Logistics driver evaluation method and device, computer equipment and storage medium

Abstract

The invention relates to a logistics driver evaluation method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: obtaining transportation data of a logistics driver in a target time period, user evaluation data and order data of undertaking and transporting electric power materials; according to the transportation data and the user evaluation data, performing credit evaluation processing on the logistics driver to obtain first credit evaluation data of the logistics driver; according to the order data, carrying out stepped credit evaluation processing on the logistics driver to obtain second credit evaluation data of the logistics driver; and obtaining a target credit evaluation result of the logistics driver in the next time period of the target time period according to the first credit evaluation data and the second credit evaluation data. By adopting the method, the accuracy of credit evaluation of the logistics driver can be improved, and the logistics driver can be encouraged to undertake more transportation orders of electric power materials.

Description

Technical Field

[0001] The present application relates to the technical field of power grid material transportation, and in particular to a method, device, computer equipment, storage medium and computer program product for evaluating logistics drivers. Background Art

[0002] In the process of cargo transportation in the power grid industry, the service quality of logistics drivers is particularly important.

[0003] Traditional evaluation methods for logistics drivers often have problems such as a single evaluation dimension, inaccurate evaluation results, and lack of incentive mechanisms, making it difficult to fully and objectively reflect the actual work performance of logistics drivers. Summary of the Invention

[0004] Based on this, it is necessary to provide a logistics driver evaluation method, device, computer equipment, computer-readable storage medium and computer program product that can improve the evaluation accuracy of logistics drivers in the power grid industry in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for evaluating logistics drivers. The method comprises:

[0006] Obtain logistics drivers' transportation data, user evaluation data, and order data for transporting power supplies within the target time period;

[0007] Performing a credit evaluation on the logistics driver based on the transportation data and the user evaluation data to obtain first credit evaluation data of the logistics driver;

[0008] Performing a step-by-step credit evaluation process on the logistics driver based on the order data to obtain second credit evaluation data of the logistics driver;

[0009] A target credit evaluation result of the logistics driver in the next time period after the target time period is obtained based on the first credit evaluation data and the second credit evaluation data.

[0010] In one embodiment, a credit evaluation process is performed on the logistics driver based on the transportation data and the user evaluation data to obtain first credit evaluation data of the logistics driver, including:

[0011] Based on the positive transportation data in the transportation data and the positive evaluation data in the user evaluation data, performing credit increase processing on the first basic credit data of the logistics driver to obtain first evaluation data of the logistics driver;

[0012] Based on the negative transportation data in the transportation data and the negative evaluation data in the user evaluation data, performing credit reduction processing on the second basic credit data of the logistics driver to obtain second evaluation data of the logistics driver;

[0013] The first evaluation data and the second evaluation data are integrated to obtain the first credit evaluation data of the logistics driver.

[0014] In one embodiment, based on the positive transportation data in the transportation data and the positive evaluation data in the user evaluation data, the first basic credit data of the logistics driver is subjected to credit increase processing to obtain the first evaluation data of the logistics driver, including:

[0015] If the forward transport data is greater than a preset transport data threshold, performing credit increase processing on the first basic credit data based on the forward transport data to obtain increased credit data of the first basic credit data;

[0016] If the positive evaluation data is greater than a preset evaluation data threshold, the increased credit data is subjected to credit increase processing based on the positive evaluation data to obtain the first evaluation data of the logistics driver.

[0017] In one embodiment, a stepwise credit evaluation process is performed on the logistics driver based on the order data to obtain second credit evaluation data of the logistics driver, including:

[0018] If it is detected that the order data is not less than the first order quantity threshold, the third basic credit data of the logistics driver is credit-increased based on the first-tier evaluation data and the order data to obtain the second credit evaluation data of the logistics driver;

[0019] If it is detected that the order data is less than the first order quantity threshold, the third basic credit data of the logistics driver is credit-increased based on the second-step evaluation data and the order data to obtain the second credit evaluation data of the logistics driver.

[0020] In one embodiment, obtaining the logistics driver's transportation data, user evaluation data, and order data for transporting power materials within a target time period includes:

[0021] Obtaining the original transportation data, original user evaluation data, and original order data of the logistics driver for transporting the power materials within the target time period;

[0022] Data consistency processing is performed on the original transportation data, the original user evaluation data, and the original order data to obtain the transportation data, the user evaluation data, and the order data.

[0023] In one embodiment, after obtaining the target credit evaluation result of the logistics driver in the next time period after the target time period based on the first credit evaluation data and the second credit evaluation data, the method further includes:

[0024] Visualizing the target credit evaluation result to obtain a visualization report of the target credit evaluation result;

[0025] Based on the order data, the visualization report is processed to obtain a credit evaluation analysis report of the logistics driver;

[0026] The credit evaluation analysis report is sent to the logistics driver's account.

[0027] In a second aspect, the present application also provides a logistics driver evaluation device. The device includes:

[0028] The data acquisition module is used to obtain the transportation data of logistics drivers within the target time period, user evaluation data, and order data for transporting power materials;

[0029] A first evaluation module is configured to perform a credit evaluation on the logistics driver based on the transportation data and the user evaluation data to obtain first credit evaluation data of the logistics driver;

[0030] A second evaluation module is configured to perform a step-by-step credit evaluation on the logistics driver based on the order data to obtain second credit evaluation data of the logistics driver;

[0031] The result determination module is used to obtain the target credit evaluation result of the logistics driver in the next time period of the target time period based on the first credit evaluation data and the second credit evaluation data.

[0032] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are performed:

[0033] Obtain logistics drivers' transportation data, user evaluation data, and order data for transporting power supplies within the target time period;

[0034] Performing a credit evaluation on the logistics driver based on the transportation data and the user evaluation data to obtain first credit evaluation data of the logistics driver;

[0035] Performing a step-by-step credit evaluation process on the logistics driver based on the order data to obtain second credit evaluation data of the logistics driver;

[0036] A target credit evaluation result of the logistics driver in the next time period after the target time period is obtained based on the first credit evaluation data and the second credit evaluation data.

[0037] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the following steps:

[0038] Obtain logistics drivers' transportation data, user evaluation data, and order data for transporting power supplies within the target time period;

[0039] Performing a credit evaluation on the logistics driver based on the transportation data and the user evaluation data to obtain first credit evaluation data of the logistics driver;

[0040] Performing a step-by-step credit evaluation process on the logistics driver based on the order data to obtain second credit evaluation data of the logistics driver;

[0041] A target credit evaluation result of the logistics driver in the next time period after the target time period is obtained based on the first credit evaluation data and the second credit evaluation data.

[0042] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the following steps:

[0043] Obtain logistics drivers' transportation data, user evaluation data, and order data for transporting power supplies within the target time period;

[0044] Performing a credit evaluation on the logistics driver based on the transportation data and the user evaluation data to obtain first credit evaluation data of the logistics driver;

[0045] Performing a step-by-step credit evaluation process on the logistics driver based on the order data to obtain second credit evaluation data of the logistics driver;

[0046] A target credit evaluation result of the logistics driver in the next time period after the target time period is obtained based on the first credit evaluation data and the second credit evaluation data.

[0047] The above-described logistics driver evaluation method, device, computer device, storage medium, and computer program product obtain a logistics driver's transportation data, user evaluation data, and order data for the transportation of power supplies during a target time period; perform a credit evaluation on the logistics driver based on the transportation data and user evaluation data, obtaining first credit evaluation data for the logistics driver; perform a step-by-step credit evaluation on the logistics driver based on the order data, obtaining second credit evaluation data for the logistics driver; and obtain a target credit evaluation result for the logistics driver for the next time period after the target time period based on the first and second credit evaluation data. This method enables a multi-dimensional credit evaluation of the logistics driver based on the logistics driver's transportation data and user evaluation data, providing a more comprehensive reflection of the logistics driver's work performance and improving the accuracy of the logistics driver's credit evaluation. Furthermore, based on the order data for the transportation of power supplies undertaken by the logistics driver, a more in-depth credit evaluation of the logistics driver is performed, thereby enhancing the importance of power supplies in the credit evaluation, thereby encouraging logistics drivers to accept more orders for the transportation of power supplies, and thereby improving the power supply transportation capacity of power grid enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 1 is a flow chart of a method for evaluating a logistics driver in one embodiment;

[0049] Figure 2 This is a flowchart of the steps for performing credit evaluation on a logistics driver in one embodiment;

[0050] Figure 3 is a flowchart of a method for evaluating logistics drivers in another embodiment;

[0051] Figure 4 is a structural block diagram of a logistics driver evaluation device in one embodiment;

[0052] Figure 5 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0054] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0055] In one embodiment, Figure 1 As shown, a method for evaluating logistics drivers is provided. This embodiment uses the method applied to a server as an example. It is understood that the method can also be applied to a terminal, or to a system including a terminal and a server, and implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0056] Step S101: Obtain the transportation data, user evaluation data, and order data of the logistics driver for transporting power materials within the target time period.

[0057] Among them, logistics drivers refer to people responsible for transporting and distributing materials.

[0058] The target time period refers to a designated period of time used to provide reference data for evaluating the current credit status of logistics drivers. For example, the target time period includes but is not limited to the past month, the past week, or the past three days.

[0059] Transportation data refers to the materials and material information transported by logistics drivers, as well as logistics information on the arrival of the transported goods. For example, transportation data includes but is not limited to material status, delivery timeliness, order rush data, late pickup rate, and late arrival rate.

[0060] User evaluation data refers to the evaluation information of logistics drivers by material suppliers. For example, user evaluation data includes but is not limited to the rate of positive reviews and the rate of negative reviews.

[0061] Power supplies refer to supplies related to the power grid industry. For example, power supplies can include various types of power equipment. Order data can include information describing the number of orders for transporting power supplies.

[0062] Specifically, the server can obtain the original transportation data, original user evaluation data, and original order data of logistics drivers for the target time period (such as last month) from the power industry's transportation capacity management platform, and then clean and preprocess the original transportation data, original user evaluation data, and original order data to obtain processed transportation data, user evaluation data, and order data.

[0063] Step S102: Perform credit evaluation on the logistics driver based on the transportation data and user evaluation data to obtain first credit evaluation data of the logistics driver.

[0064] The first credit evaluation data refers to evaluation data on the transportation performance of a logistics driver transporting non-electrical materials. For example, the credit evaluation data may be a credit score, which is used to evaluate the logistics driver's material transportation service.

[0065] Specifically, after a logistics driver registers on the power grid company's capacity management platform, basic credit data (such as first, second, and third basic credit data) will be issued to their account. The server can use transportation data and user evaluation data to increase or decrease the logistics driver's basic credit data, ultimately calculating the logistics driver's first credit rating data.

[0066] Step S103: Perform a step-by-step credit evaluation on the logistics driver based on the order data to obtain the second credit evaluation data of the logistics driver.

[0067] Among them, the second credit evaluation data refers to the evaluation data on the transportation conditions of logistics drivers transporting electric power materials.

[0068] Specifically, the materials that logistics drivers undertake to transport during the target time include general non-electrical materials as well as electrical materials. Based on the order data of logistics drivers undertaking the transportation of electrical materials during the target time period, a dedicated analysis of the logistics drivers' electrical material transportation services during the target time period is conducted to obtain the logistics drivers' second credit rating data.

[0069] Step S104: Obtain the target credit evaluation result of the logistics driver in the next time period of the target time period based on the first credit evaluation data and the second credit evaluation data.

[0070] Specifically, the server can fuse the first credit evaluation data and the second credit evaluation data. For example, the first credit evaluation data and the second credit evaluation data can be added together. The first credit evaluation data and the second credit evaluation data can also be weighted and summed according to the weights corresponding to the first credit evaluation data and the second credit evaluation data. For example, a higher weight can be set for the second credit evaluation data of electric power materials than the first credit evaluation data to increase the proportion of the second credit evaluation data of electric power materials in the total credit evaluation results. The server then obtains the target credit evaluation results of the logistics driver in the next time period (such as this month) of the target time period (such as last month).

[0071] In the above-mentioned logistics driver evaluation method, the logistics driver's transportation data, user evaluation data, and order data for the transportation of power supplies accepted during a target time period are obtained; the logistics driver's credit is evaluated based on the transportation data and user evaluation data to obtain the logistics driver's first credit evaluation data; the logistics driver's credit is evaluated in a step-by-step manner based on the order data to obtain the logistics driver's second credit evaluation data; and the logistics driver's target credit evaluation result for the next time period after the target time period is obtained based on the first credit evaluation data and the second credit evaluation data. This method enables a multi-dimensional credit evaluation of the logistics driver based on the logistics driver's transportation data and user evaluation data, providing a more comprehensive reflection of the logistics driver's work performance and improving the accuracy of the logistics driver's credit evaluation. Furthermore, the logistics driver's order data for the transportation of power supplies accepted by the logistics driver allows for a more in-depth credit evaluation of the logistics driver, thereby enhancing the importance of power supplies in the credit evaluation, thereby encouraging logistics drivers to accept more orders for the transportation of power supplies, and thereby improving the power supply transportation capacity of power grid enterprises.

[0072] In one embodiment, Figure 2 As shown, in the above step S102, the logistics driver is evaluated for credit based on the transportation data and the user evaluation data to obtain the first credit evaluation data of the logistics driver, which specifically includes the following content:

[0073] Step S201: Based on the positive transportation data in the transportation data and the positive evaluation data in the user evaluation data, the first basic credit data of the logistics driver is processed for credit increase to obtain the first evaluation data of the logistics driver.

[0074] Transportation data includes both positive and negative data. Positive data refers to data with a higher value, indicating a better evaluation (i.e., a positive correlation). Negative data refers to data with a lower value, indicating a better evaluation (i.e., a negative correlation). For example, positive data can include order-grabbing enthusiasm and bid-concluded success rates. Negative data can include delayed pickup rates and late arrival rates.

[0075] User evaluation data contains both positive and negative evaluation data. Positive evaluation data refers to user evaluation data where larger values indicate better evaluation results (i.e., positive correlation). Negative evaluation data refers to user evaluation data where smaller values indicate better evaluation results (i.e., negative correlation). For example, positive evaluation data can be the percentage of positive reviews. Negative evaluation data can be the percentage of negative reviews.

[0076] Specifically, the server can pre-define credit evaluation metrics. The server can extract positive and negative transportation data from transportation data, or calculate the positive and negative transportation data based on the transportation data. Similarly, the server can extract positive and negative evaluation data from user evaluation data, or calculate the positive and negative evaluation data based on the user evaluation data. For example, after a logistics driver completes a vehicle dispatch task, the supplier can evaluate the driver's dispatch task. The server can then calculate the positive evaluation rate by dividing the number of positive reviews in the user evaluation data that exceed a positive score threshold (e.g., a maximum score of 4 points) by the total number of dispatches performed by the logistics driver during the target time period. After a logistics driver registers on the transportation management platform, a first basic credit score (e.g., 5 points) is assigned to the driver's account. The server uses the positive transportation data and positive evaluation data to increase the first basic credit score of the logistics driver accordingly, thereby obtaining the first evaluation data of the logistics driver.

[0077] Step S202: Based on the negative transportation data in the transportation data and the negative evaluation data in the user evaluation data, the second basic credit data of the logistics driver is subjected to credit reduction processing to obtain the second evaluation data of the logistics driver.

[0078] Specifically, the server may also send a second basic credit score (e.g., 0 points) to the logistics driver's account. The server may combine multiple negative transportation data and negative evaluation data to comprehensively calculate the logistics driver's second evaluation data, such as negative transportation data such as late pickup rate, late arrival rate, and negative review rate.

[0079] In actual applications, the server can obtain the number of dispatch orders for which the logistics driver signed in overtime within the target time period, and then calculate the logistics driver's late pickup rate within the target time period by dividing the total number of dispatch orders for which the sign-in time period was overtime. If the logistics driver's late pickup rate within the target time period exceeds a preset late pickup rate threshold (e.g., 2%), the first negative credit data (e.g., 5 points) is subtracted from the logistics driver's second basic credit data (e.g., 0 points) to obtain the first reduced credit data. The server can obtain the logistics driver's actual arrival time at the order destination within the target time period, and calculate the logistics driver's late arrival rate within the target time period by dividing the number of actual arrival times greater than or equal to the order's scheduled arrival time by the total number of dispatches within the target time period. If the logistics driver's late arrival rate within the target time period exceeds a preset late arrival rate threshold (e.g., 2%), the second negative credit data (e.g., 5 points) is subtracted from the first reduced credit data (e.g., 0 points or -5 points) to obtain the second reduced credit data. The server can calculate the negative review rate by dividing the number of negative reviews in the user evaluation data with an evaluation score equal to or less than the negative review score threshold (for example, the full score is 5 points and the negative review score threshold is 2 points) by the total number of vehicle dispatches in the target time period; if the negative review rate of the logistics driver in the target time period is higher than the preset negative review rate threshold (for example, 2%), the second reduced credit data (for example, -10 points) is subtracted from the third negative credit data (for example, 5 points) to obtain the second evaluation data of the logistics driver.

[0080] Step S203: The first evaluation data and the second evaluation data are integrated to obtain the first credit evaluation data of the logistics driver.

[0081] Specifically, the server may add the first evaluation data and the second evaluation data to calculate the first credit evaluation data of the logistics driver.

[0082] In this embodiment, the first basic credit data of a logistics driver is increased based on the positive transportation data in the transportation data and the positive evaluation data in the user evaluation data, resulting in positive first evaluation data for the logistics driver. This first evaluation data reflects the excellent work performance of the logistics driver. Furthermore, the second basic credit data of the logistics driver is decreased based on the negative transportation data in the transportation data and the negative evaluation data in the user evaluation data, resulting in negative second evaluation data for the logistics driver. This second evaluation data reflects the shortcomings of the logistics driver's transportation service. Finally, the first evaluation data and the second evaluation data are combined to obtain the first credit evaluation data of the logistics driver, thus effectively obtaining the basic first credit evaluation data of the logistics driver.

[0083] In one embodiment, the above step S201, based on the positive transportation data in the transportation data and the positive evaluation data in the user evaluation data, performs credit increase processing on the first basic credit data of the logistics driver to obtain the first evaluation data of the logistics driver, specifically including the following contents: if the positive transportation data is greater than the preset transportation data threshold, then based on the positive transportation data, the first basic credit data is credit increased to obtain the increased credit data of the first basic credit data; if the positive evaluation data is greater than the preset evaluation data threshold, then based on the positive evaluation data, the increased credit data is credit increased to obtain the first evaluation data of the logistics driver.

[0084] In actual applications, the server can combine multiple forward transportation data to comprehensively calculate the first evaluation data, such as the number of bidding orders, the rate of positive reviews, the bidding transaction rate and other forward transportation data. Specifically, if the number of orders that a logistics driver has bid for (including the number of orders that have been completed after bidding for the order and the number of orders that have not been completed after bidding for the order) exceeds a preset order threshold (for example, 1 order), the first basic credit data can be increased according to the order bidding number range to which the number of orders that the logistics driver has bid for. For example, if the number of orders that the logistics driver has bid for belongs to the order bidding number range [1,3), the first basic credit data (for example, 5 points) is added to the first positive credit data (for example, 1 point); if the number of orders that the logistics driver has bid for belongs to the order bidding number range [3,5), the first basic credit data (for example, 5 points) is added to the second positive credit data (for example, 2 points); if the number of orders that the logistics driver has bid for belongs to the order bidding number range [5,7), the first basic credit data (for example, 5 points) is added to the third positive credit data (for example, 3 points); if the number of orders that the logistics driver has bid for belongs to the order bidding number range [7,+∞), the first basic credit data (for example, 5 points) is added to the fourth positive credit data (for example, 11 points). points); the server obtains first increased credit data for the first basic credit data (e.g., 5+1=6, 5+2=7, 5+3=8, 5+11=16). If the logistics driver's positive review rate is greater than a preset evaluation data threshold (e.g., 85%), the first increased credit data is credit-added based on the positive review data to obtain second increased credit data for the first basic credit data. The server may also calculate the logistics driver's bid success rate during the target time period by dividing the number of orders completed by the logistics driver during the target time period by the number of orders participating in the bidding during the target time period. If the bidding success rate is greater than a preset success rate threshold (e.g., 90%), the second increased credit data is added to the fifth credit score (e.g., 6 points) to obtain the logistics driver's first evaluation data.

[0085] In this embodiment, the first evaluation data of the logistics driver is comprehensively determined by the number of orders the logistics driver bids for, the praise rate, and the bidding success rate. This can evaluate the positive transportation data of the logistics driver from multiple dimensions, comprehensively reflect the excellent work performance of the logistics driver, and thus improve the accuracy of the evaluation data.

[0086] In one embodiment, the above step S103 performs a step-by-step credit evaluation on the logistics driver based on the order data to obtain the second credit evaluation data of the logistics driver, which specifically includes the following contents: if it is detected that the order data is not less than the first order quantity threshold, then the third basic credit data of the logistics driver is credit-increased based on the first-step evaluation data and the order data to obtain the second credit evaluation data of the logistics driver; if it is detected that the order data is less than the first order quantity threshold, then the third basic credit data of the logistics driver is credit-increased based on the second-step evaluation data and the order data to obtain the second credit evaluation data of the logistics driver.

[0087] The order data refers to information describing the order for transporting power supplies. For example, the order data includes the order quantity, name, and time limit of the power supplies.

[0088] Specifically, the server counts the number of orders for transporting power supplies undertaken by the logistics driver during the target time period. If the order quantity is determined to be no less than a first order volume threshold, the server may increase the logistics driver's third basic credit data based on the first-tier evaluation data and the order data, calculating the driver's second credit rating. For example, the following formula may be used to calculate the logistics driver's second credit rating: Second credit rating = Order data / First order volume threshold * First-tier evaluation data + Third basic credit data. This means that for every order completed exceeding the first order volume threshold (e.g., 5 orders), the driver's first-tier evaluation data is rewarded (e.g., 2 points). If the order data is determined to be less than the first order volume threshold, the server may increase the logistics driver's third basic credit data based on the second-tier evaluation data and the order data, calculating the driver's second credit rating. For example, the following formula may be used to calculate the logistics driver's second credit rating: Second credit rating = Order data / Second order volume threshold * Second-tier evaluation data + Third basic credit data. This means that for every order completed exceeding the second order volume threshold (e.g., 3 orders), the driver's second-tier evaluation data is rewarded (e.g., 1 point).

[0089] In this embodiment, through the first order quantity threshold, the logistics driver is rewarded with two different evaluation data, the first-tier evaluation data and the second-tier evaluation data, for the number of orders for transporting electric power materials undertaken within the target time period, thereby realizing a tiered reward of different levels for electric power material transportation orders, so as to increase the proportion of the second credit evaluation data of electric power materials in the total target credit evaluation results, effectively enhancing the importance of electric power materials in the credit evaluation system, thereby encouraging logistics drivers to undertake more transportation orders for electric power materials, and thus improving the transportation capacity of electric power materials of power grid enterprises.

[0090] In one embodiment, the above step S101, which obtains the logistics driver's transportation data, user evaluation data, and order data for transporting electric power materials within the target time period, specifically includes the following contents: obtaining the logistics driver's original transportation data, original user evaluation data, and original order data for transporting electric power materials within the target time period; performing data consistency processing on the original transportation data, original user evaluation data, and original order data to obtain transportation data, user evaluation data, and order data.

[0091] Specifically, after the logistics driver's authorization, the power grid company's capacity management platform automatically collects the driver's raw transportation data, raw user evaluation data, and raw order data. The server can retrieve the logistics driver's raw transportation data, raw user evaluation data, and raw order data for power supplies transported during the target time period through legal and compliant backend methods. The server performs data consistency checks on the raw transportation data, raw user evaluation data, and raw order data. For example, the server can first cleanse the raw transportation data, raw user evaluation data, and raw order data to remove duplicate, erroneous, or invalid data. The cleansed transportation data, cleansed user evaluation data, and cleansed order data are then standardized to ensure data accuracy and consistency, ultimately outputting the transportation data, user evaluation data, and order data.

[0092] In this embodiment, by pre-processing the original transportation data, original user evaluation data, and original order data of the logistics drivers within the target time period for the transportation of electric power materials, the accuracy and consistency of the processed transportation data, user evaluation data, and order data can be improved, thereby effectively improving the data quality and providing a reliable processing basis for subsequent credit evaluation processing.

[0093] In one embodiment, in the above step S104, after obtaining the target credit evaluation result of the logistics driver in the next time period of the target time period based on the first credit evaluation data and the second credit evaluation data, it also includes: visualizing the target credit evaluation result to obtain a visualization report of the target credit evaluation result; processing the visualization report based on the order data to obtain a credit evaluation analysis report of the logistics driver; and sending the credit evaluation analysis report to the logistics driver's account.

[0094] Specifically, after obtaining the target credit evaluation results, the server can also convert the target credit evaluation results into a visual report. Based on the order data, the server can also analyze the logistics driver's acceptance and transportation of power-related materials during the target time period to obtain an analysis result of the acceptance and transportation of power-related materials. This analysis result is then input into a text generation model to obtain a text analysis result of the acceptance and transportation of power-related materials. The text analysis results are then used to update and optimize the visual report, ultimately resulting in a credit evaluation analysis report for the logistics driver. The server can also send the credit evaluation analysis report to the logistics driver's account, allowing the logistics driver to review their credit status and for users to view.

[0095] Furthermore, the server dynamically adjusts the dispatch priority of logistics drivers based on the target credit evaluation results. For example, the dispatch priority of logistics drivers with better target credit evaluation results is increased, and orders are assigned to logistics drivers with better target credit evaluation results first.

[0096] In this embodiment, the target credit evaluation results are visualized to form a visualization report of the target credit evaluation results; the visualization report is further improved based on the order data to obtain a credit evaluation analysis report that focuses on the transportation of power materials; finally, the credit evaluation analysis report is sent to the logistics driver's account for the logistics driver and the user to view, thereby realizing the regular update of the logistics driver's credit evaluation status and motivating the logistics driver to improve the quality of service.

[0097] In one embodiment, Figure 3 As shown, another evaluation method for logistics drivers is provided. Taking the application of this method to a server as an example, the method includes the following steps:

[0098] Step S301: Obtain the logistics driver's transportation data, user evaluation data, and order data for transporting power materials within the target time period.

[0099] Step S302: Based on the positive transportation data in the transportation data and the positive evaluation data in the user evaluation data, the first basic credit data of the logistics driver is processed for credit increase to obtain the first evaluation data of the logistics driver.

[0100] Step S303: Based on the negative transportation data in the transportation data and the negative evaluation data in the user evaluation data, the second basic credit data of the logistics driver is subjected to credit reduction processing to obtain the second evaluation data of the logistics driver.

[0101] Step S304: The first evaluation data and the second evaluation data are integrated to obtain the first credit evaluation data of the logistics driver.

[0102] Step S305: If it is detected that the order data is not less than the first order quantity threshold, the third basic credit data of the logistics driver is credit-increased according to the first-step evaluation data and the order data to obtain the second credit evaluation data of the logistics driver.

[0103] Step S306: If it is detected that the order data is less than the first order quantity threshold, the third basic credit data of the logistics driver is credit-increased according to the second-tier evaluation data and the order data to obtain the second credit evaluation data of the logistics driver.

[0104] Step S307: Obtain the target credit evaluation result of the logistics driver in the next time period of the target time period based on the first credit evaluation data and the second credit evaluation data.

[0105] The above-mentioned logistics driver evaluation method can achieve the following beneficial effects: It can conduct a multi-dimensional credit evaluation of logistics drivers based on their transportation data and user evaluation data, providing a more comprehensive reflection of their work performance and improving the accuracy of the credit evaluation of logistics drivers. It also uses data on the orders for power supplies that logistics drivers have accepted to conduct a more in-depth credit evaluation of logistics drivers, thereby enhancing the importance of power supplies in credit evaluations, thereby encouraging logistics drivers to accept more power supply transportation orders and, in turn, improving the power supply transportation capacity of power grid companies.

[0106] In order to more clearly illustrate the evaluation method for logistics drivers provided by the embodiment of the present disclosure, the following is a specific example of the evaluation method for logistics drivers. Another evaluation method for logistics drivers is provided, which can be applied to a server and specifically includes the following content:

[0107] Data collection: Through the power grid company's transportation management platform, basic information such as logistics drivers' transportation order data (such as license plate number, driver information, delivery time, and cargo status), order grabbing data (number of bidding orders, transaction rate), and user evaluation data (positive review rate and negative review rate) is automatically collected.

[0108] Data cleaning and preprocessing: Clean the collected data, remove duplicate, erroneous or invalid data records, and perform standardization to ensure data accuracy and consistency.

[0109] Metric Calculation and Credit Score Updates: Drivers' metrics are calculated monthly based on the evaluation system's rules. This automated process supports addition and subtraction logic, and the driver's credit score is updated accordingly. This calculation is automated using a pre-set algorithm model, ensuring the objectivity and timeliness of the evaluation results.

[0110] Feedback and public announcement of evaluation results: The evaluation results of logistics drivers will be fed back to the logistics drivers' accounts in the form of reports, and will be counted in the transportation management background, so that logistics drivers can understand their credit status in a timely manner, and also provide a reference for customers.

[0111] This embodiment can achieve the following beneficial effects: 1. Through multi-dimensional indicator analysis, it covers multiple aspects such as the credit basis, service quality, transaction data, and transaction volume of logistics drivers, which can comprehensively reflect the work performance and credit level of logistics drivers and realize refined management. 2. Through clear quantitative indicators and automated data processing processes, the interference of human factors on the evaluation results is reduced, and the objectivity and fairness of the evaluation are guaranteed. 3. The evaluation results are updated regularly every month, which can timely reflect the changes in the credit status of logistics drivers, so that the evaluation system has good dynamic adaptability. The logistics driver scheduling priority is dynamically adjusted through credit points to encourage logistics drivers to improve service quality. 4. By using the number of orders for electric power materials as a bonus item, logistics drivers can be encouraged to take on more such orders, thereby improving the power grid company's ability to guarantee the transportation of electric power materials. At the same time, it also reflects the recognition and incentives for the professional capabilities of logistics drivers in the field of electric power material transportation.

[0112] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0113] Based on the same inventive concept, embodiments of the present application also provide a logistics driver evaluation device for implementing the aforementioned logistics driver evaluation method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more logistics driver evaluation device embodiments provided below can be found in the limitations of the logistics driver evaluation method described above and will not be repeated here.

[0114] In one embodiment, Figure 4 As shown, a logistics driver evaluation device 400 is provided, comprising: a data acquisition module 401, a first evaluation module 402, a second evaluation module 403 and a result determination module 404, wherein:

[0115] The data acquisition module 401 is used to obtain the transportation data, user evaluation data and order data of the logistics driver for transporting power materials within the target time period.

[0116] The first evaluation module 402 is used to perform credit evaluation on the logistics driver based on the transportation data and the user evaluation data to obtain the first credit evaluation data of the logistics driver.

[0117] The second evaluation module 403 is used to perform a step-by-step credit evaluation process on the logistics driver based on the order data to obtain the second credit evaluation data of the logistics driver.

[0118] The result determination module 404 is used to obtain the target credit evaluation result of the logistics driver in the next time period of the target time period based on the first credit evaluation data and the second credit evaluation data.

[0119] In one embodiment, the first evaluation module 402 is also used to perform credit increase processing on the first basic credit data of the logistics driver based on the positive transportation data in the transportation data and the positive evaluation data in the user evaluation data, so as to obtain the first evaluation data of the logistics driver; perform credit reduction processing on the second basic credit data of the logistics driver based on the negative transportation data in the transportation data and the negative evaluation data in the user evaluation data, so as to obtain the second evaluation data of the logistics driver; and fuse the first evaluation data and the second evaluation data to obtain the first credit evaluation data of the logistics driver.

[0120] In one embodiment, the logistics driver evaluation device 400 also includes a positive evaluation module, which is used to perform credit increase processing on the first basic credit data based on the positive transportation data if the positive transportation data is greater than the preset transportation data threshold, so as to obtain the increased credit data of the first basic credit data; if the positive evaluation data is greater than the preset evaluation data threshold, then the credit increase processing is performed on the increased credit data based on the positive evaluation data to obtain the first evaluation data of the logistics driver.

[0121] In one embodiment, the second evaluation module 403 is also used to, if it is detected that the order data is not less than the first order quantity threshold, perform credit increase processing on the third basic credit data of the logistics driver based on the first-step evaluation data and the order data to obtain the second credit evaluation data of the logistics driver; if it is detected that the order data is less than the first order quantity threshold, perform credit increase processing on the third basic credit data of the logistics driver based on the second-step evaluation data and the order data to obtain the second credit evaluation data of the logistics driver.

[0122] In one embodiment, the data acquisition module 401 is also used to obtain the original transportation data, original user evaluation data and original order data of the logistics driver for the transportation of power materials within the target time period; perform data consistency processing on the original transportation data, original user evaluation data and original order data to obtain transportation data, user evaluation data and order data.

[0123] In one embodiment, the logistics driver evaluation device 400 also includes a result feedback module, which is used to visualize the target credit evaluation results to obtain a visualization report of the target credit evaluation results; based on the order data, the visualization report is processed to obtain a credit evaluation analysis report of the logistics driver; and the credit evaluation analysis report is sent to the logistics driver's account.

[0124] Each module in the aforementioned logistics driver evaluation device can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a computer device memory in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0125] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 5As shown. The computer device includes a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, memory and input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. 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, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store data such as transportation data, user evaluation data, order data, target credit evaluation results, etc. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for evaluating logistics drivers is implemented.

[0126] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0127] In one embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0128] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0129] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0130] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the 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-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, and the like.

[0131] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0132] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for evaluating logistics drivers, characterized in that: The method comprises: Obtain logistics drivers' transportation data, user evaluation data, and order data for transporting power supplies within the target time period; Performing a credit evaluation on the logistics driver based on the transportation data and the user evaluation data to obtain first credit evaluation data of the logistics driver; Performing a step-by-step credit evaluation process on the logistics driver based on the order data to obtain second credit evaluation data of the logistics driver; A target credit evaluation result of the logistics driver in the next time period after the target time period is obtained based on the first credit evaluation data and the second credit evaluation data.

2. The method according to claim 1, characterized in that The step of performing credit evaluation on the logistics driver based on the transportation data and the user evaluation data to obtain first credit evaluation data of the logistics driver includes: Based on the positive transportation data in the transportation data and the positive evaluation data in the user evaluation data, performing credit increase processing on the first basic credit data of the logistics driver to obtain first evaluation data of the logistics driver; Based on the negative transportation data in the transportation data and the negative evaluation data in the user evaluation data, performing credit reduction processing on the second basic credit data of the logistics driver to obtain second evaluation data of the logistics driver; The first evaluation data and the second evaluation data are integrated to obtain the first credit evaluation data of the logistics driver.

3. The method according to claim 2, characterized in that The step of performing credit increase processing on the first basic credit data of the logistics driver based on the positive transportation data in the transportation data and the positive evaluation data in the user evaluation data to obtain the first evaluation data of the logistics driver includes: If the forward transport data is greater than a preset transport data threshold, performing credit increase processing on the first basic credit data based on the forward transport data to obtain increased credit data of the first basic credit data; If the positive evaluation data is greater than a preset evaluation data threshold, the increased credit data is subjected to credit increase processing based on the positive evaluation data to obtain the first evaluation data of the logistics driver.

4. The method according to claim 1, wherein The stepwise credit evaluation of the logistics driver is performed based on the order data to obtain second credit evaluation data of the logistics driver, including: If it is detected that the order data is not less than the first order quantity threshold, the third basic credit data of the logistics driver is credit-increased based on the first-tier evaluation data and the order data to obtain the second credit evaluation data of the logistics driver; If it is detected that the order data is less than the first order quantity threshold, the third basic credit data of the logistics driver is credit-increased based on the second-step evaluation data and the order data to obtain the second credit evaluation data of the logistics driver.

5. The method according to claim 1, wherein The acquisition of the logistics driver's transportation data, user evaluation data, and order data for transporting power materials within the target time period includes: Obtaining the original transportation data, original user evaluation data, and original order data of the logistics driver for transporting the power materials within the target time period; Data consistency processing is performed on the original transportation data, the original user evaluation data, and the original order data to obtain the transportation data, the user evaluation data, and the order data.

6. The method according to any one of claims 1 to 5, characterized in that After obtaining the target credit evaluation result of the logistics driver in the next time period after the target time period according to the first credit evaluation data and the second credit evaluation data, the method further includes: Visualizing the target credit evaluation result to obtain a visualization report of the target credit evaluation result; Based on the order data, the visualization report is processed to obtain a credit evaluation analysis report of the logistics driver; The credit evaluation analysis report is sent to the logistics driver's account.

7. A logistics driver evaluation device, characterized in that: The device comprises: The data acquisition module is used to obtain the transportation data of logistics drivers within the target time period, user evaluation data, and order data for transporting power materials; A first evaluation module is configured to perform a credit evaluation on the logistics driver based on the transportation data and the user evaluation data to obtain first credit evaluation data of the logistics driver; A second evaluation module is configured to perform a step-by-step credit evaluation on the logistics driver based on the order data to obtain second credit evaluation data of the logistics driver; The result determination module is used to obtain the target credit evaluation result of the logistics driver in the next time period of the target time period based on the first credit evaluation data and the second credit evaluation data.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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