset upper limit value.

8. The method for comparative evaluation of automobile repair and maintenance prices based on multi-dimensional data fusion according to claim 1, characterized in that: The S5 implementation includes the following steps: S5.1: Within the preset evaluation period, record the repair shop finally selected by the user, compare the ranking results of each repair shop obtained by the car repair and maintenance price comparison evaluation result presentation module, filter out U repair shops whose price comparison results are not the largest, and calculate the selection deviation coefficient η(de), η(de)=U / TU, where TU is the total number of car user maintenance project requests; S5.2: Compare the calculated selection deviation coefficient η(de) with the threshold. If it is less than the threshold, it indicates that the car repair and maintenance price comparison assessment is good; otherwise, the car repair and maintenance price comparison assessment process is optimized.

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