Remote diagnosis cost estimation method and device, electronic equipment and storage medium

Abstract

The application discloses a remote diagnosis cost estimation method and device, electronic equipment and a storage medium. The method comprises the following steps: firstly, n remote diagnosis devices carried on a target diagnosis platform are acquired; then, a target remote diagnosis device required when a target vehicle performs remote diagnosis is determined from the n remote diagnosis devices, so that the target remote diagnosis device is obtained; finally, a target remote diagnosis cost required when the target vehicle performs remote diagnosis through the target remote diagnosis device is determined, so that the target remote diagnosis cost is obtained. According to the embodiment of the application, the cost required when the vehicle performs remote diagnosis can be accurately estimated.

Description

Technical Field

[0001] This invention relates to the field of vehicle remote diagnostics technology, and in particular to a method, apparatus, electronic device, and storage medium for estimating remote diagnostic costs. Background Technology

[0002] With the continuous improvement of automotive electronics and intelligence, remote vehicle diagnostic technology is constantly developing. Remote vehicle diagnostic services mainly connect suppliers with professional diagnostic equipment and customers with diagnostic needs through diagnostic platforms. This connection model breaks the geographical limitations of traditional offline diagnostics and provides customers with a convenient and efficient diagnostic service channel. However, current remote diagnostic services are unable to quickly and accurately match the optimal diagnostic equipment for the target vehicle, which leads to the inability to accurately estimate the cost of remote vehicle diagnostics. Therefore, how to accurately estimate the cost of remote vehicle diagnostics is an urgent problem to be solved. Summary of the Invention

[0003] This application provides a method, apparatus, electronic device, and storage medium for estimating the cost of remote diagnostics, which can accurately estimate the cost required for remote diagnostics of a vehicle.

[0004] In a first aspect, embodiments of this application provide a method for estimating the cost of remote diagnostics, including: Obtain the n remote diagnostic devices mounted on the target diagnostic platform; n is an integer greater than 1; Determine the remote diagnostic device required for remote diagnostics of the target vehicle from among the n remote diagnostic devices, and obtain the target remote diagnostic device; Determine the remote diagnostic cost required to perform remote diagnostics on the target vehicle using the target remote diagnostic device, and obtain the target remote diagnostic cost.

[0005] Secondly, embodiments of this application provide a remote diagnostic cost estimation device, the device comprising: an acquisition unit and a processing unit; The acquisition unit is used to acquire n remote diagnostic devices mounted on the target diagnostic platform; n is an integer greater than 1. The processing unit is used to determine the remote diagnostic device required for the target vehicle to perform remote diagnostics among the n remote diagnostic devices, and to obtain the target remote diagnostic device. Determine the remote diagnostic cost required to perform remote diagnostics on the target vehicle using the target remote diagnostic device, and obtain the target remote diagnostic cost.

[0006] Thirdly, embodiments of the present invention provide an electronic device, including: a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor to cause the electronic device to perform the method as described in the first aspect.

[0007] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program that is executed by a processor to implement the method as described in the first aspect.

[0008] Fifthly, embodiments of the present invention provide a computer program product including a non-transitory computer-readable storage medium storing a computer program, such that a computer performs the method as described in the first aspect.

[0009] Implementing the embodiments of the present invention has the following beneficial effects: As can be seen, the remote diagnostic cost estimation method described in the embodiments of the present invention first obtains n remote diagnostic devices mounted on the target diagnostic platform, then determines the remote diagnostic device required for remote diagnostics of the target vehicle among the n remote diagnostic devices, thus obtaining the target remote diagnostic device, and finally determines the remote diagnostic cost required for remote diagnostics of the target vehicle through the target remote diagnostic device, thus obtaining the target remote diagnostic cost, thereby accurately estimating the cost required for remote diagnostics of the vehicle. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application or the background art, the accompanying drawings used in the embodiments of this application or the background art will be described below.

[0011] Figure 1 This is a schematic diagram of the structure of a remote diagnostic cost estimation system provided in the embodiments of this application; Figure 2 This is a flowchart of a remote diagnostic cost estimation method provided in the embodiments of this application; Figure 3 This is a flowchart illustrating how to determine the cost of a target remote diagnostic procedure, as provided in an embodiment of this application. Figure 4 This is a flowchart of a method for determining the target diagnostic platform's commission adjustment coefficient, provided in an embodiment of this application. Figure 5 This is a flowchart illustrating a method for determining a target remote diagnostic device according to an embodiment of this application; Figure 6 This is a flowchart illustrating how to determine the complexity value of a target diagnosis, as provided in an embodiment of this application. Figure 7This is a schematic diagram of the structure of a remote diagnostic cost estimation device provided in an embodiment of this application; Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0012] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present application.

[0013] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0014] In this document, the term "implementation" means that a specific feature, structure, or characteristic described in connection with an implementation may be included in at least one implementation of this application. The appearance of this phrase in various places in the specification does not necessarily refer to the same implementation, nor is it a separate or alternative implementation mutually exclusive with other implementations. It will be explicitly and implicitly understood by those skilled in the art that the implementations described herein can be combined with other implementations.

[0015] Please see Figure 1 , Figure 1 This is a schematic diagram of a remote diagnostic cost estimation system provided in an embodiment of this application. The remote diagnostic cost estimation system 10 includes a target diagnostic platform 101 and a target vehicle 102. The target diagnostic platform 101 is equipped with n remote diagnostic devices.

[0016] In this embodiment, the remote diagnostic cost estimation system 10 uses the target diagnostic platform 101 and the target vehicle 102 as the core interaction objects. Relying on preset equipment matching rules and cost calculation models, it can achieve fully automated processing from diagnostic equipment selection to cost result output. When the remote diagnostic cost estimation system 10 runs, it first obtains the basic information, functional attributes, and billing standards of the n remote diagnostic devices mounted on the target diagnostic platform 101. Then, based on the specific needs of the target vehicle 102, such as vehicle model, fault type, and diagnostic items, it selects the most suitable target remote diagnostic device from the equipment pool. Finally, it calculates the target remote diagnostic cost by combining parameters such as the basic rate, diagnostic time, and service value of the device.

[0017] The target diagnostic platform 101 is the core hardware and software carrier of the remote diagnostic cost estimation system 10. It is a comprehensive service platform supporting n remote diagnostic devices, possessing multiple functions including device management, diagnostic task scheduling, data storage, and communication interaction. The remote diagnostic devices integrated into the target diagnostic platform 101 cover various models and functional types, adapting to the remote diagnostic needs of different brands and vehicle models, including dedicated diagnostic devices for engine faults and general diagnostic devices for vehicle electronic systems. The target diagnostic platform 101 has a built-in device status monitoring module that can update the online status load rate and functional availability of each remote diagnostic device in real time, providing reliable data support for the system to select target remote diagnostic devices. The target diagnostic platform 101 also stores a comprehensive device billing rule database, covering core information such as the basic charging standards and differentiated pricing conditions for each device. This database can be linked in real time with the cost calculation module of the remote diagnostic cost estimation system 10 to ensure the accuracy of cost estimation. Furthermore, the platform is equipped with a standardized communication interface, enabling stable connections with the target vehicle 102 and user terminals to complete interactive operations such as issuing diagnostic commands, uploading vehicle data, and pushing cost results.

[0018] Target vehicle 102 is the service object of the remote diagnostic cost estimation system 10. It is a motor vehicle that requires remote diagnostics, and its diagnostic needs are the core prerequisite for triggering the system's operation. Target vehicle 102 is equipped with an on-board terminal that supports remote data transmission, including on-board equipment and vehicle-mounted diagnostic interface modules. It can establish a communication connection with the remote diagnostic equipment on the target diagnostic platform 101 and upload key information such as real-time operating data, fault codes, and vehicle parameters. The specific diagnostic needs of target vehicle 102 are diverse, covering different types of projects such as engine fault diagnosis, transmission status detection, and in-vehicle entertainment system debugging. These needs are the core basis for the system to select target remote diagnostic equipment. In the cost estimation process, attributes such as the model configuration and fault complexity of target vehicle 102 will directly affect the equipment matching results and the final cost calculation. For example, the dedicated diagnostic needs of high-end models require matching with more specialized remote diagnostic equipment, and the corresponding diagnostic costs will be reasonably adjusted according to the equipment costs.

[0019] In this embodiment, firstly, n remote diagnostic devices mounted on the target diagnostic platform 101 are obtained. Then, the remote diagnostic device required for remote diagnostics of the target vehicle 102 among the n remote diagnostic devices is determined, thus obtaining the target remote diagnostic device. Finally, the remote diagnostic cost required for remote diagnostics of the target vehicle 102 through the target remote diagnostic device is determined, thus obtaining the target remote diagnostic cost. This allows for an accurate estimation of the cost required for remote diagnostics of the vehicle.

[0020] Please see Figure 2 , Figure 2 This is a flowchart of a remote diagnostic cost estimation method provided in the embodiments of this application, including but not limited to the following steps: S201: Obtain the n remote diagnostic devices mounted on the target diagnostic platform.

[0021] In this implementation, n is an integer greater than 1. The n remote diagnostic devices mounted on the target diagnostic platform constitute a diversified hardware cluster supporting vehicle remote diagnostic services. These remote diagnostic devices cover the diagnostic needs of vehicles with varying degrees of fault complexity. Each remote diagnostic device has built-in targeted diagnostic algorithms and communication adaptation capabilities, enabling it to independently complete remote diagnostic work. In actual remote diagnostic operations, in most cases, only one device that precisely corresponds to the fault information of the target vehicle is needed to complete the entire diagnostic process from fault data collection to problem localization. At the same time, the configuration of multiple devices can also meet the collaborative diagnostic needs in special and complex fault scenarios. Each device is also associated with clear billing standards and operating status parameters. This information is synchronized to the remote diagnostic cost estimation system in real time, providing accurate and reliable basic data support for subsequent selection of target remote diagnostic devices and calculation of diagnostic costs. The deployment of multiple devices also ensures the comprehensiveness and fault tolerance of the platform's diagnostic services.

[0022] S202: Determine the remote diagnostic device required for remote diagnostics of the target vehicle among the n remote diagnostic devices, and obtain the target remote diagnostic device.

[0023] In this embodiment, determining the remote diagnostic device required for remote diagnosis of the target vehicle among the n remote diagnostic devices, and obtaining the target remote diagnostic device, is a precise screening process based on matching fault information with diagnostic complexity levels. First, fault information of the target vehicle is obtained, including key information such as vehicle fault manifestations, fault codes, and abnormal vehicle operating parameters. Then, this fault information is quantitatively analyzed according to preset complexity assessment rules to calculate the target diagnostic complexity value corresponding to the target vehicle. Next, the target diagnostic complexity value is mapped to a specific target diagnostic complexity level according to a numerical range division standard. Subsequently, a pre-constructed mapping table between diagnostic complexity levels and remote diagnostic devices is retrieved. This mapping table covers n remote diagnostic devices on the target diagnostic platform and follows a matching logic where each diagnostic complexity level corresponds to one remote diagnostic device. Finally, the target diagnostic complexity level is compared with the mapping table to filter out the remote diagnostic device that precisely matches that level, which is the target remote diagnostic device.

[0024] S203: Determine the remote diagnostic cost required to perform remote diagnostics on the target vehicle using the target remote diagnostic device, and obtain the target remote diagnostic cost.

[0025] In this embodiment, determining the remote diagnostic cost required to remotely diagnose the target vehicle using the target remote diagnostic device is a dynamic calculation process that combines the equipment's basic pricing platform commission rules with the actual cost and performance parameters of the equipment. First, the preset basic diagnostic cost corresponding to the target remote diagnostic device and the preset platform commission rate corresponding to the target diagnostic platform can be clearly defined. These two data points serve as the basis for cost calculation. Then, the first purchase cost corresponding to the target remote diagnostic device and the first diagnostic success rate of the device within a historical time period are obtained. The first purchase cost reflects the initial investment in the equipment, and the first diagnostic success rate reflects the actual diagnostic efficiency of the equipment. Finally, based on the first purchase cost... The target diagnostic platform commission adjustment coefficient is calculated based on the first diagnostic success rate. The purchase cost and the diagnostic success rate directly affect the value of the adjustment coefficient, thus adjusting the platform commission ratio. Then, the target diagnostic platform commission adjustment coefficient is used to correct the preset platform commission ratio to obtain the target platform commission ratio adapted to the device and diagnostic scenario. Next, the specific platform commission fee is calculated based on the target platform commission ratio and the preset basic diagnostic fee. Finally, the platform commission fee is used to adjust the preset basic diagnostic fee to obtain the final target remote diagnostic fee. The entire calculation process takes into account both the standardization of basic pricing and the differences in actual equipment parameters to ensure that the fee result is accurate and reasonable.

[0026] As can be seen, firstly, by acquiring n remote diagnostic devices on the target diagnostic platform, the platform's device resource pool can be fully understood, providing sufficient data support for subsequent device selection and avoiding adaptation errors caused by incomplete device information. Simultaneously, the reserve of multiple devices can meet the diagnostic needs of different vehicle models and different fault types, ensuring service coverage. Secondly, selecting target remote diagnostic devices based on the target vehicle's fault information and diagnostic complexity level enables precise matching between devices and diagnostic needs. In most scenarios, relying on a single compatible device to complete the diagnostic task reduces redundant device resource usage, lowers device coordination costs during the diagnostic process, and improves the targeting and accuracy of fault diagnosis, avoiding diagnostic failures due to device incompatibility. Finally, calculating the target remote diagnostic fee by combining parameters such as basic device costs, platform commission rates, device purchase costs, and diagnostic success rates can break the ambiguity of traditional pricing, making the fee structure more reasonable and scientific. Users can clearly understand the basis for fee calculation, reducing fee disputes, and the platform can also achieve a balance between costs and benefits by dynamically adjusting the commission rate.

[0027] Please see Figure 3 , Figure 3 This is a flowchart illustrating the determination of target remote diagnostic costs according to an embodiment of this application, including but not limited to the following steps: S301: Determine the preset basic diagnostic fee corresponding to the target remote diagnostic device and the preset platform commission rate corresponding to the target diagnostic platform.

[0028] In this embodiment, the preset basic diagnostic fee is a pre-set benchmark charge for a single diagnostic service for the target remote diagnostic equipment, and it forms the core basis for calculating the overall remote diagnostic cost. The pricing of the preset basic diagnostic fee is based on multiple factors, including the hardware purchase cost of the equipment, daily operation and maintenance costs, technology research and development investment costs, and the cost of manual technical support for a single diagnostic session. It also takes into account the pricing standards of similar diagnostic equipment in the industry, market supply and demand, and the scarcity of the equipment's functions. Different types of remote diagnostic equipment correspond to different preset basic diagnostic fees. For example, dedicated diagnostic equipment for detecting core engine faults has a higher preset basic diagnostic fee than general diagnostic equipment for debugging in-vehicle entertainment systems due to its high technical threshold and maintenance costs.

[0029] The preset platform commission rate is a fixed percentage of revenue that the target diagnostic platform takes from each diagnostic service service it provides, including equipment hosting, data interaction, service integration, and user management support services. The preset commission rate is determined based on factors such as the platform's operational and management costs, technical maintenance costs, marketing costs, and risk-bearing costs, and is also adjusted reasonably based on the average revenue sharing rates of platforms within the industry. This preset commission rate serves as the basic standard for the platform's participation in fee sharing and applies to all remote diagnostic devices on the platform. Until dynamic adjustments are made, the platform's commission fee for all diagnostic services can be calculated according to this rate.

[0030] Determining the preset basic diagnostic fee for the target remote diagnostic device and the preset platform commission rate for the target diagnostic platform requires comprehensive calculation based on multiple factors, including the device's total lifecycle cost, industry market pricing levels, platform operating costs, and service value. Specifically, for the preset basic diagnostic fee, core expenditure items such as the hardware purchase cost, daily operation and maintenance cost, technology upgrade and iteration cost, and manual technical support cost per diagnosis for the target remote diagnostic device should be identified. Then, the market benchmark pricing standards for similar diagnostic devices should be referenced, and adjustments should be made based on the device's functional scarcity, diagnostic accuracy, and other differentiated advantages to form a basic pricing that balances cost recovery and market competitiveness.

[0031] To determine the target diagnostic platform's revenue sharing ratio, it is necessary to calculate the platform's operating expenses, including equipment hosting costs, data interaction system maintenance costs, user service and management costs, and marketing costs. At the same time, it is necessary to refer to the revenue sharing ratio range of similar platforms in the industry, and combine the additional service value provided by the platform to diagnostic equipment, such as resource integration and risk protection, in order to determine a fixed ratio that can cover the platform's operating costs and attract equipment to join.

[0032] S302: Obtain the first purchase cost of the target remote diagnostic device and the first diagnostic success rate of the target remote diagnostic device within a historical time period.

[0033] In this embodiment, the first purchase cost corresponding to the target remote diagnostic equipment refers to the total initial capital cost invested by the target diagnostic platform in purchasing the target remote diagnostic equipment. It is a core indicator for measuring the hardware value of the equipment and the platform's initial investment. The first purchase cost includes not only the ex-factory price of the equipment itself, but also logistics costs incurred during equipment transportation, technical service costs during the installation and commissioning phase, software integration costs for adapting to the platform system, and related acceptance and testing costs. It is a one-time fixed expenditure incurred by the platform before the equipment is put into use. The level of the first purchase cost is usually directly related to the functional complexity, technological advancement, and brand reliability of the equipment. For example, the first purchase cost of a dedicated diagnostic equipment with multi-vehicle compatibility and high-precision fault location functions is much higher than that of a general-purpose diagnostic equipment with a single function. This cost indicator will also serve as a core parameter in the calculation of the target diagnostic platform's commission adjustment coefficient, thereby affecting the final pricing of remote diagnostic fees.

[0034] The first diagnostic success rate of the target remote diagnostic equipment within a historical time period refers to the ratio of the number of times the target remote diagnostic equipment successfully completes fault data collection, fault cause location, and provides an effective solution in a single instance for remote diagnostic needs of vehicles connected to the platform within a set historical statistical period, to the total number of diagnostic tasks during the same period. The first diagnostic success rate is a key quantitative criterion for measuring the equipment's diagnostic efficiency, technical reliability, and practicality. During the statistical process, diagnostic failures caused by non-equipment factors such as abnormal data transmission at the vehicle end or user operational errors are excluded to ensure that the data accurately reflects the equipment's performance level. A higher first diagnostic success rate indicates stronger adaptability and accuracy in diagnosing various vehicle faults, enabling the equipment to provide more efficient diagnostic services to users. This indicator is linked to the initial purchase cost, jointly determining the value of the target diagnostic platform's commission adjustment coefficient, thereby achieving a precise match between diagnostic fees and the actual service value of the equipment.

[0035] S303: Determine the target diagnostic platform commission adjustment coefficient corresponding to the target remote diagnostic device based on the first purchase cost and the first diagnostic success rate.

[0036] In this embodiment, a first mapping relationship between the pre-constructed purchase cost and the diagnostic platform commission adjustment coefficient, and a second mapping relationship between the diagnostic success rate and the diagnostic platform commission adjustment coefficient can be obtained first. The first and second mapping relationships are quantitative correspondence rules established based on a large amount of historical data and cost-benefit models, which can reflect the influence weight of purchase cost and diagnostic success rate on the commission adjustment coefficient. Then, the first purchase cost of the target remote diagnostic device is substituted into the first mapping relationship to match and calculate the corresponding first diagnostic platform commission adjustment coefficient. At the same time, the first diagnostic success rate of the device is substituted into the second mapping relationship to match and calculate the corresponding second diagnostic platform commission adjustment coefficient. Finally, the first diagnostic platform commission adjustment coefficient and the second diagnostic platform commission adjustment coefficient are weighted and calculated according to a preset integration algorithm to obtain the target diagnostic platform commission adjustment coefficient that can comprehensively reflect the dual factors of equipment purchase cost and diagnostic success rate.

[0037] S304: Adjust the preset platform commission ratio based on the target diagnostic platform commission adjustment coefficient to obtain the target platform commission ratio.

[0038] In this embodiment, the commission rate of the target platform is calculated according to the following formula: Target platform commission rate = Preset platform commission rate × Target diagnostic platform commission adjustment coefficient; According to the above formula, the preset platform commission rate can be adjusted based on the target diagnostic platform commission adjustment coefficient to obtain the target platform commission rate.

[0039] It should be explained that, based on the equipment type, purchase cost, service life, functional level, historical diagnostic success rate, and other attribute information stored in the equipment database, a device value score can be calculated according to preset weights. Then, a pre-set platform benchmark commission rate and value commission adjustment coefficient can be retrieved, and the target platform commission rate can be determined based on the device value score. Alternatively, the target platform commission rate can be calculated using the following formula: The target platform commission rate = platform base commission rate - value commission adjustment coefficient × (equipment value score - 50) / 50, where the platform base commission rate can be preset to 20% and the value commission adjustment coefficient can be preset to 10%. The calculation process must follow the rule that the higher the score, the lower the commission rate. At the same time, a minimum threshold of 5% is set for the commission rate. Finally, the target platform commission rate adapted to the target remote diagnostic equipment is obtained through this dynamic adjustment method.

[0040] S305: Determine the platform commission fee based on the target platform commission ratio and the preset basic diagnostic fee.

[0041] In this embodiment, the platform commission fee is calculated according to the following formula: Platform commission fee = Preset basic diagnostic fee × (1 - target platform commission rate); The platform commission fee can be determined based on the target platform's commission rate and the preset basic diagnostic fee, according to the above formula.

[0042] It should be explained that, based on the equipment type, purchase cost, service life, functional level, historical diagnostic success rate, and other attribute information in the equipment database, a device value score can be calculated according to preset weights for basic attribute scores, functional level scores, performance scores, and depreciation coefficients. The device value score is then substituted into the service fee calculation formula to calculate the final service fee. At the same time, it is substituted into the platform's actual commission rate calculation formula to calculate the target platform's commission rate for the device. Finally, the calculated service fee is multiplied by the target platform's commission rate to obtain the platform's commission fee for this remote diagnostic service. The entire calculation process strictly follows the rule that the higher the device value score, the higher the service fee and the lower the platform's commission rate, ensuring the rationality and fairness of the fee calculation.

[0043] It should be noted that this embodiment can also provide a remote diagnostic service pricing and settlement system based on equipment value quantification. First, an equipment database containing information such as equipment type, brand, model, purchase cost, service life, function level, calibration status, historical diagnostic success rate, etc. is established. Then, an equipment value quantification model is constructed according to preset weights and scoring rules. The equipment value score is output from 0 to 100 through a weighted calculation of basic attribute scores, function level scores, performance scores, and depreciation coefficients. Next, the service fee and the platform's actual commission rate are dynamically calculated by combining the equipment value score and preset parameters. The higher the score, the higher the service fee and the lower the platform commission rate. After that, the client submits information about the faulty vehicle to initiate a remote diagnostic request. The system matches the appropriate diagnostic equipment according to the vehicle brand and fault complexity and displays the relevant score and fee information. After the client selects the target equipment and confirms the order, the system establishes a connection between the client and the equipment owner to carry out remote diagnostic operations. After the diagnosis is completed, the platform commission is deducted from the dynamically calculated fee, and the remaining amount is settled to the equipment owner's account. Finally, the equipment attribute information, value score, and historical transaction data are stored, and the equipment value score is updated regularly.

[0044] S306: Adjust the preset basic diagnostic fee based on the platform's commission fee to obtain the target remote diagnostic fee.

[0045] In this embodiment, the target remote diagnostic fee is obtained by adjusting the preset basic diagnostic fee based on the platform commission fee. First, it must be clarified that the preset basic diagnostic fee is the benchmark price for the remote diagnostic service. Then, combined with the already calculated platform commission fee, an integrated calculation is performed according to the fee composition logic. Specifically, the preset basic diagnostic fee and the platform commission fee are added together, and the sum is the target remote diagnostic fee. This fee is the final amount the user needs to pay, covering both the basic cost of the device providing diagnostic services and the revenue sharing from the platform's support services such as device hosting, data interaction, and service integration. The entire adjustment process clearly reflects the constituent elements of the fee, ensuring that the calculation of the target remote diagnostic fee has a clear basis and is reasonable.

[0046] As can be seen, firstly, by determining the preset basic diagnostic fee and the preset platform commission rate, a standardized benchmark for fee accounting is provided, avoiding arbitrary pricing. Secondly, by introducing two core parameters—the initial purchase cost and the historical diagnostic success rate—and determining the commission adjustment coefficient accordingly, fee accounting can be linked to the actual investment and usage efficiency of the equipment. Equipment with high purchase costs and high diagnostic success rates can receive more reasonable commission rate adjustments, reflecting the differences in equipment value. Then, by adjusting the preset platform commission rate through the adjustment coefficient, the target platform commission rate is obtained, breaking the limitations of fixed commissions and achieving dynamic matching between platform revenue and equipment performance. Finally, the platform commission fee is calculated based on the target platform commission rate, and the preset basic diagnostic fee is adjusted to obtain the target remote diagnostic fee. This ensures that the final fee includes both the basic equipment service cost and the platform support service revenue, making the structure clear and transparent. This not only improves user acceptance of fees and reduces disputes but also ensures reasonable revenue for both the platform and equipment providers, promoting the standardization and sustainable development of remote diagnostic services.

[0047] Please see Figure 4 , Figure 4 This is a flowchart of determining the target diagnostic platform's commission adjustment coefficient according to an embodiment of this application, including but not limited to the following steps: S401: Obtain the first mapping relationship between the purchase cost and the diagnostic platform commission adjustment coefficient, and the second mapping relationship between the diagnostic success rate and the diagnostic platform commission adjustment coefficient.

[0048] In this embodiment, the first mapping relationship is a corresponding rule pre-constructed based on a large amount of equipment purchase cost data and the corresponding commission adjustment coefficient values. It can reflect the impact of purchase cost on commission adjustment coefficient. Generally, the higher the purchase cost, the larger the corresponding adjustment coefficient value.

[0049] The second mapping relationship is a corresponding rule pre-constructed based on a large amount of historical diagnostic success rate data of devices and the corresponding commission adjustment coefficient values. It can reflect the impact of the diagnostic success rate on the commission adjustment coefficient. Generally, the higher the diagnostic success rate, the larger the corresponding adjustment coefficient value.

[0050] S402: Based on the first mapping relationship, determine the diagnostic platform commission adjustment coefficient corresponding to the first purchase cost, and obtain the first diagnostic platform commission adjustment coefficient.

[0051] In this embodiment, since the first mapping relationship is a standardized correspondence rule pre-constructed by the platform based on a large amount of historical equipment purchase cost data and corresponding commission adjustment requirements, the higher the purchase cost, the larger the corresponding commission adjustment coefficient value. Then, the specific value of the first purchase cost of the target remote diagnostic equipment is substituted into the mapping relationship, and the commission adjustment coefficient value that completely corresponds to the purchase cost is located through precise numerical matching or interval classification matching. This coefficient, which reflects the adjustment strength of the purchase cost on the commission ratio, is the first diagnostic platform commission adjustment coefficient.

[0052] S403: Based on the second mapping relationship, determine the diagnostic platform commission adjustment coefficient corresponding to the first diagnostic success rate, and obtain the second diagnostic platform commission adjustment coefficient.

[0053] In this embodiment, since the second mapping relationship is a standardized correspondence rule pre-constructed by the platform based on a large amount of historical device diagnostic success rate data and corresponding commission adjustment requirements, the higher the diagnostic success rate, the larger the corresponding commission adjustment coefficient value. Then, the specific value of the first diagnostic success rate of the target remote diagnostic device is substituted into the mapping relationship. Through precise numerical matching or interval classification matching, the commission adjustment coefficient value that completely corresponds to the diagnostic success rate is located. This coefficient, which reflects the degree of adjustment of the commission ratio by the diagnostic success rate, is the second diagnostic platform commission adjustment coefficient.

[0054] S404: Determine the target diagnostic platform's commission adjustment coefficient based on the first diagnostic platform's commission adjustment coefficient and the second diagnostic platform's commission adjustment coefficient.

[0055] In this embodiment, determining the target diagnostic platform commission adjustment coefficient based on the first and second diagnostic platform commission adjustment coefficients is a multi-dimensional quantitative calculation process that first weights and integrates basic factors and then optimizes based on user feedback. Specifically, first, a first weight and a second weight corresponding to the two adjustment coefficients are determined, ensuring that the sum of the two weights is 1. Then, the first diagnostic platform commission adjustment coefficient is multiplied by the first weight, and the second diagnostic platform commission adjustment coefficient is multiplied by the second weight. The results of the two multiplications are added together to obtain the reference diagnostic platform commission adjustment coefficient. Next, the user satisfaction rate corresponding to the target remote diagnostic device is obtained, and then an adjustment parameter matching the satisfaction rate is determined based on preset rules. Finally, the reference diagnostic platform commission adjustment coefficient is corrected using the adjustment parameter according to a preset algorithm, ultimately obtaining the target diagnostic platform commission adjustment coefficient that integrates the three factors of purchase cost, diagnostic success rate, and user satisfaction rate.

[0056] For example, a first weight corresponding to the commission adjustment coefficient of the first diagnostic platform and a second weight corresponding to the commission adjustment coefficient of the second diagnostic platform are determined. Specifically, the sum of the first weight and the second weight is 1. The first weight corresponds to the commission adjustment coefficient of the first diagnostic platform, and its magnitude reflects the importance of equipment purchase cost to the commission ratio adjustment. The second weight corresponds to the commission adjustment coefficient of the second diagnostic platform, and its magnitude reflects the importance of the equipment's historical diagnostic success rate to the commission ratio adjustment. Setting the sum of the first weight and the second weight to 1 is to ensure that the influence ratio of the two factors is on the same quantitative dimension, avoiding double counting or weight imbalance. The basis for weight allocation is usually the platform's operation strategy, industry data, and cost-benefit model. For example, if the platform attaches more importance to the diagnostic efficiency of the equipment, the second weight can be set to a higher value; if it attaches more importance to the hardware investment cost of the equipment, the first weight can be set to a higher value.

[0057] For example, a reference diagnostic platform commission adjustment coefficient is determined based on the first diagnostic platform commission adjustment coefficient, the second diagnostic platform commission adjustment coefficient, the first weight, and the second weight. Specifically, according to a preset weighted summation algorithm, the first diagnostic platform commission adjustment coefficient is multiplied by the first weight, and then the second diagnostic platform commission adjustment coefficient is multiplied by the second weight. The results of the two multiplications are then added together, and the final value is the reference diagnostic platform commission adjustment coefficient. This coefficient integrates the influence of two core factors: purchase cost and diagnostic success rate. It is the basic value for further optimization of the adjustment coefficient by combining other parameters. Its calculation process ensures that the influence of the two factors is accurately matched with the preset weights. Specifically, the reference diagnostic platform commission adjustment coefficient is calculated according to the following formula: Reference diagnostic platform commission adjustment coefficient = First diagnostic platform commission adjustment coefficient × First weight + Second diagnostic platform commission adjustment coefficient × Second weight; Based on the above formula, the reference diagnostic platform commission adjustment coefficient can be determined based on the first diagnostic platform commission adjustment coefficient, the second diagnostic platform commission adjustment coefficient, the first weight, and the second weight.

[0058] For example, the user satisfaction rate of the target remote diagnostic device is obtained. Specifically, the user satisfaction rate is a direct reflection of the actual service quality and user satisfaction of the device. The higher the user satisfaction rate of the device, the more efficient and accurate the service experience can be provided to users during remote diagnosis. Based on this positive market feedback, the platform will further optimize the value of the target diagnostic platform commission adjustment coefficient by integrating the adjustment coefficients corresponding to the purchase cost and the diagnostic success rate. This will incentivize the device owner to improve service quality. For devices with low user satisfaction rates, the platform will correspondingly weaken the optimization range of their adjustment coefficients. In this way, the commission adjustment coefficient not only reflects the hardware cost and technical performance of the device, but also takes into account the actual user experience, achieving a precise match between the platform's commission ratio and the overall value of the device. Therefore, it is necessary to first obtain the user satisfaction rate of the target remote diagnostic device.

[0059] For example, the adjustment parameters corresponding to the user approval rating are determined. Specifically, this can be a preset mapping relationship between the user approval rating and the adjustment parameters. Based on this mapping relationship, the adjustment parameters corresponding to the user approval rating can be determined.

[0060] For example, the revenue adjustment coefficient of the reference diagnostic platform is adjusted based on the adjustment parameters to obtain the revenue adjustment coefficient of the target diagnostic platform. Specifically, the revenue adjustment coefficient of the target diagnostic platform is calculated according to the following formula: Target diagnostic platform commission adjustment coefficient = Reference diagnostic platform commission adjustment coefficient × (1 + adjustment parameter); Based on the above formula, the reference diagnostic platform's commission adjustment coefficient can be adjusted according to the adjustment parameters to obtain the target diagnostic platform's commission adjustment coefficient.

[0061] As can be seen, firstly, by assigning weights to the commission adjustment coefficients of the first and second diagnostic platforms, with the sum of these weights being 1, the influence ratio of the two core factors—equipment purchase cost and historical diagnostic success rate—can be accurately allocated. This avoids adjustment bias caused by a single factor dominating, ensuring the scientific rigor of the calculation of the reference diagnostic platform commission adjustment coefficient. Secondly, by introducing the market feedback indicator of user satisfaction rate and matching it with corresponding adjustment parameters, the determination of the adjustment coefficient is no longer limited to the hardware and performance dimensions of the equipment itself, but extends to the actual service experience of users. This effectively incentivizes equipment owners to improve service quality. Finally, by combining the reference adjustment coefficient with the adjustment parameters, the final target diagnostic platform commission adjustment coefficient is obtained, achieving a fusion of equipment cost, technical efficiency, and user satisfaction. This ensures that the subsequent platform commission ratio adjusted based on the target diagnostic platform commission adjustment coefficient better matches the comprehensive value of the equipment, thereby balancing the interests of the platform, equipment owners, and users, and ensuring the standardization of remote diagnostic services.

[0062] Please see Figure 5 , Figure 5 This is a flowchart illustrating the process of determining a target remote diagnostic device according to an embodiment of this application, including but not limited to the following steps: S501: Obtain the fault information of the target vehicle.

[0063] In this embodiment, the fault information of the target vehicle includes key information such as the vehicle's brand and model, the specific manifestation of the fault, the operating status when the fault occurred, and the fault code. This fault information is the basis for judging the type of vehicle fault and the difficulty of diagnosis. The fault information can be obtained by the requester actively submitting it or by the remote diagnostic cost estimation system automatically reading it through preliminary data interaction. Comprehensive and accurate fault information can provide reliable support for the subsequent determination of the complexity of diagnosis and avoid equipment matching errors caused by incomplete information.

[0064] S502: Determine the target diagnostic complexity value corresponding to the target vehicle based on the fault information.

[0065] In this embodiment, determining the target diagnostic complexity value for the target vehicle based on the fault information is a quantitative evaluation process that combines the scope of the fault and the pattern of fault occurrence. Specifically, two core indicators are first extracted from the fault information of the target vehicle: the number of fault-related components and the frequency of fault occurrence within a preset time period. Then, according to preset evaluation rules, a first diagnostic complexity value is matched to the number of fault-related components. The more components there are, the more complex the system involved in the fault, and the higher the corresponding first diagnostic complexity value. At the same time, a second diagnostic complexity value is matched to the frequency of fault occurrence. The more frequent the fault occurs, the more complex the cause of the fault may be or the existence of hidden problems, and the higher the corresponding second diagnostic complexity value will be. Finally, the first and second diagnostic complexity values ​​are calculated according to a preset integration algorithm to obtain a target diagnostic complexity value that comprehensively reflects the difficulty of diagnosing the vehicle's fault.

[0066] S503: Determine the target diagnostic complexity level corresponding to the target vehicle based on the target diagnostic complexity value.

[0067] In this implementation, the platform can first pre-set a classification system for diagnostic complexity levels, and at the same time match a corresponding complexity value range for each level. The range of different ranges will be scientifically defined based on factors such as the distribution of diagnostic difficulty of common faults and the coverage of the equipment's diagnostic capabilities. For example, a small numerical range can be set for low complexity level, a medium numerical range for medium complexity level, and a large numerical range for high complexity level. Then, the calculated target diagnostic complexity value is substituted into the range classification rule. By accurately comparing the value with the range, the specific range to which the value belongs can be found, thereby determining the target diagnostic complexity level corresponding to the vehicle.

[0068] S504: Obtain the mapping table between diagnostic complexity levels and remote diagnostic devices.

[0069] In this embodiment, the mapping table includes the n remote diagnostic devices, with each diagnostic complexity level corresponding to one remote diagnostic device.

[0070] Obtaining a mapping table between diagnostic complexity levels and remote diagnostic devices is a preparatory step to build a precise matching bridge between vehicle fault diagnosis needs and suitable diagnostic equipment. Specifically, the platform pre-sorts all attribute information of the n remote diagnostic devices under its management, including core parameters such as the diagnostic function coverage, technical accuracy level, applicable fault types, and historical diagnostic success rate of each device. Then, combined with the defined diagnostic complexity level system, based on the technical threshold and functional requirements of different levels of diagnostic needs, a remote diagnostic device that best meets the needs of that level is matched for each diagnostic complexity level. Low complexity levels are usually matched with basic diagnostic devices with general functions, while high complexity levels are matched with advanced specialized diagnostic devices. Finally, all diagnostic complexity levels and their corresponding remote diagnostic devices are organized into a structured lookup table with a one-to-one correspondence. This lookup table is the mapping table between diagnostic complexity levels and remote diagnostic devices.

[0071] The mapping table between diagnostic complexity levels and remote diagnostic devices is a standardized device matching rule table pre-built by the platform. This table fully includes all remote diagnostic devices managed by the platform, and the core logic is to match one suitable remote diagnostic device with each diagnostic complexity level. When constructing the mapping table between diagnostic complexity levels and remote diagnostic devices, it is necessary to first analyze the performance parameters of each remote diagnostic device, including diagnostic function coverage, technical accuracy, applicable fault types, and historical diagnostic success rate. Then, combined with the characteristics of different diagnostic complexity levels, low complexity levels are matched with basic diagnostic devices with general functions. These remote diagnostic devices can meet the diagnostic needs of common minor vehicle faults. Medium complexity levels are matched with mid-range diagnostic devices with multi-system testing capabilities. These remote diagnostic devices can handle general complex faults involving multiple vehicle components. High complexity levels are matched with technologically advanced dedicated diagnostic devices. These remote diagnostic devices can handle technically challenging problems such as core engine faults or multi-system linkage faults.

[0072] S505: Determine the target remote diagnostic device based on the target diagnostic complexity level and the mapping table.

[0073] In this embodiment, the pre-built mapping table between diagnostic complexity levels and remote diagnostic devices can be retrieved first. Then, the target diagnostic complexity level previously determined for the target vehicle is used as the matching basis. The remote diagnostic device entry corresponding to the level is searched in the mapping table. Since the mapping table pre-sets a rule that each diagnostic complexity level corresponds to one remote diagnostic device, the unique matching device can be quickly located. This device that precisely corresponds to the target diagnostic complexity level is the target remote diagnostic device that can meet the fault diagnosis needs of the target vehicle. The entire matching process does not require additional complex calculations and can be completed efficiently based on the standardized mapping relationship. This ensures the accuracy of device matching and improves the overall efficiency of the remote diagnostic service.

[0074] Please see Figure 6 , Figure 6 This application provides a flowchart for determining the complexity value of a target diagnosis, including but not limited to the following steps: S601: Based on the fault information, determine the number of fault-related components corresponding to the target vehicle and the frequency of fault occurrence of the target vehicle within a preset time period.

[0075] In this embodiment, the acquired target vehicle fault information can be sorted out first to identify all vehicle components that are directly or indirectly related to the current fault. Then, these components are counted one by one, and the total number obtained is the number of fault-related components. The number of components directly reflects the breadth of the system involved in the fault. Next, the historical fault records of the vehicle within a preset time period are retrieved, and the total number of faults occurring during this period is counted. Combined with the duration of the preset time period, the frequency of fault occurrence is calculated. The frequency of fault occurrence can reflect the persistence and latent characteristics of the fault. Therefore, it is necessary to determine the number of fault-related components corresponding to the target vehicle and the frequency of fault occurrence of the target vehicle within the preset time period based on the fault information.

[0076] S602: Determine a first diagnostic complexity value corresponding to the number of components associated with the fault and a second diagnostic complexity value corresponding to the frequency of the fault occurrence.

[0077] In this embodiment, it can be a first mapping relationship between a preset number of fault-associated components and a diagnostic complexity value, based on which a first diagnostic complexity value corresponding to the number of fault-associated components can be determined. Alternatively, it can be a second mapping relationship between a preset fault occurrence frequency and a diagnostic complexity value, based on which a second diagnostic complexity value corresponding to the fault occurrence frequency can be determined.

[0078] S603: Determine the target diagnostic complexity value based on the first diagnostic complexity value and the second diagnostic complexity value.

[0079] In this embodiment, determining the target diagnostic complexity value based on the first and second diagnostic complexity values ​​is a multi-dimensional quantitative evaluation process that first integrates basic indicators and then combines them with vehicle service life optimization. Specifically, the first and second diagnostic complexity values ​​are first calculated according to a preset algorithm to obtain a reference diagnostic complexity value. This reference value comprehensively reflects the impact of two core factors on diagnostic difficulty: the number of fault-related components and the frequency of fault occurrence. Then, the service life of the target vehicle is obtained as a supplementary indicator. Service life is an important basis for measuring the degree of vehicle aging and potential fault risks. Next, an optimization factor matching the service life is determined according to preset rules. The longer the service life, the more likely the optimization factor will be adjusted to reflect the changes in diagnostic difficulty caused by vehicle aging. Finally, the reference diagnostic complexity value is corrected using the optimization factor according to a preset calculation method to obtain a target diagnostic complexity value that comprehensively reflects the scope of the fault, the fault occurrence pattern, and the aging state of the vehicle.

[0080] For example, a reference diagnostic complexity value is determined based on the first diagnostic complexity value and the second diagnostic complexity value. Specifically, the first diagnostic complexity value, which reflects the number of fault-related components, and the second diagnostic complexity value, which reflects the frequency of fault occurrence, can be integrated and calculated according to a pre-set calculation rule. The calculation methods that can be used include weighted summation, arithmetic mean, etc. The specific algorithm will set different weights according to the platform's emphasis on the two indicators, and the reference diagnostic complexity value is obtained through the calculation.

[0081] For example, the service life of the target vehicle is obtained. Specifically, the service life of the target vehicle is an important indicator for measuring the aging degree of vehicle parts and the potential risk of failure. Generally, the longer the service life of the vehicle, the higher the wear and aging degree of the parts, and the more hidden the cause of the failure may be and the more likely it is to cause multi-component linkage failure. This will directly increase the difficulty of diagnosis. Therefore, based on the reference diagnostic complexity value determined by combining the number of fault-related parts and the frequency of failure, the reference value will be reasonably corrected according to the length of the vehicle's service life. This will ensure that the final target diagnostic complexity value not only reflects the characteristics of the failure itself, but also takes into account the wear and tear caused by long-term vehicle use. This will make the determination of the diagnostic complexity value more in line with the actual condition of the vehicle. Therefore, it is necessary to obtain the service life of the target vehicle first.

[0082] For example, an optimization factor corresponding to the service life can be determined. Specifically, it can be a preset mapping relationship between the service life and the optimization factor. Based on this mapping relationship, the optimization factor corresponding to the service life can be determined.

[0083] For example, the target diagnostic complexity value is obtained by adjusting the reference diagnostic complexity value based on the optimization factor. Specifically, the target diagnostic complexity value is calculated according to the following formula: Target diagnostic complexity value = Reference diagnostic complexity value × (1 + optimization factor); The reference diagnostic complexity value can be adjusted based on the optimization factor according to the above formula to obtain the target diagnostic complexity value.

[0084] As can be seen, firstly, by extracting two core indicators from the fault information—the number of fault-related components and the frequency of fault occurrence—and matching them with corresponding complexity values, the diagnostic difficulty can be quantified from two dimensions: the scope of the fault and its occurrence pattern, providing a solid foundation for assessment. Secondly, by integrating the values ​​from the two dimensions to obtain a reference diagnostic complexity value, and then introducing the key parameter of vehicle age to correct the reference value, the additional diagnostic difficulty caused by vehicle aging, such as the concealment of faults and the risk of multi-component linkage, can be fully considered, avoiding the one-sidedness caused by assessment based solely on fault information. Finally, the entire assessment process is progressive and logically rigorous, and the calculated target diagnostic complexity value can more comprehensively reflect the true diagnostic difficulty of vehicle faults, providing scientific and reliable quantitative support for the classification of diagnostic complexity levels and the accurate matching of diagnostic equipment, thereby improving the overall efficiency and accuracy of remote diagnostic services.

[0085] In summary, implementing the embodiments of the present invention has the following beneficial effects: As can be seen, the remote diagnostic cost estimation method described in the embodiments of the present invention first obtains n remote diagnostic devices mounted on the target diagnostic platform, then determines the remote diagnostic device required for remote diagnostics of the target vehicle among the n remote diagnostic devices, thus obtaining the target remote diagnostic device, and finally determines the remote diagnostic cost required for remote diagnostics of the target vehicle through the target remote diagnostic device, thus obtaining the target remote diagnostic cost, thereby accurately estimating the cost required for remote diagnostics of the vehicle.

[0086] Please see Figure 7 , Figure 7 This is a schematic diagram of the structure of a remote diagnostic cost estimation device provided in an embodiment of this application. The remote diagnostic cost estimation device 700 includes: an acquisition unit 701 and a processing unit 702. The acquisition unit 701 is used to acquire n remote diagnostic devices mounted on the target diagnostic platform; n is an integer greater than 1. The processing unit 702 is used to determine the remote diagnostic device required for the target vehicle to perform remote diagnostics among the n remote diagnostic devices, and obtain the target remote diagnostic device. Determine the remote diagnostic cost required to perform remote diagnostics on the target vehicle using the target remote diagnostic device, and obtain the target remote diagnostic cost.

[0087] In some possible implementations, the processing unit 702, in determining the remote diagnostic cost required to perform remote diagnostics on the target vehicle via the target remote diagnostic device, and obtaining the target remote diagnostic cost, is specifically used for: Determine the preset basic diagnostic fee corresponding to the target remote diagnostic device and the preset platform commission rate corresponding to the target diagnostic platform; Obtain the first purchase cost of the target remote diagnostic device and the first diagnostic success rate of the target remote diagnostic device within a historical time period; The target diagnostic platform commission adjustment coefficient corresponding to the target remote diagnostic device is determined based on the first purchase cost and the first diagnostic success rate. The target platform commission rate is obtained by adjusting the preset platform commission rate based on the target diagnostic platform commission adjustment coefficient. The platform commission fee is determined based on the target platform's commission rate and the preset basic diagnostic fee. The target remote diagnostic fee is obtained by adjusting the preset basic diagnostic fee based on the platform's commission fee.

[0088] In some possible implementations, in determining the target diagnostic platform commission adjustment coefficient corresponding to the target remote diagnostic device based on the first acquisition cost and the first diagnostic success rate, the processing unit 702 is specifically used for: The first mapping relationship between the purchase cost and the diagnostic platform commission adjustment coefficient, and the second mapping relationship between the diagnostic success rate and the diagnostic platform commission adjustment coefficient are obtained. Based on the first mapping relationship, the diagnostic platform commission adjustment coefficient corresponding to the first purchase cost is determined, and the first diagnostic platform commission adjustment coefficient is obtained. Based on the second mapping relationship, the diagnostic platform commission adjustment coefficient corresponding to the first diagnostic success rate is determined, and the second diagnostic platform commission adjustment coefficient is obtained. The target diagnostic platform's commission adjustment coefficient is determined based on the first diagnostic platform's commission adjustment coefficient and the second diagnostic platform's commission adjustment coefficient.

[0089] In some possible implementations, in determining the target diagnostic platform commission adjustment coefficient based on the first diagnostic platform commission adjustment coefficient and the second diagnostic platform commission adjustment coefficient, the processing unit 702 is specifically configured to: Determine the first weight corresponding to the first diagnostic platform commission adjustment coefficient and the second weight corresponding to the second diagnostic platform commission adjustment coefficient; the sum of the first weight and the second weight is 1; The reference diagnostic platform commission adjustment coefficient is determined based on the first diagnostic platform commission adjustment coefficient, the second diagnostic platform commission adjustment coefficient, the first weight, and the second weight. Obtain the user satisfaction rate corresponding to the target remote diagnostic device; Determine the adjustment parameters corresponding to the user approval rating; The target diagnostic platform's commission adjustment coefficient is obtained by adjusting the reference diagnostic platform's commission adjustment coefficient based on the adjustment parameters.

[0090] In some possible implementations, regarding determining the remote diagnostic device required for remote diagnostics of the target vehicle among the n remote diagnostic devices, and obtaining the target remote diagnostic device, the processing unit 702 is specifically used for: Obtain the fault information of the target vehicle; Based on the fault information, determine the target diagnostic complexity value corresponding to the target vehicle; The target diagnostic complexity level corresponding to the target vehicle is determined based on the target diagnostic complexity value; Obtain a mapping table between diagnostic complexity levels and remote diagnostic devices; the mapping table includes the n remote diagnostic devices, with each diagnostic complexity level corresponding to one remote diagnostic device; The target remote diagnostic device is determined based on the target diagnostic complexity level and the mapping table.

[0091] In some possible implementations, in determining the target diagnostic complexity value corresponding to the target vehicle based on the fault information, the processing unit 702 is specifically used for: Based on the fault information, determine the number of fault-related components corresponding to the target vehicle and the frequency of fault occurrence of the target vehicle within a preset time period; Determine a first diagnostic complexity value corresponding to the number of components associated with the fault and a second diagnostic complexity value corresponding to the frequency of fault occurrence; The target diagnostic complexity value is determined based on the first diagnostic complexity value and the second diagnostic complexity value.

[0092] In some possible implementations, in determining the target diagnostic complexity value based on the first diagnostic complexity value and the second diagnostic complexity value, the processing unit 702 is specifically configured to: A reference diagnostic complexity value is determined based on the first diagnostic complexity value and the second diagnostic complexity value; Obtain the service life of the target vehicle; Determine the optimization factor corresponding to the service life; The target diagnostic complexity value is obtained by adjusting the reference diagnostic complexity value based on the optimization factor.

[0093] Please see Figure 8 , Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. For example... Figure 8 As shown, the electronic device 800 includes a transceiver 801, a processor 802, and a memory 803. These are connected via a bus 804. The memory 803 stores computer programs and data, and the transceiver 801 can transmit data stored in the memory 803 to the processor 802. The program includes instructions for performing the following steps: Obtain the n remote diagnostic devices mounted on the target diagnostic platform; n is an integer greater than 1; Determine the remote diagnostic device required for remote diagnostics of the target vehicle from among the n remote diagnostic devices, and obtain the target remote diagnostic device; Determine the remote diagnostic cost required to perform remote diagnostics on the target vehicle using the target remote diagnostic device, and obtain the target remote diagnostic cost.

[0094] In some possible implementations, the above procedure includes instructions for performing the following steps in order to determine the remote diagnostic cost required to remotely diagnose the target vehicle via the target remote diagnostic device and to obtain the target remote diagnostic cost: Determine the preset basic diagnostic fee corresponding to the target remote diagnostic device and the preset platform commission rate corresponding to the target diagnostic platform; Obtain the first purchase cost of the target remote diagnostic device and the first diagnostic success rate of the target remote diagnostic device within a historical time period; The target diagnostic platform commission adjustment coefficient corresponding to the target remote diagnostic device is determined based on the first purchase cost and the first diagnostic success rate. The target platform commission rate is obtained by adjusting the preset platform commission rate based on the target diagnostic platform commission adjustment coefficient. The platform commission fee is determined based on the target platform's commission rate and the preset basic diagnostic fee. The target remote diagnostic fee is obtained by adjusting the preset basic diagnostic fee based on the platform's commission fee.

[0095] In some possible implementations, the above procedure includes instructions for performing the following steps in determining the target diagnostic platform commission adjustment coefficient corresponding to the target remote diagnostic device based on the first acquisition cost and the first diagnostic success rate: The first mapping relationship between the purchase cost and the diagnostic platform commission adjustment coefficient, and the second mapping relationship between the diagnostic success rate and the diagnostic platform commission adjustment coefficient are obtained. Based on the first mapping relationship, the diagnostic platform commission adjustment coefficient corresponding to the first purchase cost is determined, and the first diagnostic platform commission adjustment coefficient is obtained. Based on the second mapping relationship, the diagnostic platform commission adjustment coefficient corresponding to the first diagnostic success rate is determined, and the second diagnostic platform commission adjustment coefficient is obtained. The target diagnostic platform's commission adjustment coefficient is determined based on the first diagnostic platform's commission adjustment coefficient and the second diagnostic platform's commission adjustment coefficient.

[0096] In some possible implementations, the above procedure includes instructions for performing the following steps in determining the target diagnostic platform commission adjustment coefficient based on the first diagnostic platform commission adjustment coefficient and the second diagnostic platform commission adjustment coefficient: Determine the first weight corresponding to the first diagnostic platform commission adjustment coefficient and the second weight corresponding to the second diagnostic platform commission adjustment coefficient; the sum of the first weight and the second weight is 1; The reference diagnostic platform commission adjustment coefficient is determined based on the first diagnostic platform commission adjustment coefficient, the second diagnostic platform commission adjustment coefficient, the first weight, and the second weight. Obtain the user satisfaction rate corresponding to the target remote diagnostic device; Determine the adjustment parameters corresponding to the user approval rating; The target diagnostic platform's commission adjustment coefficient is obtained by adjusting the reference diagnostic platform's commission adjustment coefficient based on the adjustment parameters.

[0097] In some possible implementations, regarding determining the remote diagnostic device required for remote diagnostics of the target vehicle among the n remote diagnostic devices, and obtaining the target remote diagnostic device, the above procedure includes instructions for performing the following steps: Obtain the fault information of the target vehicle; Based on the fault information, determine the target diagnostic complexity value corresponding to the target vehicle; The target diagnostic complexity level corresponding to the target vehicle is determined based on the target diagnostic complexity value; Obtain a mapping table between diagnostic complexity levels and remote diagnostic devices; the mapping table includes the n remote diagnostic devices, with each diagnostic complexity level corresponding to one remote diagnostic device; The target remote diagnostic device is determined based on the target diagnostic complexity level and the mapping table.

[0098] In some possible implementations, the above procedure includes instructions for performing the following steps in determining the target diagnostic complexity value corresponding to the target vehicle based on the fault information: Based on the fault information, determine the number of fault-related components corresponding to the target vehicle and the frequency of fault occurrence of the target vehicle within a preset time period; Determine a first diagnostic complexity value corresponding to the number of components associated with the fault and a second diagnostic complexity value corresponding to the frequency of fault occurrence; The target diagnostic complexity value is determined based on the first diagnostic complexity value and the second diagnostic complexity value.

[0099] In some possible implementations, in determining the target diagnostic complexity value based on the first diagnostic complexity value and the second diagnostic complexity value, the above procedure includes instructions for performing the following steps: A reference diagnostic complexity value is determined based on the first diagnostic complexity value and the second diagnostic complexity value; Obtain the service life of the target vehicle; Determine the optimization factor corresponding to the service life; The target diagnostic complexity value is obtained by adjusting the reference diagnostic complexity value based on the optimization factor.

[0100] It should be understood that the electronic devices mentioned in this application may include smartphones (such as Android phones, iOS phones, Windows Phones, etc.), tablets, PDAs, laptops, mobile internet devices (MIDs) or wearable devices, servers, edge computing nodes, etc. The above-mentioned electronic devices are merely examples and not exhaustive, and include, but are not limited to, the electronic devices described above.

[0101] This application also provides a computer-readable storage medium storing a computer program that is executed by a processor to implement some or all of the steps of any of the methods described in the above method embodiments.

[0102] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments.

[0103] It should be noted that, for the sake of simplicity, the aforementioned methods are described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are optional, and the actions and modules involved are not necessarily essential to this application.

[0104] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0105] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0106] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0107] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software program module.

[0108] If the integrated unit is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, 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. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0109] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage device, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0110] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The above description of the embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for estimating the cost of remote diagnosis, characterized in that, include: Obtain the n remote diagnostic devices mounted on the target diagnostic platform; n is an integer greater than 1; Determine the remote diagnostic device required for remote diagnostics of the target vehicle from among the n remote diagnostic devices, and obtain the target remote diagnostic device; Determine the remote diagnostic cost required to perform remote diagnostics on the target vehicle using the target remote diagnostic device, and obtain the target remote diagnostic cost.

2. The method as described in claim 1, characterized in that, The determination of the remote diagnostic cost required to perform remote diagnostics on the target vehicle using the target remote diagnostic device, and the acquisition of the target remote diagnostic cost, includes: Determine the preset basic diagnostic fee corresponding to the target remote diagnostic device and the preset platform commission rate corresponding to the target diagnostic platform; Obtain the first purchase cost of the target remote diagnostic device and the first diagnostic success rate of the target remote diagnostic device within a historical time period; The target diagnostic platform commission adjustment coefficient corresponding to the target remote diagnostic device is determined based on the first purchase cost and the first diagnostic success rate. The target platform commission rate is obtained by adjusting the preset platform commission rate based on the target diagnostic platform commission adjustment coefficient. The platform commission fee is determined based on the target platform's commission rate and the preset basic diagnostic fee. The target remote diagnostic fee is obtained by adjusting the preset basic diagnostic fee based on the platform's commission fee.

3. The method as described in claim 2, characterized in that, The step of determining the target diagnostic platform commission adjustment coefficient corresponding to the target remote diagnostic device based on the first purchase cost and the first diagnostic success rate includes: The first mapping relationship between the purchase cost and the diagnostic platform commission adjustment coefficient, and the second mapping relationship between the diagnostic success rate and the diagnostic platform commission adjustment coefficient are obtained. Based on the first mapping relationship, the diagnostic platform commission adjustment coefficient corresponding to the first purchase cost is determined, and the first diagnostic platform commission adjustment coefficient is obtained. Based on the second mapping relationship, the diagnostic platform commission adjustment coefficient corresponding to the first diagnostic success rate is determined, and the second diagnostic platform commission adjustment coefficient is obtained. The target diagnostic platform's commission adjustment coefficient is determined based on the first diagnostic platform's commission adjustment coefficient and the second diagnostic platform's commission adjustment coefficient.

4. The method as described in claim 3, characterized in that, Determining the target diagnostic platform's commission adjustment coefficient based on the first diagnostic platform's commission adjustment coefficient and the second diagnostic platform's commission adjustment coefficient includes: Determine the first weight corresponding to the first diagnostic platform commission adjustment coefficient and the second weight corresponding to the second diagnostic platform commission adjustment coefficient; the sum of the first weight and the second weight is 1; The reference diagnostic platform commission adjustment coefficient is determined based on the first diagnostic platform commission adjustment coefficient, the second diagnostic platform commission adjustment coefficient, the first weight, and the second weight. Obtain the user satisfaction rate corresponding to the target remote diagnostic device; Determine the adjustment parameters corresponding to the user approval rating; The target diagnostic platform's commission adjustment coefficient is obtained by adjusting the reference diagnostic platform's commission adjustment coefficient based on the adjustment parameters.

5. The method as described in claim 1, characterized in that, The process of determining the remote diagnostic device required for remote diagnostics of the target vehicle from among the n remote diagnostic devices, and obtaining the target remote diagnostic device, includes: Obtain the fault information of the target vehicle; Based on the fault information, determine the target diagnostic complexity value corresponding to the target vehicle; The target diagnostic complexity level corresponding to the target vehicle is determined based on the target diagnostic complexity value; Obtain a mapping table between diagnostic complexity levels and remote diagnostic devices; the mapping table includes the n remote diagnostic devices, with each diagnostic complexity level corresponding to one remote diagnostic device; The target remote diagnostic device is determined based on the target diagnostic complexity level and the mapping table.

6. The method as described in claim 5, characterized in that, Determining the target diagnostic complexity value corresponding to the target vehicle based on the fault information includes: Based on the fault information, determine the number of fault-related components corresponding to the target vehicle and the frequency of fault occurrence of the target vehicle within a preset time period; Determine a first diagnostic complexity value corresponding to the number of components associated with the fault and a second diagnostic complexity value corresponding to the frequency of fault occurrence; The target diagnostic complexity value is determined based on the first diagnostic complexity value and the second diagnostic complexity value.

7. The method as described in claim 6, characterized in that, Determining the target diagnostic complexity value based on the first diagnostic complexity value and the second diagnostic complexity value includes: A reference diagnostic complexity value is determined based on the first diagnostic complexity value and the second diagnostic complexity value; Obtain the service life of the target vehicle; Determine the optimization factor corresponding to the service life; The target diagnostic complexity value is obtained by adjusting the reference diagnostic complexity value based on the optimization factor.

8. A remote diagnostic cost estimation device, characterized in that, The device includes: an acquisition unit and a processing unit; The acquisition unit is used to acquire n remote diagnostic devices mounted on the target diagnostic platform; n is an integer greater than 1. The processing unit is used to determine the remote diagnostic device required for the target vehicle to perform remote diagnostics among the n remote diagnostic devices, and to obtain the target remote diagnostic device. Determine the remote diagnostic cost required to perform remote diagnostics on the target vehicle using the target remote diagnostic device, and obtain the target remote diagnostic cost.

9. An electronic device, characterized in that, The method includes a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the one or more programs include instructions for performing the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that is executed by a processor to implement the method as described in any one of claims 1-7.

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