Multi-dimensional data-driven zero-labor economic employment record credible evidence storage method

Abstract

The invention discloses a multi-dimensional data-driven zero-labor economic employment record credible evidence storage method, and particularly relates to the technical field of credible evidence storage, and the method comprises the steps: collecting the multi-dimensional data of a rider in real time through equipment, storing the multi-dimensional data in a block chain, connecting the data of each distribution link through a hash value to form the block chain, the method comprises the steps of determining the similarity between a time behavior of a current distribution task and a historical task by comparing a time sequence of the current distribution task and historical task data so as to obtain time matching information, comparing actual distribution time, a path and a distance with expected values, analyzing data integrity, and determining the data integrity. According to the method, the data matching information is obtained, the distribution task credibility of the rider is judged through combined analysis of the time matching information and the data matching information, the data integrity, transparency and credibility in the distribution process are ensured, and meanwhile the operation efficiency and the management precision of the platform are improved.

Description

Technical Field

[0001] This invention relates to the field of trusted evidence preservation technology, and more specifically, to a multi-dimensional data-driven trusted evidence preservation method for gig economy employment records. Background Technology

[0002] In the gig economy, especially in sectors like food delivery, the sharing economy, and express delivery, the work quality and performance of riders or other laborers are crucial factors in platform management, user reviews, and payroll decisions. Currently, many platforms rely on traditional data storage and evaluation methods to record rider work data. These traditional methods lack transparency, making it difficult for platforms and customers to verify the data's authenticity. Even when platforms provide historical rider data, they cannot guarantee that the data hasn't been modified or deleted, leading to trust issues. Furthermore, because rider work data is typically recorded by the riders themselves or automatically generated by the platform system, this data is easily tampered with or falsified.

[0003] To address the aforementioned shortcomings, a technical solution is provided. Summary of the Invention

[0004] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a multi-dimensional data-driven method for credible evidence storage of gig economy employment records, thereby addressing the problems raised in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A multi-dimensional data-driven method for credible evidence storage of employment records in the gig economy includes the following steps:

[0007] S1: Collect multi-dimensional data of riders during delivery, package the multi-dimensional data of each link in the delivery process into blocks, and connect each block with the previous blockchain through the data collection layer, blockchain storage layer and verification layer to build the blockchain for each delivery task of the rider.

[0008] S2: Compare the rider's delivery tasks with historical data in the delivery platform, compare the time series of the current delivery task with the historical data, and determine the time matching information of the blockchain for the current delivery task;

[0009] S3: By comparing the actual delivery time, actual delivery route, and actual delivery distance of each rider's block with the expected values, and analyzing the integrity of the data, the data matching information of the current delivery task blockchain is determined;

[0010] S4: Perform a comprehensive analysis of the time matching information and data matching information of the current delivery task blockchain, and mark the delivery task blockchain with low credibility.

[0011] In a preferred embodiment, the time matching information and data matching information of the current delivery task blockchain include:

[0012] The temporal matching information of the blockchain is represented by the temporal similarity sequence coefficient, and the data matching information of the blockchain is represented by the data deviation anomaly coefficient and the data integrity consistency coefficient, where XS sj PC represents the coefficients of time-similarity sequences. yc YZ is the data deviation anomaly coefficient. wz This is the data integrity and consistency coefficient.

[0013] In a preferred embodiment, the logic for obtaining the time similarity sequence coefficients is as follows:

[0014] Based on the blockchain currently being delivered by the rider, obtain the generation time and expiration time of each block in the blockchain, and standardize the generation time and expiration time of each block;

[0015] Obtain historical time data from the delivery platform, collect the minimum and maximum timestamps of each block in the historical time data, and calculate the standardized generation and expiration times of each block using the following formula: Among them, T nor Let T be the time series consisting of the generation and deadline times of each standardized block. min T is a time series consisting of the minimum generation and expiration times of each block in historical time data. max A time series consisting of the maximum values ​​of the generation time and the expiration time of each block in historical time data;

[0016] For the current delivery blockchain, calculate the maximum similarity between the current blockchain's time series and the historical time series data. The calculation formula is as follows: Among them, JL min The maximum similarity between the current blockchain time series and the time series of historical tasks. For historical time data, n is the number of the time series in the historical time data, n = 1, 2, 3, ..., N, where N is a positive integer, and i is the number of the generation time and end time of each block in the time series, i = 1, 2, 3, ..., I, where I is a positive integer;

[0017] The average similarity between the current blockchain time series and historical time series data is calculated using the following formula:

[0018] The formula for calculating the coefficients of time-similarity sequences is as follows:

[0019] In a preferred embodiment, the logic for obtaining the data deviation anomaly coefficient is as follows:

[0020] Based on the blockchain currently being delivered by the rider, multi-dimensional data of each block in the blockchain is obtained. The multi-dimensional data includes the rider's location information and time information in each block. The actual delivery time, actual delivery route and actual delivery distance of the rider in each block are obtained. Based on the delivery platform's expected delivery time, expected delivery route and expected delivery distance of the rider in each block, the rider's delivery time deviation, delivery route deviation and delivery distance deviation are obtained.

[0021] The formula for calculating the data deviation anomaly coefficient is as follows: Where m is the block number in the blockchain, m = 1, 2, 3, ..., M, and M is a positive integer. Due to delivery time deviation, Due to delivery route deviation, Due to delivery distance deviation, For the expected delivery time, Expected delivery route Expected delivery distance.

[0022] In a preferred embodiment, the logic for obtaining the data integrity and consistency coefficient is as follows:

[0023] Based on the blockchain data currently being delivered by the rider, obtain the data items contained in each block of the blockchain, and represent the integrity of each package pickup data item using an integrity index, which is then marked as follows: in, This indicates that the data is complete. This indicates that a data item is missing, where j = 1, 2, 3, ..., J, where J is a positive integer and j is the data item number of each block;

[0024] The data integrity and consistency coefficient is calculated using the following formula:

[0025] In a preferred embodiment, the time matching information and data matching information of the current delivery task blockchain are comprehensively analyzed.

[0026] A credibility assessment model is constructed by weighting the time similarity series coefficient, data deviation anomaly coefficient, and data integrity consistency coefficient, and a credibility assessment coefficient is generated. The formula for calculating the credibility assessment coefficient is: PG kx =α1XS sj -α2PC yc +α3YZ wz Among them, PG kxα1, α2, and α3 are the reliability evaluation coefficients, and the proportional coefficients of the time similarity sequence coefficient, the data deviation anomaly coefficient, and the data integrity consistency coefficient, respectively. α1, α2, and α3 are all greater than 0.

[0027] In a preferred embodiment, marking delivery task blockchains with low credibility includes:

[0028] Set a credibility assessment coefficient threshold, obtain the credibility assessment coefficient of each blockchain for rider delivery tasks, compare the credibility assessment coefficient of each blockchain with the credibility assessment coefficient threshold, if the credibility assessment coefficient is less than the credibility assessment coefficient threshold, then mark the blockchain; if the credibility assessment coefficient is greater than the credibility assessment coefficient threshold, then do not mark the blockchain.

[0029] The technical effects and advantages of this invention are as follows:

[0030] This invention collects multi-dimensional data from delivery riders in real time using equipment and stores it in a blockchain. Data from each delivery stage is linked together using hash values ​​to form a blockchain, ensuring data integrity and immutability. By comparing the time series of the current delivery task with historical task data, the similarity between the time behavior of the current delivery task and historical tasks is determined, thereby obtaining time matching information. The actual delivery time, route, and distance are compared with expected values ​​to analyze data integrity and obtain data matching information. By combining and analyzing the time matching information and data matching information, the credibility of the rider's delivery task is determined. This invention ensures the data integrity, transparency, and credibility of the delivery process. At the same time, by utilizing real-time data analysis and the automated execution of smart contracts, it improves the platform's operational efficiency and management accuracy. Attached Figure Description

[0031] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0032] Figure 1 This is a flowchart illustrating a multi-dimensional data-driven method for credible evidence storage of gig economy employment records according to the present invention. Detailed Implementation

[0033] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] Example 1

[0035] Figure 1This is a flowchart illustrating a multi-dimensional data-driven method for credible evidence storage of gig economy employment records according to the present invention, specifically including the following steps:

[0036] S1: Collect multi-dimensional data of riders during delivery, package the multi-dimensional data of each link in the delivery process into blocks, and connect each block with the previous blockchain through the data collection layer, blockchain storage layer and verification layer to build the blockchain for each delivery task of the rider.

[0037] S2: Compare the rider's delivery tasks with historical data in the delivery platform, compare the time series of the current delivery task with the historical data, and determine the time matching information of the blockchain for the current delivery task;

[0038] S3: By comparing the actual delivery time, actual delivery route, and actual delivery distance of each rider's block with the expected values, and analyzing the integrity of the data, the data matching information of the current delivery task blockchain is determined;

[0039] S4: Perform a comprehensive analysis of the time matching information and data matching information of the current delivery task blockchain, and mark the delivery task blockchain with low credibility.

[0040] In the food delivery process, by collecting multi-dimensional data on riders during their delivery, a comprehensive and accurate assessment of employment records in the gig economy can be provided. This multi-dimensional data includes geographical location information, time information, workload information, and evaluation information.

[0041] Specifically, the system uses geolocation information such as riding trajectory, location changes, and dwell time to track the rider's delivery process. By comparing the actual delivery route with the planned route, the system can analyze the rider's delivery efficiency and adherence to the route.

[0042] Time information, including order acceptance time, delivery time, and delivery duration, helps the system evaluate riders' delivery efficiency. By analyzing rider performance during different time periods (peak and off-peak hours), the system can dynamically adjust the reward mechanism.

[0043] Workload information, including the complexity, number, and content of delivery tasks (such as special delivery requirements), is also part of multidimensional data. By analyzing different dimensions of a task (such as the number of meals delivered, the delivery distance, and whether it is an urgent task), the system can determine the rider's performance under specific tasks.

[0044] The evaluation information, consisting of customer ratings of riders and evaluation content (such as punctuality, attitude, and food quality), constitutes important multi-dimensional data. This evaluation data can be used to assess the overall service quality of riders.

[0045] Based on multidimensional data collected from delivery riders during their deliveries, the system ensures that the collected data is not tampered with during storage and use, and can be verified and traced at any time. Utilizing blockchain technology and smart contracts, the authenticity, integrity, and immutability of the data can be guaranteed.

[0046] Specifically, in the scenario of food delivery riders, the data collection layer technology can ensure the real-time and secure collection of riders' work data, including:

[0047] By deploying TEE (Transportation Equipment), riders' multidimensional data (such as riding routes, delivery times, customer reviews, etc.) is reliably and securely collected and protected from tampering. This ensures the authenticity of the data collection, especially in the data-dependent gig economy, where TEE provides a high level of security for data collection.

[0048] Multi-dimensional data fingerprints, such as GPS location, biometrics (like rider fingerprints or facial recognition), and device fingerprints, can effectively link a rider's identity to their job tasks. For example, the system tracks a rider's delivery route using GPS while using biometrics to verify the rider's identity, preventing identity theft or tampering with work data.

[0049] Precise timestamps ensure that all delivery tasks have accurate start and end times. This is crucial for task timeliness, work duration, and related pay settlement.

[0050] The blockchain evidence storage layer is crucial for ensuring the immutability, transparency, and traceability of data during the delivery process, including:

[0051] By adopting a consortium blockchain architecture (such as enterprise nodes, worker nodes, and regulatory nodes), different participants (platforms, riders, and regulators) can transparently record information on the same chain and ensure the authenticity and immutability of the data. For example, riders and platforms can share information on the same chain, while regulatory nodes are responsible for reviewing and ensuring data compliance.

[0052] The smart contract notarization mechanism works like this: when a rider receives a delivery task, the system generates a commitment hash, representing the contractual commitment between the rider and the platform at the start of the task. This hash is pre-stored on the blockchain to ensure consistency between the task and the requirements. During the delivery process, key data points (such as riding route, delivery time, customer ratings, etc.) are uploaded to the blockchain in real time, ensuring the entire delivery process is effectively recorded and cannot be tampered with. For example, if a rider fails to complete the task within the stipulated time, the system can record this delay. After the task is completed, the platform verifies the rider's workload and generates a complete notarization certificate. The notarization certificate obtained by the rider on the blockchain proves the quality of their work and can be used as a basis for settlement.

[0053] A hierarchical storage structure—combining on-chain data fingerprints (such as Merkle Roots) with off-chain distributed storage of raw data files (such as images or videos of cycling routes)—ensures efficient data storage and easy traceability. On-chain data fingerprints ensure data integrity, while the raw data files, stored using distributed storage technologies such as IPFS, guarantee the accessibility and immutability of large data files.

[0054] In the verification layer, data verification and privacy protection mechanisms are crucial for trust between the platform and riders, including:

[0055] Data verification functionality can be provided to different platforms via APIs, SDKs, and web interfaces, facilitating verification by various stakeholders (platforms, riders, and regulators). For example, riders can query their work records through a web interface, while platforms can perform real-time verification via API.

[0056] Zero-knowledge proofs: Zero-knowledge proofs can be used for privacy protection verification. For example, during the rider's income settlement process, the platform can verify the rider's work performance using zero-knowledge proof technology without disclosing specific income data. This technology can ensure the rider's privacy is protected while also guaranteeing the transparency and fairness of income calculation.

[0057] Cross-chain verification module: Modules supporting cross-chain verification enable data interoperability between gig economy platforms, which is especially crucial for gig workers working across platforms (such as multi-platform food delivery riders). Cross-chain verification ensures the consistency and mutual recognition of gig workers' performance and work records across different platforms.

[0058] In this blockchain, key stages of a rider's delivery task are represented as blocks. Each block records multi-dimensional data about the rider and is linked to the previous block via hashing to ensure data integrity and consistency. The chain consists of multiple blocks, recording different stages of the task (e.g., task start, in progress, completed) and related multi-dimensional data (e.g., timestamps, GPS, customer feedback). Each block records one stage of the delivery task and is linked to the previous block via hashing, forming a chain. Once the delivery task is completed and confirmed, blockchain technology generates final proof of delivery, and a smart contract automatically triggers the payment or reward related to that task.

[0059] By collecting multidimensional data from each block in the blockchain generated by each delivery by riders, and analyzing the multidimensional data, the time matching information and data matching information of the blockchain are obtained. The credibility model of the blockchain is automatically generated to judge the credibility of each delivery. The time matching information of the blockchain is represented by the time similarity sequence coefficient, and the data matching information of the blockchain is represented by the data deviation anomaly coefficient and the data integrity consistency coefficient.

[0060] The role of the time similarity sequence coefficient is as follows:

[0061] The time-similarity sequence coefficient utilizes blockchain technology to store riders' work records, ensuring these records are immutable and trustworthy. The blockchain can store time-series data for each delivery, task completion status, customer feedback, and other information, thus forming a detailed work history for each rider. This history can be queried by subsequent task scheduling systems, evaluation systems, or other relevant parties, ensuring data transparency and fairness.

[0062] By measuring the similarity between the time series of current rider delivery tasks and historical data, the platform helps identify whether riders are completing tasks according to the expected time pattern. High similarity indicates that the rider's work behavior meets the platform's requirements, while low similarity may reflect abnormal behavior or deviation from standard delivery methods.

[0063] Different stakeholders (such as platforms, riders, and customers) can access transparent and reliable delivery records based on blockchain. This means that all parties can ensure a fair evaluation of rider performance, reducing trust costs, especially by establishing a decentralized trust mechanism between the platform and riders.

[0064] Blockchain-based smart contracts can automatically execute task scheduling and evaluation systems, using time similarity coefficients to automate task allocation and reward mechanisms. For example, when a rider's behavior is highly consistent with historical data, the smart contract can trigger a reward mechanism, increasing their priority on the platform.

[0065] The logic for obtaining the time similarity sequence coefficient is as follows: based on the blockchain currently being delivered by the rider, obtain the generation time and expiration time of each block in the blockchain, and standardize the generation time and expiration time of each block;

[0066] It should be noted that each block represents a specific stage in the delivery process. The generation time of each block indicates the start time of each delivery stage (such as task receipt, delivery start, arrival at destination, etc.), and the end time of each block indicates the end time of each stage (such as task completion, customer feedback, etc.).

[0067] Obtain historical time data from the delivery platform, collect the minimum and maximum timestamps of each block in the historical time data, and calculate the standardized generation and expiration times of each block using the following formula: Among them, T nor Let T be the time series consisting of the generation and deadline times of each standardized block. minT is a time series consisting of the minimum generation and expiration times of each block in historical time data. max A time series consisting of the maximum values ​​of the generation time and the expiration time of each block in historical time data;

[0068] It should be noted that the historical time data in the delivery platform includes delivery tasks of the same type as the riders, ensuring that the destinations of the delivery tasks are consistent and guaranteeing standardized fairness.

[0069] For the current delivery blockchain, calculate the maximum similarity between the current blockchain's time series and the historical time series data. The calculation formula is as follows: Among them, JL min The maximum similarity between the current blockchain time series and the time series of historical tasks. For historical time data, n is the number of the time series in the historical time data, n = 1, 2, 3, ..., N, where N is a positive integer, and i is the number of the generation time and end time of each block in the time series, i = 1, 2, 3, ..., I, where I is a positive integer;

[0070] The average similarity between the current blockchain time series and historical time series data is calculated using the following formula:

[0071] The formula for calculating the coefficients of time-similarity sequences is as follows: Among them, XS sj These are the coefficients for time-similarity sequences.

[0072] As can be seen from the formula, the larger the temporal similarity sequence coefficient, the more similar the temporal behavior of the current delivery task is to the temporal behavior of historical tasks, which means that the rider's credibility in this delivery task is higher.

[0073] The role of the data deviation anomaly coefficient is as follows:

[0074] The data deviation anomaly coefficient can identify behaviors that deviate from normal delivery patterns. If the actual delivery situation differs significantly from expectations, it indicates a potential anomaly. For example, delivery times may be longer than expected, riders may have taken unnecessary detours, or delivery routes may be illogical.

[0075] When the system detects a significant deviation from expected delivery routes, the platform can analyze the reasons to identify issues such as detours or unreasonable routes. In this way, the platform can further optimize route planning to prevent similar deviations. By using the data deviation anomaly coefficient, the platform can identify the causes of time delays during delivery and make corresponding adjustments, such as adjusting delivery times and optimizing task allocation.

[0076] The logic for obtaining the data deviation anomaly coefficient is as follows: Based on the blockchain currently being delivered by the rider, obtain multi-dimensional data of each block in the blockchain. The multi-dimensional data includes the rider's location information and time information in each block. Obtain the rider's actual delivery time, actual delivery route, and actual delivery distance in each block. Based on the delivery platform's expected delivery time, expected delivery route, and expected delivery distance of the rider in each block, obtain the rider's delivery time deviation, delivery route deviation, and delivery distance deviation.

[0077] It should be noted that the actual delivery time is obtained from the start and end times of each block in the blockchain. The expected delivery time is the delivery time estimated by the platform or system when assigning tasks. The actual delivery path is obtained from the blockchain to determine the rider's location at each point in time (usually latitude and longitude coordinates). The expected delivery path is the path that the rider should travel, estimated by the system based on the shortest path or optimal path algorithm (such as map algorithms or historical paths). The actual delivery distance is obtained by obtaining the rider's waypoints (latitude and longitude) from the blockchain and calculating the total length of the path. The expected delivery distance is the distance of the predetermined path calculated based on maps, route planning, or historical data.

[0078] The formula for calculating the data deviation anomaly coefficient is as follows: Among them, PC yc Here, m represents the data deviation anomaly coefficient, and m is the block number in the blockchain, where m = 1, 2, 3, ..., M, and M is a positive integer. Due to delivery time deviation, Due to delivery route deviation, Due to delivery distance deviation, For the expected delivery time, Expected delivery route Expected delivery distance.

[0079] As the formula shows, the larger the data deviation anomaly coefficient, the more likely there are problems in the delivery process, such as detours, delays, or delivery routes and distances that do not meet expectations. This indicates that the rider's credibility in this delivery task is lower.

[0080] Incomplete data during delivery can stem from various factors. These include rider errors such as forgetting to start recording or misoperating equipment, leading to missing crucial information like timestamps, location coordinates, and customer feedback. Platforms may also experience synchronization errors or data loss when receiving, processing, and merging data from different sources. For instance, data from different time points may not match accurately, or parts may be lost when merging multiple data streams. During delivery, tasks may change due to customer requests, route adjustments, or unforeseen circumstances. Failure to update the system or records promptly can result in missing or inconsistent final data. Furthermore, if riders fail to properly differentiate or integrate data across multiple tasks, data loss can also occur. For example, a rider might not correctly record the start and end times of each task when handling multiple orders.

[0081] The logic for obtaining the data integrity and consistency coefficient is as follows: Based on the blockchain currently being delivered by the rider, obtain the data items contained in each block of the blockchain, represent the integrity of each package pickup data item through an integrity index, and mark the integrity index as follows: in, This indicates that the data is complete. This indicates that a data item is missing, where j = 1, 2, 3, ..., J, where J is a positive integer and j is the data item number of each block;

[0082] The data integrity and consistency coefficient is calculated using the following formula: Among them, YZ wz This is the data integrity and consistency coefficient.

[0083] As the formula shows, the higher the data integrity and consistency coefficient, the better the data items are in all blocks, with virtually no missing data, indicating a higher level of credibility for the rider in this delivery task.

[0084] By comprehensively analyzing the time matching and data matching information of the blockchain, and through weighted calculation of time similarity sequence coefficient, data deviation anomaly coefficient, and data integrity and consistency coefficient, a credibility assessment model is constructed, generating a credibility assessment coefficient. The formula for calculating the credibility assessment coefficient is: PG kx =α1XS sj -α2PC yc +α3YZ wz Among them, PG kx α1, α2, and α3 are the reliability evaluation coefficients, and the proportional coefficients of the time similarity sequence coefficient, the data deviation anomaly coefficient, and the data integrity consistency coefficient, respectively. α1, α2, and α3 are all greater than 0.

[0085] As the formula shows, a higher credibility evaluation coefficient indicates that the delivery task's time behavior, route, and data are consistent with expectations, the delivery process is efficient and reliable, the rider's performance in this task meets platform standards, and the task data has not been tampered with, thus possessing high credibility. This means that the delivery task has high credibility, and the platform can rely on the rider's performance. Conversely, a lower coefficient indicates that there are many deviations in the delivery process, possibly including time delays, route detours, or data loss. A low credibility evaluation coefficient means that the rider's performance does not meet expectations, and the platform may need to further evaluate the rider's behavior or adjust task allocation and management strategies.

[0086] Set a credibility assessment coefficient threshold, obtain the credibility assessment coefficient of each blockchain for rider delivery tasks, compare the credibility assessment coefficient of each blockchain with the credibility assessment coefficient threshold, if the credibility assessment coefficient is less than the credibility assessment coefficient threshold, then mark the blockchain for subsequent manual inspection or further processing, if the credibility assessment coefficient is greater than the credibility assessment coefficient threshold, then do not mark the blockchain.

[0087] This invention collects multi-dimensional data from delivery riders in real time using equipment and stores it in a blockchain. Data from each delivery stage is linked together using hash values ​​to form a blockchain, ensuring data integrity and immutability. By comparing the time series of the current delivery task with historical task data, the similarity between the time behavior of the current delivery task and historical tasks is determined, thereby obtaining time matching information. The actual delivery time, route, and distance are compared with expected values ​​to analyze data integrity and obtain data matching information. By combining and analyzing the time matching information and data matching information, the credibility of the rider's delivery task is determined. This invention ensures the data integrity, transparency, and credibility of the delivery process. At the same time, by utilizing real-time data analysis and the automated execution of smart contracts, it improves the platform's operational efficiency and management accuracy.

[0088] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0089] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0090] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0091] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0092] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0093] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0094] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0095] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A multi-dimensional data-driven method for credible evidence storage of gig economy employment records, characterized in that, Specifically, the following steps are included: S1: Collect multi-dimensional data of riders during delivery, package the multi-dimensional data of each link in the delivery process into blocks, and connect each block with the previous blockchain through the data collection layer, blockchain storage layer and verification layer to build the blockchain for each delivery task of the rider. S2: Compare the rider's delivery tasks with historical data in the delivery platform, compare the time series of the current delivery task with the historical data, and determine the time matching information of the blockchain for the current delivery task; S3: By comparing the actual delivery time, actual delivery route, and actual delivery distance of each rider's block with the expected values, and analyzing the integrity of the data, the data matching information of the current delivery task blockchain is determined; S4: Perform a comprehensive analysis of the time matching information and data matching information of the current delivery task blockchain, and mark the delivery task blockchain with low credibility.

2. The multi-dimensional data-driven method for credible evidence storage of gig economy employment records according to claim 1, characterized in that, The current delivery task blockchain includes time matching information and data matching information, including: The temporal matching information of the blockchain is represented by the temporal similarity sequence coefficient, and the data matching information of the blockchain is represented by the data deviation anomaly coefficient and the data integrity consistency coefficient, where XS sj PC represents the coefficients of time-similarity sequences. yc YZ is the data deviation anomaly coefficient. wz This is the data integrity and consistency coefficient.

3. The multi-dimensional data-driven method for credible evidence storage of gig economy employment records according to claim 2, characterized in that, The logic for obtaining the coefficients of the time-similar sequences is as follows: Based on the blockchain currently being delivered by the rider, obtain the generation time and expiration time of each block in the blockchain, and standardize the generation time and expiration time of each block; Obtain historical time data from the delivery platform, collect the minimum and maximum timestamps of each block in the historical time data, and calculate the standardized generation and expiration times of each block using the following formula: Among them, T nor Let T be the time series consisting of the generation and deadline times of each standardized block. min T is a time series consisting of the minimum generation and expiration times of each block in historical time data. max A time series consisting of the maximum values ​​of the generation time and the expiration time of each block in historical time data; For the current delivery blockchain, calculate the maximum similarity between the current blockchain's time series and the historical time series data. The calculation formula is as follows: Among them, JL min The maximum similarity between the current blockchain time series and the time series of historical tasks. For historical time data, n is the number of the time series in the historical time data, n = 1, 2, 3, ..., N, where N is a positive integer, and i is the number of the generation time and end time of each block in the time series, i = 1, 2, 3, ..., I, where I is a positive integer; The average similarity between the current blockchain time series and historical time series data is calculated using the following formula: The formula for calculating the coefficients of time-similarity sequences is as follows:

4. The method for reliable evidence storage of gig economy employment records driven by multi-dimensional data, as described in claim 1, is characterized in that... The logic for obtaining the data deviation anomaly coefficient is as follows: Based on the blockchain currently being delivered by the rider, multi-dimensional data of each block in the blockchain is obtained. The multi-dimensional data includes the rider's location information and time information in each block. The actual delivery time, actual delivery route and actual delivery distance of the rider in each block are obtained. Based on the delivery platform's expected delivery time, expected delivery route and expected delivery distance of the rider in each block, the rider's delivery time deviation, delivery route deviation and delivery distance deviation are obtained. The formula for calculating the data deviation anomaly coefficient is as follows: Where m is the block number in the blockchain, m = 1, 2, 3, ..., M, and M is a positive integer. Due to delivery time deviation, Due to delivery route deviation, Due to delivery distance deviation, For the expected delivery time, Expected delivery route Expected delivery distance.

5. The multi-dimensional data-driven method for credible evidence storage of gig economy employment records according to claim 4, characterized in that, The logic for obtaining the data integrity and consistency coefficient is as follows: Based on the blockchain data currently being delivered by the rider, obtain the data items contained in each block of the blockchain, and represent the integrity of each package pickup data item using an integrity index, which is then marked as follows: in, This indicates that the data is complete. This indicates that a data item is missing, where j = 1, 2, 3, ..., J, where J is a positive integer and j is the data item number of each block; The data integrity and consistency coefficient is calculated using the following formula:

6. The multi-dimensional data-driven method for credible evidence storage of gig economy employment records according to claim 5, characterized in that, A comprehensive analysis will be conducted on the time matching information and data matching information of the current delivery task blockchain. A credibility assessment model is constructed by weighting the time similarity series coefficient, data deviation anomaly coefficient, and data integrity consistency coefficient, and a credibility assessment coefficient is generated. The formula for calculating the credibility assessment coefficient is: PG kx =α1XS sj -α2PC yc +α3YZ wz ; Among them, PG kx α1 represents the credibility evaluation coefficient, and α2 and α3 represent the proportional coefficients of the time similarity sequence coefficient, the data deviation anomaly coefficient, and the data integrity consistency coefficient, respectively. α1, α2, and α3 are all greater than 0.

7. A multi-dimensional data-driven method for credible evidence storage of gig economy employment records according to claim 6, characterized in that, The blockchain of delivery tasks with low credibility is marked, including: Set a credibility assessment coefficient threshold, obtain the credibility assessment coefficient of each blockchain for rider delivery tasks, compare the credibility assessment coefficient of each blockchain with the credibility assessment coefficient threshold, if the credibility assessment coefficient is less than the credibility assessment coefficient threshold, then mark the blockchain; if the credibility assessment coefficient is greater than the credibility assessment coefficient threshold, then do not mark the blockchain.

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