Tamper-proof loss assessment image traceability method and device, equipment and medium

Abstract

The invention relates to the technical field of image detection, can be applied to a financial science and technology business system platform, and discloses a tamper-proof loss assessment image traceability method, device, equipment and medium, and the method comprises the steps: obtaining global positioning data, base station positioning data and equipment sensor data of a loss assessment image; performing fusion processing on the global positioning data, the base station positioning data and the equipment sensor data to obtain a dynamic timestamp; a preset device hardware fingerprint and a preset sensor fingerprint are combined to generate a composite fingerprint; performing asymmetric encryption binding on the dynamic timestamp and the composite fingerprint to obtain encryption binding data; performing multiple hash processing on the loss assessment image to obtain hash chain data; and tracing the loss assessment image according to the encrypted binding data and the Hash chain data to obtain a tracing result. Through traceability of shooting information, the phenomenon that the operator does not arrive at the scene for survey and certificate collection and is shot by others can be avoided, the leakage risk of a case is further reduced, and the operation cost of a company is reduced.

Description

Technical Field

[0001] The present invention relates to the field of image detection technology, and in particular to a tamper-proof damage assessment image tracing method, device, equipment and medium. Background Art

[0002] For a long time, during the process of motor insurance claims investigation and motor insurance claims assessment, it has been necessary for investigation and assessment personnel to arrive at the scene to evaluate and retain photos of the loss with professional knowledge and rich experience. This has an important impact on the accuracy of subsequent insurance liability assessment and risk management.

[0003] However, some unscrupulous car owners may use image processing software to modify the degree of vehicle damage in order to obtain higher compensation amounts, such as exaggerating collision marks, forging damaged parts, or even fabricating accident scenes to tamper with damage assessment images. This behavior not only seriously damages the economic interests of insurance companies and leads to unreasonable compensation expenditures, but also undermines the fair and honest environment of the insurance market, causing policyholders who abide by the rules to bear higher premium costs.

[0004] At the same time, there are also a very small number of damage assessors or repair shop staff with poor professional ethics who may collude with the car owner to tamper with the damage assessment images to help the car owner defraud insurance money and seek personal gain;

[0005] In addition, during the investigation and operation process, in order to ensure a good user experience and timely and accurate investigation and retention of on-site information, operators are required to arrive at the scene quickly to handle the matter. If the operator is delayed and cannot arrive at the scene and asks someone to take photos on his behalf, the system can meet the assessment requirements for the timeliness of the operation, but the collected photos cannot be traced, and their authenticity cannot be guaranteed, which may lead to risk leakage and indirectly affect customer satisfaction. Summary of the invention

[0006] The present invention provides a tamper-proof damage assessment image tracing method, device, equipment and medium. By tracing the source of the photographed information, it can prevent the phenomenon that the operating personnel do not arrive at the scene to investigate and collect evidence, and others take photos on their behalf, thereby reducing the risk of case leakage and helping to reduce the company's operating costs.

[0007] In a first aspect, a tamper-proof damage assessment image traceability method is provided, comprising:

[0008] Obtain global positioning data, base station positioning data, and equipment sensor data of damage assessment images;

[0009] fusing the global positioning data, base station positioning data and device sensor data to obtain a dynamic timestamp;

[0010] Combine a preset device hardware fingerprint and a preset sensor fingerprint to generate a composite fingerprint;

[0011] Perform asymmetric encryption binding on the dynamic timestamp and the composite fingerprint to obtain encrypted binding data;

[0012] Perform multiple hashing on the damage assessment image to obtain hash chain data;

[0013] Trace the damage assessment image based on the encrypted binding data and the hash chain data to obtain a tracing result.

[0014] In a second aspect, a tamper-proof damage assessment image tracing device is provided, including:

[0015] An acquisition module for acquiring global positioning data, base station positioning data, and device sensor data of a damage assessment image;

[0016] A processing module for performing fusion processing on the global positioning data, base station positioning data, and device sensor data to obtain a dynamic timestamp;

[0017] A generation module for combining a preset device hardware fingerprint and a preset sensor fingerprint to generate a composite fingerprint;

[0018] A binding module for performing asymmetric encryption binding on the dynamic timestamp and the composite fingerprint to obtain encrypted binding data;

[0019] A hashing processing module for performing multiple hashing on the damage assessment image to obtain hash chain data;

[0020] A tracing module for tracing the damage assessment image based on the encrypted binding data and the hash chain data to obtain a tracing result.

[0021] In a third aspect, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned tamper-proof damage assessment image tracing method are implemented.

[0022] In a fourth aspect, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned tamper-proof damage assessment image tracing method are implemented.

[0023] In the solution implemented by the above anti-tampering loss assessment image traceability method, device, computer device, and storage medium, the traceability shooting information is used to prevent the phenomenon that the operator fails to reach the scene for investigation and evidence collection and is replaced by others for shooting. This reduces the leakage risk of cases, helps reduce the company's operating costs. At the same time, the fused dynamic timestamp data contains multi-source information, providing richer materials for data analysis. By mining and analyzing this data, more valuable information can be discovered, such as users' behavior patterns, consumption habits, location preferences, etc. This information can help financial institutions better understand users, optimize products and services, and improve market competitiveness. Moreover, the encrypted bound data ensures the authenticity and integrity of the data through encryption technology, and the hash chain data utilizes the characteristics of the hash algorithm, making any minor modification to the loss assessment image result in a change in the hash value. By tracing both, it can be accurately determined whether the loss assessment image has been tampered with during generation, storage, and transmission, ensuring that the loss assessment image can truly reflect the situation of the accident scene. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments of the present invention. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0025] Figure 1 FIG. is a schematic diagram of an application environment of an anti-tampering loss assessment image traceability method in an embodiment of the present invention;

[0026] Figure 2 FIG. is a schematic flowchart of an anti-tampering loss assessment image traceability method in an embodiment of the present invention;

[0027] Figure 3 FIG. is a schematic structural diagram of an anti-tampering loss assessment image traceability device in an embodiment of the present invention;

[0028] Figure 4 FIG. is a schematic diagram of a working process of an anti-tampering loss assessment image traceability method in an embodiment of the present invention;

[0029] Figure 5 FIG. is a schematic structural diagram of a computer device in an embodiment of the present invention;

[0030] Figure 6 FIG. is another schematic structural diagram of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0031] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0032] An anti-tampering damage assessment image traceability method provided by an embodiment of the present invention can be applied in an application environment such as Figure 1 . Among them, the client communicates with the server through the network. The server can obtain the global positioning data, base station positioning data, and device sensor data of the damage assessment image; perform fusion processing on the global positioning data, base station positioning data, and device sensor data to obtain a dynamic timestamp; combine a preset device hardware fingerprint and a preset sensor fingerprint to generate a composite fingerprint; perform asymmetric encryption binding on the dynamic timestamp and the composite fingerprint to obtain encrypted binding data; perform multiple hashing processing on the damage assessment image to obtain hash chain data; trace the damage assessment image according to the encrypted binding data and the hash chain data to obtain a traceability result, and feedback the traceability result to the client. The present invention provides an anti-tampering damage assessment image traceability device. For the traceability result service, through the traceability shooting information, it can prevent the phenomenon that the operator does not arrive at the scene for investigation and evidence collection and is taken by others on behalf, thereby reducing the leakage risk of cases and helping to reduce the operating costs of the company. Among them, the client can be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers, and portable wearable devices. The server can be implemented by an independent server or a server cluster composed of multiple servers. The present invention will be described in detail through specific embodiments below.

[0033] Please refer to Figure 2 as shown in Figure 2 which is a schematic flowchart of an anti-tampering damage assessment image traceability method provided by an embodiment of the present invention, and includes the following steps:

[0034] S1. Obtain the global positioning data, base station positioning data, and device sensor data of the damage assessment image.

[0035] In the embodiment of the present invention, the "obtaining" refers to collecting, extracting, or receiving multi-dimensional positioning and sensor information related to the damage assessment image through technical means.

[0036] Specifically, the loss assessment images are usually taken by devices with GPS functions, such as smartphones, cameras, etc. When taking pictures, the device will automatically embed GPS information into the metadata of the image, and this information can be obtained through image viewing software or professional metadata viewing tools; some mobile phone manufacturers or third-party location service platforms provide location data query services. If the loss assessment image taking device uses the services of these platforms and enables the corresponding location function and data recording in the device settings, then the base station location data corresponding to the shooting time can be found by logging in to the corresponding platform account and searching in the relevant location records or location history; the device sensor data is similar to the global positioning data. Some devices will record sensor data in the metadata of the image when taking pictures. For example, the photos taken by smartphones may contain data from acceleration sensors, gyro sensors, etc. Special metadata parsing tools can be used to extract this information.

[0037] In the fintech scenario, through the global positioning data of the loss assessment images, financial institutions can understand the geographical location of the insured objects, evaluate the natural risks (such as high-incidence areas of disasters like floods and earthquakes) and social risks (such as areas with poor public security) they face, so as to formulate insurance rates and underwriting policies more accurately, reasonably control risks. By confirming whether the base station location where the customer's device is located is consistent with the location claimed by the customer, the reliability of identity verification can be increased, preventing identity theft or fraud. At the same time, by analyzing the device sensor data, financial institutions can understand the customer's behavior habits and lifestyle, such as consumption habits, sports preferences, etc., provide personalized financial product recommendations and services for customers, and also help evaluate the customer's credit risk and insurance needs.

[0038] S2. Perform fusion processing on the global positioning data, base station location data, and device sensor data to obtain a dynamic timestamp.

[0039] In the embodiment of the present invention, the fusion processing refers to integrating, calibrating, and correlating multi-source heterogeneous positioning and sensor data through algorithms and technical means to generate high-precision and highly reliable spatio-temporal tags (dynamic timestamps).

[0040] Specifically, unify the time reference and spatial reference system of multi-source data, where the multi-source data refers to global positioning data, base station location data, and device sensor data. Utilize the advantages of different data (such as the absolute accuracy of GPS, the coverage of base stations, and the real-time performance of sensors), and based on time series and spatial relationships, correct error or conflicting data, and finally output a dynamic timestamp, that is, a composite tag containing accurate time, spatial coordinates, and credibility scores.

[0041] Meanwhile, by integrating various positioning data and device sensor data and combining with dynamic timestamps, user identity can be verified more comprehensively. By analyzing the location changes, usage habits, and time information of the user's device, a user behavior profile can be established. For example, dynamic timestamps can record the time sequence and intervals of a user's financial transactions at different locations. If there are abnormal location jumps or operations that do not conform to the user's regular behavior pattern, the system can issue a warning in a timely manner to prevent identity theft and fraudulent transactions.

[0042] In the embodiments of the present invention, the fusion processing of the global positioning data, base station positioning data, and device sensor data to obtain a dynamic timestamp includes:

[0043] Feature extraction is respectively performed on the global positioning data, base station positioning data, and device sensor data to obtain global feature data, base station feature data, and device feature data;

[0044] Weighted average fusion is performed on the global feature data, base station feature data, and device feature data to obtain fusion data;

[0045] A dynamic timestamp is generated according to the combination of the fusion data and a preset time interval.

[0046] In the embodiments of the present invention, the feature extraction refers to extracting representative and discriminative features from the original data for further data analysis, modeling, prediction, and other tasks. The weighted average fusion refers to the process of obtaining a comprehensive data result by weighted averaging the global feature data, base station feature data, and device feature data according to certain weights. The combination generation refers to combining the fusion data with a preset time interval to generate a timestamp reflecting dynamic time information.

[0047] Specifically, first, position information including basic coordinate data such as longitude, latitude, and altitude is extracted from the global positioning data. These data can directly reflect the specific position of the device or target object on the earth. Then, the rate of change of the position, that is, the velocity information, including horizontal velocity and vertical velocity (if there is altitude change), is calculated. The velocity can be obtained by dividing the position difference between consecutive time points by the time interval. For example, two position coordinates (x1, y1, z1), (x2, y2, z2) are obtained at time t1 and t2, and the time interval is Δ t = t2 - t1, then the horizontal velocity The vertical velocity v z = (z2 - z1) / Δ t , and at the same time, direction information such as the heading angle, that is, the direction of movement of the device or target object, can also be extracted.

[0048] Specifically, from the base station location data, first extract the identification information of the base station, such as the base station number, name, etc., which is used to uniquely identify the base station coverage area where the device is located; then obtain the signal strength information, that is, the strength values of the signals received by the device from each base station. The signal strength can reflect the distance relationship between the device and the base station. Generally speaking, the stronger the signal strength, the closer the device is to the base station; calculate the relative distance between the device and the base station. Although the base station location data usually cannot directly provide accurate distance information, the approximate distance between the device and the base station can be estimated through factors such as the signal propagation model and signal strength. For example, according to the attenuation law of the signal strength, combined with known parameters such as the base station transmission power, use relevant wireless propagation models (such as free space propagation model, lognormal shadow model, etc.) to calculate the distance.

[0049] Furthermore, for device sensor data, if the sensor is an acceleration sensor, it is necessary to extract the magnitude and direction information of the acceleration. The acceleration magnitude can be directly obtained from the sensor readings, and the direction can be determined by the coordinate axis direction of the sensor and the positive and negative of the readings; for example, in a three-dimensional space, the acceleration sensor can provide the acceleration components a x ,a y ,a z in the x, y, and z directions. The acceleration magnitude If it is a gyroscope sensor, extract the angular velocity information, including the angular velocity components around each coordinate axis, which can reflect the rotation state and rotation speed of the device. If it is a pressure sensor, extract the pressure value and its change rate. The pressure value is directly obtained from the sensor, and the change rate can be calculated by dividing the pressure difference between adjacent time points by the time interval, which is used to reflect the dynamic change of the pressure.

[0050] Furthermore, according to factors such as the importance and reliability of the global positioning data, base station location data, and device sensor data, assign corresponding weights to the characteristics of each type of data. For example, if in a specific application scenario, the global positioning data has high accuracy and a large impact on the results, a relatively high weight w1 can be assigned to it; although the base station location data has relatively low accuracy, it can provide auxiliary information in some cases, and a weight w2 can be assigned; the device sensor data can reflect the real-time state of the device and is also very important for the overall analysis, and a weight w3 is assigned. The value range of the weight is usually between 0 and 1, and w1 + w2 + w3 = 1. The determination of the weight can be through empirical methods, data analysis, or machine learning; perform weighted average calculation on the extracted global feature data, base station feature data, and device feature data. Assume that the global feature data is G, the base station feature data is B, and the device feature data is E, then the fused data F can be calculated by the following formula: F = w1G + w2B + w3E.

[0051] Specifically, according to specific application requirements and data update frequency requirements, a fixed time interval Δ is preset. t , for example, in the scenario of financial transaction risk monitoring, a relatively high time precision may be required, and the time interval is set to 1 minute or shorter; while in some scenarios with relatively low requirements for time precision, such as long-term behavior analysis in the insurance business, the time interval can be set to 1 hour or longer. Starting from a certain initial time point t0, according to the preset time interval Δ t , the fused data is sampled and recorded in sequence, and the fused position information, speed information, device status information, etc. at that time are recorded together with the time to form a dynamic timestamp containing rich information, so as to track and analyze the comprehensive state changes of the target object at different times.

[0052] In the embodiments of the present invention, higher weights are given to data with high precision and strong reliability, so that they play a dominant role in the fusion result. For data with relatively low precision or certain errors, the influence on the result is reduced through reasonable weight allocation, thereby improving the quality of the overall data. At the same time, the fused data is combined with the preset time interval to generate a dynamic timestamp, which can clearly record the change of the fused data over time and form a time series.

[0053] In the embodiments of the present invention, the fused dynamic timestamp data contains multi-source information, providing richer materials for data analysis. By mining and analyzing these data, more valuable information can be discovered, such as users' behavior patterns, consumption habits, location preferences, etc. These information can help financial institutions better understand users, optimize products and services, and improve market competitiveness.

[0054] S3. Combine the preset device hardware fingerprint and the preset sensor fingerprint to generate a composite fingerprint.

[0055] In the embodiments of the present invention, the "combination generation" refers to merging and integrating the preset device hardware fingerprint and the preset sensor fingerprint through a specific method or algorithm to form a new fingerprint with composite characteristics.

[0056] Specifically, the device hardware fingerprint is a unique identifier generated based on the hardware characteristics of the device, such as the device model, serial number, processor information, etc., which can be used to identify the identity of the device. The sensor fingerprint is generated according to the unique characteristics of the sensors in the device, such as the accuracy, noise level, response time of the sensors, etc., which can reflect the individual differences of the sensors. By performing operations such as fusion, encoding, or encryption on the information of these two fingerprints, a composite fingerprint can be generated.

[0057] In the financial scenario, there are various fraud behaviors in the field of fintech, such as false transactions, card skimming, etc.; the composite fingerprint can be used as an important basis for judging whether a transaction is abnormal. If it is found that a certain transaction comes from a strange composite fingerprint device, or there are abnormal changes in the sensor fingerprint of the device, the system can issue an alarm in time for further risk assessment and verification, effectively preventing fraud behaviors.

[0058] In the embodiment of the present invention, the combination of the preset device hardware fingerprint and the preset sensor fingerprint to generate a composite fingerprint includes:

[0059] Extract the device identification code from the preset device hardware fingerprint, and extract the device identification code according to the preset number of digits to obtain a basic identifier;

[0060] Extract the features of the preset sensor fingerprint to obtain the sensor fingerprint features, and encode the sensor fingerprint features to obtain a feature code;

[0061] Perform an exclusive OR operation on the basic identifier and the feature code to obtain a composite fingerprint.

[0062] In the embodiment of the present invention, the extraction refers to the process of separating and obtaining a specific device identification code from the preset device hardware fingerprint, the feature extraction refers to the process of identifying and extracting key information that can represent the unique attributes and features of the sensor from the preset sensor fingerprint, the encoding refers to the process of converting the extracted sensor fingerprint features into a specific format of code, and the exclusive OR operation refers to the process of performing a logical operation on the basic identifier and the feature code.

[0063] Specifically, first, a specific parsing algorithm or tool needs to be used to find the position and representation method of the device identification code according to the format and structure of the device hardware fingerprint. After determining the device identification code, extract it according to the preset number of digits. For example, if the preset number of digits is 8 bits and the device identification code is a 16-bit string, 8 bits of characters may be extracted starting from the 5th bit as the basic identifier. This step can determine the extraction start position and number of digits according to specific requirements and designs to ensure that the extracted basic identifier has sufficient representativeness and uniqueness.

[0064] Furthermore, the sensor fingerprint contains characteristic data of the sensor under various operating conditions, such as accuracy, noise level, response time, etc. Through data analysis and processing techniques, key features that can represent the unique attributes of the sensor are selected from these large amounts of data. For example, signal processing algorithms are used to analyze the output signal of the sensor to find characteristic peaks or noise patterns within a specific frequency range as features. The extracted sensor fingerprint features are converted into a code in a specific format, which usually involves using encoding algorithms to map the feature data into feature codes in the form of digital sequences, strings, or binary codes. For example, a hash algorithm is used to generate a fixed-length hexadecimal string as the feature code from the combination of feature parameters, facilitating computer storage, transmission, and processing.

[0065] Specifically, the basic identifier and the feature code are subjected to an exclusive OR operation. Since the basic identifier and the feature code are usually represented in binary form, the exclusive OR operation operates on their corresponding binary bits. According to the exclusive OR operation rule, when the values of two binary bits are different, the result is 1; when they are the same, the result is 0. The exclusive OR operation is performed on each bit of the basic identifier and the feature code, and finally a new binary number is obtained, which is the composite fingerprint.

[0066] In the embodiments of the present invention, the device hardware fingerprint contains rich device hardware information. Extracting the device identification code therein can accurately obtain the key part that uniquely identifies the device, which helps to accurately distinguish different individuals among numerous devices and provides a core basis for subsequent operations such as device management, identification, and authentication.

[0067] In the embodiments of the present invention, in some complex application scenarios, such as scenarios where multiple devices and sensors work together in an Internet of Things environment, a single device hardware fingerprint or sensor fingerprint may not be able to meet the requirements of accurate identification and management. The composite fingerprint can comprehensively consider the information of devices and sensors, better adapt to this complex environment, achieve precise positioning, management, and control of devices and sensors, and improve the operation efficiency and stability of the entire system.

[0068] S4. Asymmetrically encrypt and bind the dynamic timestamp and the composite fingerprint to obtain encrypted binding data.

[0069] In the embodiments of the present invention, the asymmetric encryption binding refers to a technical means of associating and encrypting and protecting the dynamic timestamp and the composite fingerprint using an asymmetric encryption algorithm.

[0070] Specifically, first, the public key of the receiving party is used to encrypt the dynamic timestamp and the composite fingerprint, and the encrypted dynamic timestamp and composite fingerprint are combined together to form a tightly associated data structure. This binding ensures the corresponding relationship between the two and prevents the data from being tampered with or replaced.

[0071] In the fintech scenario, the dynamic timestamp ensures the real-time nature of authentication. In financial transactions, the identity and device status of users may change at any time. By cryptographically binding the dynamic timestamp and the composite fingerprint, financial institutions can verify the identity and device information of users in real time, detect abnormal login or transaction behaviors in a timely manner, improve the timeliness and accuracy of identity authentication, and safeguard the security of users' funds.

[0072] In an embodiment of the present invention, the asymmetric encryption binding of the dynamic timestamp and the composite fingerprint to obtain encrypted binding data includes:

[0073] Encrypting the dynamic timestamp and the composite fingerprint respectively according to a preset public key to obtain an encrypted timestamp and an encrypted composite fingerprint;

[0074] Binding the encrypted timestamp and the encrypted composite fingerprint to form encrypted binding data.

[0075] In an embodiment of the present invention, the encryption refers to the process of converting data into a form that is difficult to understand or read, and the binding refers to associating the encrypted timestamp and the encrypted composite fingerprint in a specific manner to form an inseparable overall data structure, so as to ensure the correlation and integrity of these two encrypted data in subsequent use.

[0076] Specifically, the public key can be obtained from a key management system, a certificate authority, a local key storage, a configuration file. Ensure that you have obtained the correct preset public key. The public key is usually stored in a specific format, such as PEM (Privacy-Enhanced Mail Format), DER (Binary Format), etc. Subsequently, common asymmetric encryption algorithms include RSA (Rivest-Shamir-Adleman), ECC (Elliptic Curve Cryptography), etc. Select a suitable encryption algorithm and prepare the corresponding encryption library or tool according to the requirements of the algorithm.

[0077] Furthermore, convert the dynamic timestamp into a format suitable for encryption, usually a byte string, and encrypt the dynamic timestamp using the preset public key and the selected encryption algorithm; the encryption process will perform a series of mathematical operations on the data according to the rules of the algorithm to generate the encrypted ciphertext. Similarly, convert the composite fingerprint into a byte string format, and encrypt the composite fingerprint using the same preset public key and encryption algorithm to obtain the encrypted composite fingerprint; concatenate the encrypted timestamp and the encrypted composite fingerprint in a certain order, or store them in a data structure (such as a JSON object, an XML document, etc.). Concatenate the byte strings of the encrypted timestamp and the encrypted composite fingerprint in order to form a new byte string as the encrypted binding data.

[0078] In the embodiments of the present invention, the dynamic timestamp records the specific time when the transaction occurs. The composite fingerprint may contain information such as user identity characteristics and transaction characteristics. Through encryption, even if the data is intercepted during transmission or storage, attackers cannot obtain the real content therein. The preset public key is used to encrypt the data, and only the holder of the corresponding private key can decrypt it. In financial transactions, this can be used to verify the identities of both parties to the transaction. The encryption binding technology ensures that the correlation between the two cannot be easily tampered with or destroyed, which further enhances the security of the data and prevents attackers from separating or replacing one of the data to commit fraud or undermine the integrity of financial transactions.

[0079] In the embodiments of the present invention, the asymmetric encryption binding can flexibly adjust the encryption strategy and key management method according to different business requirements, and is more applicable in financial scenarios of multi-party collaboration, such as cross-institutional transactions, supply chain finance, etc. Multiple institutions can use the same public key to encrypt and bind relevant data, and only specific authorized institutions can decrypt and process this data, realizing the secure sharing and collaboration of data.

[0080] S5. Perform multiple hashing operations on the loss assessment image to obtain hash chain data.

[0081] In the embodiments of the present invention, the multiple hashing operation refers to the process of sequentially performing multiple hashing operations on the loss assessment image, using the result of each operation as the input for the next operation, and finally forming a hash chain data.

[0082] Specifically, select a suitable hashing algorithm and perform the first hashing operation on the original loss assessment image data to obtain a hash value of a fixed length. Use the hash value obtained from the first hashing operation as the input and perform the second hashing operation again using the same or different hashing algorithm to obtain the second hash value. Repeat the above steps and perform multiple hashing operations as needed. Each time, use the previous hash value as the input for the next operation, and so on, finally forming a sequence composed of multiple hash values, that is, hash chain data.

[0083] At the same time, the loss assessment images in financial transactions (such as vehicle damage images in insurance claims, disaster site images in property insurance, etc.) are important evidence and basis. The hash chain data generated by multiple hashing operations provides strong integrity protection for these images. Any modification to the image will cause a significant change in the hash value, so that it can be detected in time whether the data has been maliciously tampered with, ensuring the authenticity and reliability of transaction data and preventing fraud.

[0084] In the embodiments of the present invention, the step of performing multiple hashing operations on the loss assessment image to obtain hash chain data includes:

[0085] Perform a secure hash operation on the loss assessment image to obtain a main hash value;

[0086] Perform a hash operation on the loss assessment image to obtain an auxiliary hash value;

[0087] Generate hash chain data by combining the main hash value and the auxiliary hash value through a preset Merkle tree structure.

[0088] Specifically, secure hash operations typically use secure hash algorithms such as SHA-256 (Secure Hash Algorithm - 256 bits). That is, the binary data of the loss assessment image is used as input, and the algorithm performs a series of complex mathematical operations on the image data, including grouping, shifting, XOR, etc. These operations scramble and compress the image data, and finally generate a hash value of a fixed length (such as 256 bits), which is the main hash value. It has uniqueness, that is, it is almost impossible for different loss assessment images to generate the same main hash value, and any minor change to the image data will result in a significant change in the main hash value.

[0089] In detail, the hash operation also adopts the principle of a similar hash algorithm. Similarly, the binary data of the loss assessment image is used as input, and the image data is processed through a specific hash function. After a series of calculations, another hash value of a fixed length is generated, that is, the auxiliary hash value. It also has the general characteristics of a hash value, can reflect the characteristics of the image data to a certain extent, and is independent of the main hash value, "abstracting" the image data from different angles.

[0090] Furthermore, a Merkle tree is a tree-shaped data structure. Its leaf nodes are the main hash value and the auxiliary hash value. First, the main hash value and the auxiliary hash value are used as two leaf nodes respectively. Then, the hash values of these two leaf nodes are combined (usually by concatenating them and then performing another hash operation) to obtain the hash value of their parent node; if there are other pairs of main hash values and auxiliary hash values, they are also combined into the hash values of their respective parent nodes in the same way. Then, continue to combine these parent node hash values in pairs to generate the hash values of higher-level parent nodes, and so on recursively upward until the hash value of the root node is generated; the series of hash values from the leaf nodes to the root node constitutes the hash chain data. In this process, the Merkle tree structure enables the hash chain data to have efficient verification and traceability functions. Any change in the hash value of a leaf node will cause a change in the hash value of the root node, so it can quickly detect whether the loss assessment image has been tampered with.

[0091] In the embodiments of the present invention, two different hash operations are used to generate the main hash value and the auxiliary hash value, which is equivalent to adding an extra layer of protection for data security. Even if one hash algorithm is cracked or has vulnerabilities, the other hash value can still provide a certain degree of security protection, increasing the difficulty for attackers to forge or tamper with data. Each hash value in the hash chain data is associated with the previous hash value, forming a complete chain. Through this chain, all the operations and change records of the image from its initial generation to the current state can be traced. In scenarios such as insurance loss assessment, this helps auditors understand the processing process of the image, ensures the compliance and traceability of the loss assessment process, and prevents fraud.

[0092] In the embodiments of the present invention, the hash chain forms a complete chain structure, recording all the operation traces of the image from the initial state to the current state. When it is necessary to trace and review the loss assessment process, the processing flow and historical records of the image can be clearly understood through the hash chain, which helps to discover potential violations and ensure the fairness and transparency of the loss assessment process.

[0093] S6. Trace the loss assessment image based on the encrypted binding data and the hash chain data to obtain a traceability result.

[0094] In the embodiments of the present invention, the traceability refers to analyzing the encrypted binding data and the hash chain data to track the entire process of the loss assessment image from generation to the current state, including information such as image acquisition, transmission, storage, processing, and the records and order of related operations.

[0095] Specifically, through specific technologies and algorithms, using the information contained in the encrypted binding data and the hash chain data, to track and restore the entire life cycle process of the loss assessment image, and finally obtain the detailed historical records and related status information of the image.

[0096] In the embodiments of the present invention, the tracing the loss assessment image based on the encrypted binding data and the hash chain data to obtain a traceability result includes:

[0097] Write the encrypted binding data and the hash chain data into the blockchain node, and read the encrypted binding data and the hash chain data respectively through the blockchain node to obtain the read encrypted binding data and hash chain data;

[0098] Perform decryption judgment on the read encrypted binding data to obtain a first judgment result;

[0099] Perform verification judgment on the read hash chain data to obtain a second judgment result;

[0100] Perform traceability judgment on the first judgment result and the second judgment result to obtain a traceability result.

[0101] In the embodiments of the present invention, the writing refers to the process of recording the encrypted binding data and the hash chain data into the blockchain node, which means that these data are added to the distributed ledger of the blockchain. The reading refers to the operation of obtaining the encrypted binding data and the hash chain data from the blockchain node for subsequent analysis, verification or other processing of these data. The decryption judgment refers to the process of decrypting the encrypted binding data after reading using the corresponding decryption algorithm and key, and making relevant judgments based on the decryption result. The verification judgment refers to the process of performing a series of inspections and verifications on the read hash chain data to determine its integrity, accuracy and consistency with the original data. The traceability judgment refers to comprehensively considering the first judgment result (the judgment result of the decrypted encrypted binding data) and the second judgment result (the verification judgment result of the read hash chain data) to determine information such as the source, historical operation records and integrity of the loss assessment image, so as to obtain an accurate traceability result regarding the entire life cycle of the loss assessment image.

[0102] Specifically, the encrypted binding data related to the loss assessment image (including information such as the source identifier of the image, the acquisition time, the acquisition device, etc.) and the hash chain data (composed of a series of hash values obtained by performing multiple hash processing on the loss assessment image) are sorted and formatted to conform to the data format required by the blockchain node. According to the consensus mechanism adopted by the blockchain (such as proof of work, proof of stake, etc.), the node needs to complete corresponding computing tasks or meet certain conditions to obtain the permission to write data into the blockchain. When data needs to be read, the relevant application program or user sends a read request to the blockchain node, specifying the relevant identifiers of the encrypted binding data and the hash chain data to be read (such as block height, transaction hash, etc.). After receiving the request, the blockchain node searches in the local blockchain database according to the identifier information in the request. It traverses the blocks of the blockchain, finds the block containing the target data, and extracts the encrypted binding data and the hash chain data therein. The node returns the found data to the requester to obtain the encrypted binding data and the hash chain data after reading.

[0103] Further, use the decryption algorithm and key corresponding to the encryption process to decrypt the encrypted binding data after reading, restore it to the original plaintext form, and compare the decrypted data with the known original data features or relevant records to verify whether it is consistent with the data at the time of original generation and whether there are signs of tampering. For example, it is possible to compare whether the key information in the data (such as the collection time, device identifier, etc.) is consistent with the original record, and check whether the content of the decrypted data complies with the regulations according to the business rules and relevant regulatory requirements; for example, check whether the collection time is within the specified range and whether the collection device meets the standards, etc. If the data is complete, true, and compliant, the first judgment result is passed; otherwise, it is not passed, and the corresponding problem information is recorded. Based on the results of the above various judgments, the first judgment result is obtained.

[0104] Further, starting from the initial hash value of the hash chain, calculate each hash value in turn according to the generation rule of the hash chain, and compare it with the corresponding hash value in the read hash chain. Due to the determinism and uniqueness of the hash function, the same input should produce the same hash value, different inputs will produce different hash values, and the hash value is irreversible. If the calculated hash value is inconsistent with the read hash value, it indicates that the hash chain data may have been tampered with, or an error occurred during storage or transmission. Check whether the link relationship between the hash values in the hash chain is correct, that is, whether each hash value is correctly generated based on the previous hash value. If it is found that the link relationship between a certain hash value in the hash chain and the hash values before and after it does not conform to the generation rule of the hash chain, it indicates that the hash chain may be broken or artificially modified. Based on the above verification situations, re-hash the original loss assessment image and compare the obtained hash value with the final hash value in the hash chain. If the two are consistent, it indicates that the hash chain data can accurately reflect the characteristics of the original image and no substantial changes have occurred during the processing and transmission of the image; if they are inconsistent, it indicates that the original image may have been modified, or there is an error in the hash chain data, and the second judgment result is given.

[0105] Compare whether the information obtained by decryption in the first judgment result (such as the collection time, location, device, etc. of the loss assessment image) is consistent with the image characteristics and historical information implied by the verification through the hash chain in the second judgment result. Based on the first judgment result and the second judgment result, evaluate the credibility and integrity of the loss assessment image as evidence or a basis for business processing.

[0106] In the embodiment of the present invention, the verification and judgment of the read hash chain data to obtain the second judgment result includes:

[0107] Calculate the hash values in the read hash chain data one by one;

[0108] Compare the calculated hash value with the corresponding hash value in the hash chain data;

[0109] If the calculated hash value is the same as the corresponding hash value in the hash chain data, confirm that the second judgment result is that the loss assessment image has not been tampered with;

[0110] If the calculated hash value is different from the corresponding hash value in the hash chain data, confirm that the second judgment result is that the loss assessment image has been tampered with.

[0111] In an embodiment of the present invention, the calculation refers to the process of performing an operation on relevant information in the hash chain data according to a specific hash algorithm to generate a hash value, and the comparison refers to comparing the calculated hash value with the corresponding pre-stored hash value in the hash chain data one by one to determine whether the two are exactly the same.

[0112] Specifically, the generation of the hash chain data is based on a specific hash algorithm, such as SHA-256, MD4, MD5, SM3, etc. The hash chain consists of a series of hash values, and the calculation of each hash value depends on specific input data. Starting from the beginning of the hash chain or layer by layer upwards, according to the construction logic of the hash chain, the selected hash algorithm is applied to each input data in turn for calculation. For example, if the nth hash value in the hash chain is obtained by performing a hash operation on the combination of the nth part of the loss assessment image data and the (n - 1)th hash value, then each hash value needs to be calculated according to this rule. The calculated hash value and the corresponding original hash value in the hash chain are usually binary or hexadecimal strings of a fixed length. Starting from the first bit of the string, each bit is compared to determine whether each bit is the same. After the bit-by-bit comparison is completed, only when each bit of the calculated hash value is exactly the same as the corresponding original hash value in the hash chain is it considered that the two hash values match; as long as there is one bit different, it is determined as a mismatch.

[0113] Furthermore, if the calculated hash value is exactly the same as the corresponding hash value in the hash chain data, this indicates that during the entire process recorded by the hash chain, the data of the loss assessment image has not changed at all. If there is a difference between the calculated hash value and the corresponding hash value in the hash chain data, this means that the data of the loss assessment image has been modified at a certain link in the hash chain.

[0114] In an embodiment of the present invention, after tracing the loss assessment image according to the encrypted binding data and the hash chain data to obtain a tracing result, the method further includes:

[0115] Obtain the ID of the loss assessment image and construct a two-dimensional code according to the ID;

[0116] Combine the two-dimensional code and the loss assessment image to generate a two-dimensional code image;

[0117] Trace the loss assessment image according to the two-dimensional code image to obtain a fuzzy tracing result.

[0118] In the embodiments of the present invention, the "acquisition" refers to finding and extracting the unique identifier of the loss assessment image from a relevant system, database or storage medium. The "construction" refers to the process of encoding the obtained loss assessment image ID into a two-dimensional code using a dedicated two-dimensional code generation algorithm and tool. The "combined generation" refers to combining the previously constructed two-dimensional code with the loss assessment image into a new image through certain technical means. The "traceability" refers to the process of using the information contained in the two-dimensional code (i.e., the ID of the loss assessment image) in the generated two-dimensional code image to track and obtain relevant historical information, source information or other relevant data of the loss assessment image.

[0119] Specifically, when taking a picture, bind the current shooting time, current shooting location, current App login account information, current device information, the width-to-height ratio of the photo, etc. to an ID (the ID is generated in real time according to the shooting time, login account and some other information), add this ID as a watermark to the final picture, and generate a two-dimensional code for this ID and add it to the final photo.

[0120] Further, upload the ID and the corresponding information to the server (this step will also bring the current mobile phone time for calculating the time difference to prevent the mobile phone time from being inaccurate and used for comparing and correcting with the photo time). When the network environment is poor, it will be cached on the mobile phone and automatically retry uploading after the network is restored, so that it can be unaffected by the network environment at the case scene.

[0121] Subsequently, when the operator processes the corresponding case, in the photo collection link, directly select the already taken photo from the mobile phone album (or the photo taken through the new scheme collected by other methods). When uploading to the server, first parse the two-dimensional code to obtain the unique ID, and pass the ID to the server when uploading the picture. After the server gets the ID, it will then obtain the corresponding shooting-related information and fill it into the case, so that the shooting information can be traced and the problem of the operator not arriving at the scene or allowing others to take the photo on their behalf can be prevented.

[0122] In the embodiments of the present invention, the "obtaining the ID of the loss assessment image" includes:

[0123] Use a preset image software to query the shooting time of the loss assessment image;

[0124] Obtain the shooting location of the loss assessment image through the Global Positioning System;

[0125] Obtain the shooting account information of the loss assessment image;

[0126] Combine the shooting time, shooting location, and shooting account information to generate an ID.

[0127] In the embodiments of the present invention, open a preset image software, which should have the function of reading image metadata, such as professional image editing software like Adobe Photoshop and Lightroom, or some specialized image viewing tools. Import the loss assessment image into the software, find the metadata information of the image, find the field related to the shooting time, which is usually marked as "shooting time", "creation date", or similar expressions, record this time information, confirm that the GPS function of the shooting device is enabled during shooting, obtain the permission to obtain location information, get the shooting location of the loss assessment image, and determine the preset user authentication method adopted in the APP or system for shooting pictures. Common ones include account password login, mobile phone number verification code login, and third-party platform authorization login (such as WeChat, Alipay, etc.). If it is through the account password login or mobile phone number verification code login method, the APP usually records the user's login status and relevant account information in the local storage or the server-side database. Developers can query the local storage or interact with the server-side to obtain the account information of the logged-in user according to the context information of the current application. If it is a third-party platform authorization login, the APP will obtain the user information returned by the third-party platform when the user authorizes the login, including the unique identifier, nickname, etc.

[0128] Further, perform formatting processing on the shooting time, organize the shooting location information, ensure the uniqueness and accuracy of the shooting account information, and combine the shooting time, shooting location, and shooting account information together according to certain rules. For example, specific delimiters (such as "-" or "_") can be used to connect them to form a string. Suppose the shooting time is "2025-04-11 10:30:00", the shooting location is "XX Road, XX District, Hefei City, Anhui Province", and the shooting account information is "user123", then the combined ID may be "20250411103000_XX Road, XX District, Hefei City, Anhui Province_user123".

[0129] In addition, after logging in to the claims settlement operation system, the user information of the user (bound account, institutional information, login time, etc.) will be retained. At the same time, an icon for the operation camera is opened on the mobile phone desktop, similar to the icon of an independent application. With this icon, there is no need to enter the operation system to find the camera entry. By directly clicking the camera icon on the mobile phone desktop, the camera shooting page can be directly entered, which can reach quickly just like the built-in camera of the mobile phone. In this way, the problem that the original camera requires multiple operations to take pictures is solved, and pictures can be taken even without a network signal. Previously, when there was no network signal, it was impossible to enter the case page and thus unable to enter the camera for shooting operations.

[0130] In the embodiment of the present invention, the hash chain data is generated based on data such as loss assessment images. By verifying and judging the hash chain data, it can be ensured that the loss assessment images have not been tampered with during the entire process from shooting to storage and then to reading. By verifying the hash chain, the historical records of the loss assessment images can be traced, including the generation time, modification records, etc. of the images, which provides an important basis for subsequent traceability judgment, helps to determine the source and circulation process of the images, and provides strong evidence for possible disputes or problems.

[0131] In the embodiment of the present invention, the encrypted binding data ensures the authenticity and integrity of the data through encryption technology. The hash chain data utilizes the characteristics of the hash algorithm, so that any minor modification to the loss assessment image will cause a change in the hash value. By tracing the two, it can be accurately judged whether the loss assessment image has been tampered with during the generation, storage, and transmission processes, ensuring that the loss assessment image can truly reflect the situation of the accident scene.

[0132] It can be seen that in the above solution, for the traceability result service, the global positioning data, base station positioning data, and device sensor data of the loss assessment image are obtained; the global positioning data, base station positioning data, and device sensor data are fused to obtain a dynamic timestamp; a composite fingerprint is generated by combining a preset device hardware fingerprint and a preset sensor fingerprint; the dynamic timestamp and the composite fingerprint are asymmetrically encrypted and bound to obtain encrypted binding data; the loss assessment image is subjected to multiple hash processing to obtain hash chain data; the loss assessment image is traced according to the encrypted binding data and the hash chain data to obtain a traceability result. By tracing the shooting information, it can prevent the phenomenon that the operation personnel do not reach the scene for investigation and evidence collection and are photographed by others on their behalf, thereby reducing the leakage risk of cases and helping to reduce the operating costs of the company.

[0133] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0134] In one embodiment, a tamper-proof loss assessment image traceability device is provided, and this tamper-proof loss assessment image traceability device corresponds one-to-one with the tamper-proof loss assessment image traceability method in the above embodiment. As Figure 3 shown, this tamper-proof loss assessment image traceability device includes an acquisition module 101, a processing module 102, a generation module 103, a binding module 104, a hash processing module 105, and a traceability module 106. The detailed description of each functional module is as follows:

[0135] The acquisition module 101 is used to acquire the global positioning data, base station positioning data, and device sensor data of the loss assessment image;

[0136] The processing module 102 is used to perform fusion processing on the global positioning data, base station positioning data, and device sensor data to obtain a dynamic timestamp;

[0137] The generation module 103 is used to combine a preset device hardware fingerprint and a preset sensor fingerprint to generate a composite fingerprint;

[0138] The binding module 104 is used to perform asymmetric encryption binding on the dynamic timestamp and the composite fingerprint to obtain encrypted binding data;

[0139] The hash processing module 105 is used to perform multiple hash processing on the loss assessment image to obtain hash chain data;

[0140] The traceability module 106 is used to trace the loss assessment image according to the encrypted binding data and the hash chain data to obtain a traceability result.

[0141] In one embodiment, when the processing module 102 performs fusion processing on the global positioning data, base station positioning data, and device sensor data to obtain a dynamic timestamp, it is used for:

[0142] Extract the feature data of the global positioning data, base station positioning data, and device sensor data respectively to obtain global feature data, base station feature data, and device feature data;

[0143] Perform weighted average fusion on the global feature data, base station feature data, and device feature data to obtain fusion data;

[0144] Generate a dynamic timestamp according to the combination of the fusion data and a preset time interval.

[0145] In one embodiment, when the generation module 103 combines a preset device hardware fingerprint and a preset sensor fingerprint to generate a composite fingerprint, it is used for:

[0146] Extract the device identification code in the preset device hardware fingerprint, and extract the device identification code according to a preset number of digits to obtain a basic identifier;

[0147] Extract features from the preset sensor fingerprint to obtain sensor fingerprint features, and encode the sensor fingerprint features to obtain a feature code;

[0148] Perform an exclusive OR operation on the basic identifier and the feature code to obtain a composite fingerprint.

[0149] In one embodiment, when the binding module 104 performs asymmetric encryption binding on the dynamic timestamp and the composite fingerprint to obtain encrypted binding data, it is used for:

[0150] Encrypt the dynamic timestamp and the composite fingerprint respectively according to the preset public key to obtain an encrypted timestamp and an encrypted composite fingerprint;

[0151] Bind the encrypted timestamp and the encrypted composite fingerprint to form encrypted binding data.

[0152] In one embodiment, when the hash processing module 105 performs multiple hash processing on the loss assessment image to obtain hash chain data, it is used for:

[0153] Perform a secure hash operation on the loss assessment image to obtain a main hash value;

[0154] Perform a hash hash operation on the loss assessment image to obtain a secondary hash value;

[0155] Combine the main hash value and the secondary hash value through a preset Merkle tree structure to generate hash chain data.

[0156] In one embodiment, when the tracing module 106 traces the loss assessment image according to the encrypted binding data and the hash chain data to obtain a tracing result, it is used for:

[0157] Write the encrypted binding data and the hash chain data into the blockchain node, and read the encrypted binding data and the hash chain data respectively through the blockchain node to obtain the read encrypted binding data and the hash chain data;

[0158] Perform decryption judgment on the read encrypted binding data to obtain a first judgment result;

[0159] Perform verification judgment on the read hash chain data to obtain a second judgment result;

[0160] Perform tracing judgment on the first judgment result and the second judgment result to obtain a tracing result.

[0161] In one embodiment, when performing verification judgment on the read hash chain data to obtain a second judgment result, it is used for:

[0162] Calculate the hash values in the read hash chain data one by one;

[0163] Compare the calculated hash values with the corresponding hash values in the hash chain data;

[0164] If the calculated hash values are the same as the corresponding hash values in the hash chain data, confirm that the second judgment result is that the loss assessment image has not been tampered with;

[0165] If the calculated hash values are different from the corresponding hash values in the hash chain data, confirm that the second judgment result is that the loss assessment image has been tampered with.

[0166] In one embodiment, for the method of tracing the loss assessment image based on the encrypted binding data and the hash chain data to obtain a tracing result, the method further includes:

[0167] Obtain the ID of the loss assessment image and construct a two-dimensional code according to the ID;

[0168] Combine the two-dimensional code and the loss assessment image to generate a two-dimensional code image;

[0169] Trace the loss assessment image according to the two-dimensional code image to obtain a fuzzy tracing result.

[0170] In one embodiment, when obtaining the ID of the loss assessment image, it includes:

[0171] Use a preset image software to query the shooting time of the loss assessment image;

[0172] Obtain the shooting location of the loss assessment image through the global positioning system;

[0173] Obtain the shooting account information of the loss assessment image;

[0174] Combine the shooting time, shooting location and shooting account information to generate an ID.

[0175] The present invention provides an anti-tampering loss assessment image tracing device, which obtains the global positioning data, base station positioning data and device sensor data of the loss assessment image for the tracing result service;

[0176] Fuse the global positioning data, base station positioning data, and device sensor data to obtain a dynamic timestamp; combine a preset device hardware fingerprint and a preset sensor fingerprint to generate a composite fingerprint; perform asymmetric encryption binding on the dynamic timestamp and the composite fingerprint to obtain encrypted binding data; perform multiple hashing on the damage assessment image to obtain hash chain data; trace the damage assessment image based on the encrypted binding data and the hash chain data to obtain a tracing result. Through the tracing shooting information, it is possible to prevent the phenomenon that the operator does not arrive at the scene for investigation and evidence collection and is replaced by others to take pictures, thereby reducing the leakage risk of cases and helping to reduce the operating costs of the company.

[0177] For the specific limitations of an anti-tampering damage assessment image tracing device, reference can be made to the limitations of an anti-tampering damage assessment image tracing method in the above text, which will not be elaborated here. Each module in the above anti-tampering damage assessment image tracing device can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so as to facilitate the processor to call and execute the operations corresponding to the above modules.

[0178] In one embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 5 shown. The computer device includes a processor, a memory, a network interface, and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile and / or volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external client through a network connection. When the computer program is executed by the processor, it realizes the functions or steps on the server side of an anti-tampering damage assessment image tracing method.

[0179] In one embodiment, a computer device is provided. The computer device can be a client, and its internal structure diagram can be as Figure 6As shown in the figure. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external server through a network connection. When the computer program is executed by the processor, it realizes the functions or steps on the client side of an anti-tampering loss assessment image traceability method.

[0180] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the following steps are realized:

[0181] Obtain the global positioning data, base station positioning data, and device sensor data of the loss assessment image;

[0182] Perform fusion processing on the global positioning data, base station positioning data, and device sensor data to obtain a dynamic timestamp;

[0183] Combine a preset device hardware fingerprint and a preset sensor fingerprint to generate a composite fingerprint;

[0184] Perform asymmetric encryption binding on the dynamic timestamp and the composite fingerprint to obtain encrypted binding data;

[0185] Perform multiple hashing processing on the loss assessment image to obtain hash chain data;

[0186] Trace the loss assessment image according to the encrypted binding data and the hash chain data to obtain a traceability result.

[0187] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by the processor, the following steps are realized:

[0188] Obtain the global positioning data, base station positioning data, and device sensor data of the loss assessment image;

[0189] Perform fusion processing on the global positioning data, base station positioning data, and device sensor data to obtain a dynamic timestamp;

[0190] Combine a preset device hardware fingerprint and a preset sensor fingerprint to generate a composite fingerprint;

[0191] Perform asymmetric encryption binding on the dynamic timestamp and the composite fingerprint to obtain encrypted binding data;

[0192] Perform multiple hashing on the loss assessment image to obtain hash chain data;

[0193] Trace the origin of the loss assessment image based on the encrypted binding data and the hash chain data to obtain a tracing result.

[0194] It should be noted that for the functions or steps that can be achieved by the above computer-readable storage medium or computer device, reference can be made to the relevant descriptions on the server side and the client side in the foregoing method embodiments. To avoid repetition, they will not be described in detail here.

[0195] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above method embodiments. Among them, any reference to memory, storage, database, or other media used in the various embodiments provided in the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0196] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0197] The above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than limiting them. If software tools or components of other companies appear in the application embodiments, they are only used for example introduction and do not represent actual use. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for tracing the origin of anti-tampering loss assessment images, characterized in that, Including: Obtain the global positioning data, base station positioning data, and device sensor data of the loss assessment image; Perform fusion processing on the global positioning data, base station positioning data, and device sensor data to obtain a dynamic timestamp; Combine a preset device hardware fingerprint and a preset sensor fingerprint to generate a composite fingerprint; Perform asymmetric encryption binding on the dynamic timestamp and the composite fingerprint to obtain encrypted binding data; Perform multiple hashing processing on the loss assessment image to obtain hash chain data; Trace the loss assessment image based on the encrypted binding data and the hash chain data to obtain a tracing result.

2. The anti-tampering damage assessment image traceability method according to claim 1, characterized in that, The combining a preset device hardware fingerprint and a preset sensor fingerprint to generate a composite fingerprint includes: Extract the device identification code in the preset device hardware fingerprint, and extract the device identification code according to a preset number of digits to obtain a basic identifier; Perform feature extraction on the preset sensor fingerprint to obtain sensor fingerprint features, and encode the sensor fingerprint features to obtain a feature code; Perform an exclusive OR operation on the basic identifier and the feature code to obtain a composite fingerprint.

3. The anti-tampering and loss assessment image traceability method according to claim 1, wherein, The performing multiple hashing processing on the loss assessment image to obtain hash chain data includes: Perform a secure hashing operation on the loss assessment image to obtain a main hash value; Perform a hash hashing operation on the loss assessment image to obtain an auxiliary hash value; Combine the main hash value and the auxiliary hash value through a preset Merkle tree structure to generate hash chain data.

4. The anti-tampering and damage assessment image traceability method according to claim 1, characterized in that, The tracing the loss assessment image based on the encrypted binding data and the hash chain data to obtain a tracing result includes: Write the encrypted binding data and the hash chain data into a blockchain node, and respectively read the encrypted binding data and the hash chain data through the blockchain node; Perform decryption judgment on the read encrypted binding data to obtain a first judgment result; Perform verification judgment on the read hash chain data to obtain a second judgment result; Perform tracing judgment on the first judgment result and the second judgment result to obtain a tracing result.

5. The anti-tampering and damage assessment image traceability method according to claim 4, characterized in that The performing verification judgment on the read hash chain data to obtain a second judgment result includes: Calculate the hash values in the read hash chain data one by one; Compare the calculated hash values with the corresponding hash values in the hash chain data; If the calculated hash value is the same as the corresponding hash value in the hash chain data, confirm that the second judgment result is that the loss assessment image has not been tampered with; If the calculated hash value is different from the corresponding hash value in the hash chain data, confirm that the second judgment result is that the loss assessment image has been tampered with.

6. The anti-tampering and damage assessment image traceability method according to claim 1, characterized in that, After the tracing the loss assessment image based on the encrypted binding data and the hash chain data to obtain a tracing result, the method further includes: Obtain the ID of the loss assessment image, and construct a two-dimensional code according to the ID; Combine the two-dimensional code and the loss assessment image to generate a two-dimensional code image; Trace the loss assessment image according to the two-dimensional code image to obtain a fuzzy tracing result.

7. The anti-tampering damage assessment image traceability method according to claim 6, wherein The obtaining the ID of the loss assessment image includes: Use a preset image software to query the shooting time of the loss assessment image; Obtain the shooting location of the loss assessment image through the Global Positioning System; Obtain the shooting account information of the loss assessment image; Combine the shooting time, shooting location and shooting account information to generate an ID.

8. An anti-tampering damage assessment image traceability device, characterized in that, It includes: An acquisition module, configured to acquire the global positioning data, base station positioning data, and device sensor data of the loss assessment image; A processing module, configured to perform fusion processing on the global positioning data, base station positioning data, and device sensor data to obtain a dynamic timestamp; A generation module, configured to combine a preset device hardware fingerprint and a preset sensor fingerprint to generate a composite fingerprint; A binding module, configured to perform asymmetric encryption binding on the dynamic timestamp and the composite fingerprint to obtain encrypted binding data; A hash processing module, configured to perform multiple hash processing on the loss assessment image to obtain hash chain data; A traceability module, configured to trace the loss assessment image according to the encrypted binding data and the hash chain data to obtain a traceability result.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the anti-tampering loss assessment image traceability method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the anti-tampering loss assessment image traceability method according to any one of claims 1 to 7.

Images