Goods damage guarantee digital management method and storage medium

Abstract

The invention discloses a goods damage guarantee digital management method and a storage medium, and the method comprises the steps: receiving transportation task information which comprises a goods type and a transportation distance; weight distribution is carried out on the risk coefficient of the cargo type, the risk coefficient of the transportation distance and the historical cargo damage rate associated with the cargo type, a basic risk score is calculated in a weighted mode, and a guarantee insurance type is matched according to the basic risk score; within the matched guarantee insurance type quota range, calculating a recommended guarantee quota according to the risk score linear difference value; calculating a basic insurance premium according to the recommended insurance quota and the basic insurance premium rate; and adjusting the insurance premium according to the credit score and the member level of the driver, and generating and pushing a guarantee scheme based on the adjusted final insurance premium and the recommended guarantee quota. Weighted calculation is carried out through multiple factors such as the cargo type, the transportation distance and the historical cargo damage rate, the guarantee limit matched with the real risk is dynamically generated for the transportation task, and the accuracy of the guarantee service and the user experience are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of logistics freight insurance, and in particular to a cargo loss guarantee digital management method and a storage medium. BACKGROUND

[0002] In the field of logistics transportation, it is a common risk management method to purchase cargo loss guarantee for goods. However, the existing transportation cargo loss guarantee service, especially the digital service for individual drivers, has obvious limitations in its technical solution. The existing transportation cargo loss guarantee scheme mostly adopts a general mode and cannot accurately match the actual risks of different goods. The current system usually sets a fixed guarantee limit based on a single dimension of goods value, without considering the high cargo loss characteristics of special goods types such as fragile goods and liquids, or combining key factors such as transportation distance and historical route risk, resulting in insufficient guarantee for high-risk transportation tasks and excessive guarantee for low-risk tasks, which not only increases the cost of drivers but also fails to effectively disperse risks.

[0003] In addition, after a cargo loss occurs, the claim process is complicated and usually requires drivers to mail paper documents offline, and manual review and payment operations, which is a long process that not only seriously affects the turnover of drivers' funds but also easily leads to disputes due to opaque payment standards. SUMMARY

[0004] To solve the technical problems in the background art, the present application proposes a cargo loss guarantee digital management method and a storage medium, which solves the problem that the existing logistics guarantee scheme only sets a fixed guarantee limit based on a single dimension of goods value, and the recommended guarantee limit does not match the real risk.

[0005] The present application proposes a cargo loss guarantee digital management method, which includes: receiving transportation task information, the transportation task information including goods type and transportation distance; assigning weights to the risk coefficient of the goods type, the risk coefficient of the transportation distance, and the historical cargo loss rate associated with the goods type, calculating a basic risk score by weighting, and matching a guarantee type according to the basic risk score; within the limit of the matched guarantee type, calculating a recommended guarantee limit according to the linear difference of the risk score; calculating a basic premium according to the recommended guarantee limit and the basic premium rate; then adjusting the premium according to the credit score and membership level of the driver, and generating a guarantee scheme based on the adjusted final premium and the recommended guarantee limit and pushing it to the user for confirmation.

[0006] Preferably, it also includes an automated claim review and payment process, specifically: Claim and evidence uploading: in response to the driver's operation, a claim case is created and the driver is guided to upload the cargo damage evidence, which at least includes multi-angle cargo damage photos, transport document photos, and automatically attached with this transport task information, GPS location information and timestamp; Multi-modal evidence automatic review: call the image recognition model to automatically analyze the uploaded cargo damage photos, and the recognition content includes the damage degree of the goods, the damage part, and whether the license plate number is clear; at the same time, verify the consistency of the uploaded transport document and this transport task information, and verify the consistency of the GPS location information and the planned route, and generate a preliminary review result; Loss amount intelligent assessment: based on the image-recognized damage degree, combined with the type of goods and the declared value of the goods, the preliminary loss amount is calculated through the loss assessment model; the loss assessment model is a regression model trained based on historical cargo damage cases, the input is the cargo damage image features and the type of goods, and the output is the loss ratio, and the preliminary loss amount = declared value of goods x loss ratio; Payout scheme generation and execution: according to the preliminary review result and the preliminary loss amount, combined with the recommended guarantee amount in the guarantee scheme and the deductible rules of the target guarantee type, the final payout amount is calculated; after the audit is passed, the system automatically generates a payout single and initiates a payment to the driver's account through the payment interface; Case closure and data feedback: after the payment is successfully executed, the system automatically closes the claim case, and feeds back the key data of this claim, including the type of cargo damage, the loss amount, and the review result, to the cargo damage rate database and the driver credit scoring system, for updating the model and data.

[0007] Preferably, the multi-modal evidence automatic review specifically includes: For cargo damage photos, use convolutional neural networks to identify damaged areas and segment damaged areas, and divide the damage degree into "mild", "moderate" and "severe" according to a preset threshold; For transport documents, use optical character recognition technology to extract key fields and compare with system records; Integrate image recognition results, document verification results and GPS verification results, if all dimensions pass and the damage level is "mild" or "moderate", the automatic review is passed; if any dimension fails or the damage level is "severe", the manual review process is automatically triggered, and all analysis results are highlighted.

[0008] Preferably, the weighted calculation of the basic risk score is specifically: A multi-factor risk assessment model is established, and its calculation formula is: Basic risk score = (Cargo type risk coefficient × weight α) + (Transportation distance risk coefficient × weight β) + (Cargo historical inherent damage rate × weight γ) + (Route historical damage rate × weight δ), where α + β + γ + δ = 100%; The risk coefficient for each type of goods is based on a goods classification system that includes categories such as fragile goods, high-value goods, and general merchandise, with a risk coefficient of 0.1-0.9 assigned to each category. The transportation distance risk coefficient is calculated using a piecewise function: 0.1-0.3 for 0-100 km, 0.3-0.6 for 100-500 km, and 0.6-0.9 for distances over 500 km. The historical inherent cargo damage rate is obtained from the cargo damage rate database and is calculated as the ratio of the number of cargo damages of this category to the total number of transportations within a preset statistical period. The historical cargo damage rate of the route is obtained from the route risk database and is calculated as the ratio of the total amount of cargo damage to the total declared value of the transport of the same type of goods on the same or similar transport routes within a preset statistical period. The weights α, β, γ, and δ are dynamically optimized through a machine learning model. This model uses historical cargo damage data as a training set and aims to minimize the error between the predicted risk and the actual cargo damage, and updates the weight allocation periodically.

[0009] The preferred method for calculating the recommended coverage amount is as follows: Recommended coverage amount = Amin + [(S - Smin) / (Smax - Smin)] × (Amax - Amin); Wherein, Amin and Amax are the lower and upper limits of the coverage range of the target insurance type, respectively, S is the basic risk score, and Smin and Smax are the lower and upper limits of the basic risk score range required to match the target insurance type, respectively. The calculation process incorporates a cargo value correction factor. When the calculated recommended coverage amount exceeds a preset percentage threshold of the declared value of the cargo, the upper limit protection mechanism is activated, limiting the recommended coverage amount to no more than 120% of the declared value of the cargo.

[0010] Preferably, the premium adjustment is as follows: Obtain a driver credit score that is periodically updated by the platform's credit system. This score is calculated based on the driver's historical compensation records, on-time delivery rate, and user reviews. Obtain the driver's membership level information, which is determined based on the driver's historical total transportation volume, activity level, and years of cooperation on the platform; Establish a premium adjustment factor table to map different combinations of credit score ranges and membership levels to different premium adjustment factors; The final premium is calculated as follows: Final premium = Basic premium × K, where K is a premium adjustment coefficient determined based on the current driver credit score and membership level, and the value of K ranges from 0.7 to 1.3.

[0011] Preferably, premiums are adjusted based on the driver's credit score and membership level, specifically including: A multi-dimensional premium adjustment model has been established. The credit score discount coefficient is calculated based on the driver's credit record over the past 12 months. A credit score of 90-100 enjoys a 10% discount, 80-89 enjoys a 5% discount, 70-79 has no discount, and a score below 70 will incur a premium of 5%-10%. Membership level discounts are based on drivers' loyalty data on the platform. Regular members receive no discount, silver members receive a 3% discount, gold members receive a 5% discount, and platinum members receive an 8% discount. Final premium = base premium × (1 - credit score discount factor) × (1 - membership level discount factor).

[0012] Preferably, the specific method for calculating a driver's credit score is as follows: Credit score = Historical claims factor × F1 + On-time delivery factor × F2 + User evaluation factor × F3 + Behavioral norms factor × F4; Among them, the historical compensation factor is calculated based on the ratio of the number of compensation applications initiated by drivers to the total number of transport orders in the past year; The on-time delivery factor is calculated based on the percentage of orders delivered on time by drivers in the past year; User rating factor is calculated based on the average rating of the driver by the recipient. The behavioral norms factor is calculated based on records of whether drivers comply with the platform's transportation procedures; F1, F2, F3, and F4 are the weight coefficients of each factor, and F1+F2+F3+F4=1.

[0013] The present invention also proposes a computer storage medium comprising a stored program, wherein a processor executes the program to implement the above-described method.

[0014] Beneficial effects: This invention uses a weighted calculation based on multiple factors, including cargo type, transportation distance, and historical damage rate, to dynamically generate a coverage amount that matches the actual risk for each transportation task. This avoids drivers paying unnecessary premiums and ensures full compensation in the event of cargo damage, improving the accuracy of the coverage service and user experience. Furthermore, by incorporating driver credit scores and membership levels into the premium adjustment mechanism, a sound credit incentive system is established to encourage drivers to operate in a standardized manner, thereby reducing the risk of cargo damage at its source. Attached Figure Description

[0015] Figure 1 This is a flowchart of a digital management method for cargo damage protection proposed in this invention; Figure 2 This is a schematic diagram of the automated claims review and payment process proposed in an embodiment of the present invention. Detailed Implementation

[0016] like Figure 1 As shown, Figure 1 This is a flowchart illustrating a digital management method for cargo damage protection according to an embodiment of the present invention; Reference Figure 1 The present invention provides a digital management method for cargo damage protection, comprising: S10: Receive transportation task information, which includes cargo type and transportation distance; S20: Assign weights to the risk coefficient of the cargo type, the risk coefficient of the transportation distance, and the historical cargo damage rate associated with the cargo type, calculate the basic risk score by weighting, and match the insurance type according to the basic risk score; The system has three preset insurance types: Basic Insurance (coverage of 50%-80% of the value of the goods, basic premium rate of 0.5%), Standard Insurance (coverage of 80%-120% of the value of the goods, basic premium rate of 0.8%), and Premium Insurance (coverage of 120%-150% of the value of the goods, basic premium rate of 1.2%). The matching rule is: Basic Insurance is matched with a risk score of 0-3, Standard Insurance is matched with a risk score of 4-7, and Premium Insurance is matched with a risk score of 8-10.

[0017] In some implementation methods, the weighted calculation of the basic risk score is specifically as follows: A multi-factor risk assessment model is established, and its calculation formula is: Basic risk score = (Cargo type risk coefficient × weight α) + (Transportation distance risk coefficient × weight β) + (Cargo historical inherent damage rate × weight γ) + (Route historical damage rate × weight δ), where α + β + γ + δ = 100%; The risk coefficient for each type of goods is based on a goods classification system that includes categories such as fragile goods, high-value goods, and general merchandise, with a risk coefficient of 0.1-0.9 assigned to each category. The transportation distance risk coefficient is calculated using a piecewise function: 0.1-0.3 for 0-100 km, 0.3-0.6 for 100-500 km, and 0.6-0.9 for distances over 500 km. The historical inherent cargo damage rate is obtained from the cargo damage rate database and is calculated as the ratio of the number of cargo damages of this category to the total number of transportations within a preset statistical period. The historical cargo damage rate of the route is obtained from the route risk database and is calculated as the ratio of the total amount of cargo damage to the total declared value of the transport of the same type of goods on the same or similar transport routes within a preset statistical period. The weights α, β, γ, and δ are dynamically optimized through a machine learning model. This model uses historical cargo damage data as a training set and aims to minimize the error between the predicted risk and the actual cargo damage, and updates the weight allocation periodically.

[0018] Preferably, the weight α of the cargo type risk coefficient is 30%-40%; the weight β of the transportation distance risk coefficient is 25%-35%; the weight γ of the cargo's historical inherent damage rate is 10%-25%; and the weight δ of the route's historical damage rate is 10%-25%.

[0019] S30: Within the range of the matching insurance coverage amount, calculate the recommended coverage amount based on the linear difference of the risk score; In some implementation methods, the recommended coverage amount is calculated as follows: Recommended coverage amount = Amin + [(S - Smin) / (Smax - Smin)] × (Amax - Amin); Wherein, Amin and Amax are the lower and upper limits of the coverage range of the target insurance type, respectively, S is the basic risk score, and Smin and Smax are the lower and upper limits of the basic risk score range required to match the target insurance type, respectively. The calculation process incorporates a cargo value correction factor. When the calculated recommended coverage amount exceeds a preset percentage threshold of the declared value of the cargo, the upper limit protection mechanism is activated, limiting the recommended coverage amount to no more than 120% of the declared value of the cargo.

[0020] S40: Calculate the basic premium based on the recommended coverage amount and basic premium rate; S50: Next, the premium is adjusted based on the driver's credit score and membership level. Based on the adjusted final premium and recommended coverage amount, a protection plan is generated and pushed to the user for confirmation.

[0021] In some implementation methods, the premium adjustment specifically includes: Obtain a driver credit score that is periodically updated by the platform's credit system. This score is calculated based on the driver's historical compensation records, on-time delivery rate, and user reviews. Obtain the driver's membership level information, which is determined based on the driver's historical total transportation volume, activity level, and years of cooperation on the platform; Establish a premium adjustment factor table to map different combinations of credit score ranges and membership levels to different premium adjustment factors; The final premium is calculated as follows: Final premium = Basic premium × K, where K is a premium adjustment coefficient determined based on the current driver credit score and membership level, and the value of K ranges from 0.7 to 1.3.

[0022] Credit scores are closely linked to an individual driver's driving habits, performance history, and cargo damage control capabilities. Insurance premiums are linked to a driver's credit score, which incentivizes drivers with good credit and excellent records to receive higher premiums, while effectively constraining high-risk drivers financially.

[0023] Compared to existing systems that recommend coverage amounts based solely on a fixed percentage of cargo value, this invention comprehensively considers multiple risk factors, including cargo type (such as the inherent difference in damage rates between liquor and ordinary paper products), specific transportation routes (such as the risk differences between mountainous and plain routes), and historical damage records. This enables precise risk assessment, ensuring that the recommended coverage amount matches the actual risk. It ensures adequate protection for high-risk transportation tasks and avoids paying unnecessary high premiums for low-risk tasks, achieving a dynamic and precise match between the protection plan and the actual risk.

[0024] Secondly, addressing the issue of rigid premium calculation models in existing solutions, which generally employ fixed premium rates and require all drivers to pay the same premium for the same coverage amount, thus lacking positive incentives for driver credit and behavior, this invention abandons this one-size-fits-all pricing model. Instead, it incorporates driver credit scores and membership levels into the premium adjustment mechanism, establishing a sound credit incentive system to encourage drivers to operate in a standardized manner. This reduces cargo damage risk from the source and optimizes the overall transportation quality and resource allocation of the platform.

[0025] In some implementations, such as Figure 2 As shown, it also includes automating the claims review and payment process, specifically: S601: Claim Reporting and Document Upload: In response to the driver's operation, a claim case is created and the driver is guided to upload cargo damage documents. The cargo damage documents include at least multi-angle photos of cargo damage and photos of transport documents, and are automatically attached with the transport task information, GPS location information and timestamp. S602: Multimodal document automatic review: The image recognition model is called to automatically analyze the uploaded damaged cargo photos, and the content of the recognition includes the degree of damage to the goods, the damaged parts, and whether the license plate number is clear and identifiable; at the same time, the consistency between the uploaded transport documents and the information of this transport task is verified, and the consistency between the GPS location information and the planned route is verified, and a preliminary review result is generated. S603: Intelligent assessment of loss amount: Based on the degree of damage to the goods identified by the image, combined with the type of goods and the declared value of the goods, the preliminary loss amount is calculated through the loss assessment model; the loss assessment model is a regression model trained based on historical damage cases, the input of which is the characteristics of the damaged image and the type of goods, and the output is the loss ratio, and the preliminary loss amount = declared value of goods × loss ratio; S604: Claims Plan Generation and Execution: Based on the preliminary review results and the preliminary loss amount, combined with the recommended coverage amount in the protection plan and the deductible rules of the target insurance type, the final claim amount is calculated; after the review is approved, the system automatically generates a claim form and initiates payment to the driver's account through the payment interface; This invention integrates "purchase guarantee - anomaly reporting - online compensation" into a coherent digital process. Particularly in the claims process, online submission of supporting documents and automated review significantly shorten the traditional timeframe of several days for offline mailing and manual review, enabling rapid compensation. This not only addresses the pain point of slow compensation for drivers, protecting their economic interests, but also reduces platform operating costs through process automation and effectively minimizes disputes arising from delayed or unclear compensation.

[0026] Furthermore, S605: Case Closure and Data Feedback: After a successful payout, the system automatically closes the claim case and feeds back the key data of this claim, including the type of cargo damage, the amount of loss, and the review results, to the cargo damage rate database and the driver credit scoring system for updating the model and data.

[0027] In some implementations, step S602, automatic verification of multimodal credentials, specifically includes: For photos of damaged goods, a convolutional neural network is used to identify the damaged areas and segment the damaged areas. Based on a preset threshold, the degree of damage is divided into three levels: "minor", "moderate" and "severe". For transport documents, optical character recognition technology is used to extract key fields and compare them with system records; Based on the combined image recognition results, document verification results, and GPS verification results, if all dimensions pass and the damage level is "minor" or "moderate," the review will be automatically approved. If any dimension fails verification or the damage level is "severe," the manual review process will be automatically triggered, and all analysis results will be highlighted.

[0028] Furthermore, the specific rules for calculating the final compensation amount in step S604 are as follows: Final payout amount = Min(initial loss amount, recommended coverage) × (1 - deductible); The deductible is predetermined by the target insurance type: 10% for basic insurance, 5% for standard insurance, and 0% for premium insurance. Driver credit score is also used as a final adjustment factor: drivers with a credit score above 90 receive a 2% increase in their final compensation; drivers with a credit score below 70 receive a 5% decrease in their final compensation.

[0029] In some implementations, premiums are adjusted based on the driver's credit score and membership level, specifically including: A multi-dimensional premium adjustment model has been established. The credit score discount coefficient is calculated based on the driver's credit record over the past 12 months. A credit score of 90-100 enjoys a 10% discount, 80-89 enjoys a 5% discount, 70-79 has no discount, and a score below 70 will incur a premium of 5%-10%. Membership level discounts are based on drivers' loyalty data on the platform. Regular members receive no discount, silver members receive a 3% discount, gold members receive a 5% discount, and platinum members receive an 8% discount. Final premium = base premium × (1 - credit score discount factor) × (1 - membership level discount factor).

[0030] In some implementation methods, the driver's credit score is calculated as follows: Credit score = Historical claims factor × F1 + On-time delivery factor × F2 + User evaluation factor × F3 + Behavioral norms factor × F4; Among them, the historical compensation factor is calculated based on the ratio of the number of compensation applications initiated by drivers to the total number of transport orders in the past year; The on-time delivery factor is calculated based on the percentage of orders delivered on time by drivers in the past year; User rating factor is calculated based on the average rating of the driver by the recipient. The behavioral norms factor is calculated based on records of whether drivers comply with the platform's transportation procedures; F1, F2, F3, and F4 are the weight coefficients of each factor, and F1+F2+F3+F4=1.

[0031] The present invention also proposes a computer storage medium comprising a stored program, wherein a processor executes the program to implement the above-described method.

[0032] It should be noted that the program in the above embodiments can be written using any combination of one or more programming languages ​​to execute the operations of the embodiments of this application. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0033] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is merely an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0034] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A digital management method for cargo damage protection, characterized in that, include: Receive transportation task information, which includes cargo type and transportation distance; Weights are assigned to the risk coefficient of the cargo type, the risk coefficient of the transportation distance, and the historical cargo damage rate associated with the cargo type. A weighted basic risk score is calculated, and insurance types are matched based on the basic risk score. Within the range of the matching insurance coverage, the recommended coverage amount is calculated based on the linear difference of the risk score; Calculate the basic premium based on the recommended coverage amount and basic premium rate; Next, the premium is adjusted based on the driver's credit score and membership level. Based on the adjusted final premium and recommended coverage amount, a protection plan is generated and pushed to the user for confirmation.

2. The digital management method for cargo damage protection according to claim 1, characterized in that, This also includes automating the claims review and payment process, specifically: Claims Reporting and Document Upload: In response to the driver's actions, a claims case is created and the driver is guided to upload cargo damage documents. The cargo damage documents include at least multi-angle photos of cargo damage and photos of transport documents, and are automatically appended with information on the current transport task, GPS location information and timestamp. Multimodal voucher automatic review: The image recognition model is called to automatically analyze the uploaded damaged cargo photos, and the content of recognition includes the degree of damage to the goods, the damaged parts, and whether the license plate number is clear and identifiable; at the same time, the consistency between the uploaded transport documents and the information of this transport task is verified, and the consistency between the GPS location information and the planned route is verified, and a preliminary review result is generated. Intelligent assessment of loss amount: Based on the degree of damage to the goods identified by the image, combined with the type of goods and the declared value of the goods, the preliminary loss amount is calculated through the loss assessment model; the loss assessment model is a regression model trained based on historical damage cases, its input is the damage image features and the type of goods, and the output is the loss ratio, the preliminary loss amount = declared value of goods × loss ratio; Claims settlement plan generation and execution: Based on the preliminary review results and the preliminary loss amount, combined with the recommended coverage amount in the protection plan and the deductible rules of the target insurance type, the final claim amount is calculated; Once approved, the system automatically generates a claim form and initiates payment to the driver's account through the payment interface.

3. The digital management method for cargo damage protection according to claim 2, characterized in that, Automatic verification of multimodal vouchers specifically includes: For photos of damaged goods, a convolutional neural network is used to identify the damaged areas and segment the damaged areas. Based on a preset threshold, the degree of damage is divided into three levels: "minor", "moderate" and "severe". For transport documents, optical character recognition technology is used to extract key fields and compare them with system records; Based on the combined image recognition results, document verification results, and GPS verification results, if all dimensions pass and the damage level is "minor" or "moderate," the review will be automatically approved. If any dimension fails verification or the damage level is "severe," the manual review process will be automatically triggered, and all analysis results will be highlighted.

4. The digital management method for cargo damage protection according to claim 1, characterized in that, The weighted calculation of the basic risk score is as follows: A multi-factor risk assessment model is established, and its calculation formula is: Basic risk score = (Cargo type risk coefficient × weight α) + (Transportation distance risk coefficient × weight β) + (Cargo historical inherent damage rate × weight γ) + (Route historical damage rate × weight δ), where α + β + γ + δ = 100%; The risk coefficient for each type of goods is based on a goods classification system that includes categories such as fragile goods, high-value goods, and general merchandise, with a risk coefficient of 0.1-0.9 assigned to each category. The transportation distance risk coefficient is calculated using a piecewise function: 0.1-0.3 for 0-100 km, 0.3-0.6 for 100-500 km, and 0.6-0.9 for distances over 500 km. The historical inherent cargo damage rate is obtained from the cargo damage rate database and is calculated as the ratio of the number of cargo damages of this category to the total number of transportations within a preset statistical period. The historical cargo damage rate for a route is obtained from the route risk database and is calculated as the ratio of the total amount of cargo damage to the total declared value of the transport of the same type of goods on the same or similar transport routes within a preset statistical period.

5. The digital management method for cargo damage protection according to claim 1, characterized in that, The recommended coverage amount is calculated as follows: Recommended coverage amount = Amin + [(S - Smin) / (Smax - Smin)] × (Amax - Amin); Wherein, Amin and Amax are the lower and upper limits of the coverage range of the target insurance type, respectively, S is the basic risk score, and Smin and Smax are the lower and upper limits of the basic risk score range required to match the target insurance type, respectively. The calculation process incorporates a cargo value correction factor. When the calculated recommended coverage amount exceeds a preset percentage threshold of the declared value of the cargo, the upper limit protection mechanism is activated, limiting the recommended coverage amount to no more than 120% of the declared value of the cargo.

6. The digital management method for cargo damage protection according to claim 1, characterized in that, The specific premium adjustments are as follows: Obtain a driver credit score that is periodically updated by the platform's credit system. This score is calculated based on the driver's historical compensation records, on-time delivery rate, and user reviews. Obtain the driver's membership level information, which is determined based on the driver's historical total transportation volume, activity level, and years of cooperation on the platform; Establish a premium adjustment factor table to map different combinations of credit score ranges and membership levels to different premium adjustment factors; The final premium is calculated as follows: Final premium = Basic premium × K, where K is a premium adjustment coefficient determined based on the current driver credit score and membership level, and the value of K ranges from 0.7 to 1.

3.

7. The digital management method for cargo damage protection according to claim 6, characterized in that, Premiums are adjusted based on the driver's credit score and membership level, specifically including: A multi-dimensional premium adjustment model has been established. The credit score discount coefficient is calculated based on the driver's credit record over the past 12 months. A credit score of 90-100 enjoys a 10% discount, 80-89 enjoys a 5% discount, 70-79 has no discount, and a score below 70 will incur a premium of 5%-10%. Membership level discounts are based on drivers' loyalty data on the platform. Regular members receive no discount, silver members receive a 3% discount, gold members receive a 5% discount, and platinum members receive an 8% discount. Final premium = base premium × (1 - credit score discount factor) × (1 - membership level discount factor).

8. A digital management method for cargo damage protection according to claim 6 or 7, characterized in that, The specific calculation method for a driver's credit score is as follows: Credit score = Historical claims factor × F1 + On-time delivery factor × F2 + User evaluation factor × F3 + Behavioral norms factor × F4; Among them, the historical compensation factor is calculated based on the ratio of the number of compensation applications initiated by drivers to the total number of transport orders in the past year; The on-time delivery factor is calculated based on the percentage of orders delivered on time by drivers in the past year; User rating factor is calculated based on the average rating of the driver by the recipient. The behavioral norms factor is calculated based on records of whether drivers comply with the platform's transportation procedures; F1, F2, F3, and F4 are the weight coefficients of each factor, and F1+F2+F3+F4=1.

9. A computer storage medium, the storage medium comprising a stored program, characterized in that, The processor executes the program to implement a digital management method for cargo damage protection as described in any one of claims 1 to 8.

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