Service value identification method and device of transportation service, equipment and storage medium

Abstract

The embodiment of the invention provides a service value identification method and device of a transportation service, equipment and a storage medium, and the method comprises the steps: obtaining a reported service value of a target transportation service, and determining a pricing rule of the target transportation service according to the reported service value of the target transportation service; determining a service area corresponding to the target transportation service to obtain a target service area; determining a historical transportation service of the target service area, and obtaining an actual service value of the historical transportation service; predicting the service value of the historical transportation service according to a pricing rule to obtain the predicted service value of the historical transportation service; and identifying whether the reported service value of the target transportation service is normal or not according to the actual service value and the predicted service value of the historical transportation service. According to the embodiment of the invention, whether the submission service value of the target transportation service is normal or not can be automatically identified and audited from a global perspective, the objectivity and accuracy of audit are improved, and the audit efficiency is improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of logistics technology, and in particular to a method, apparatus, equipment and storage medium for identifying the service value of transportation services. Background Technology

[0002] In the express delivery pickup and delivery process, couriers provide services at both ends. At the pickup end, couriers collect goods from the sender and transport them to the designated station; at the delivery end, couriers retrieve goods from the designated station and deliver them to the recipient. Pricing is set for the pickup and delivery services provided. To enhance service flexibility and competitiveness, each region is allowed to set its own prices.

[0003] In developing this invention, the inventors discovered that regionally determined pricing can lead to prices that are either too high or too low. Low pricing can result in lost delivery personnel, while high pricing can lead to lost customers. Currently, the primary method for addressing this issue is manual review of submitted prices. However, manual review is inherently subjective and inefficient. Summary of the Invention

[0004] This invention provides a service value identification method, apparatus, device, and storage medium for transportation services, which can automatically identify and verify whether the reported service value of a target transportation service is normal from a global perspective, thereby improving the objectivity and accuracy of the verification and increasing the efficiency of the verification.

[0005] In a first aspect, the service value identification method for transportation services provided in embodiments of the present invention includes:

[0006] Obtain the reported service value of the target transportation service, and determine the pricing rules for the target transportation service based on the reported service value of the target transportation service;

[0007] Determine the service area corresponding to the target transportation service to obtain the target service area;

[0008] Determine the historical transportation services of the target service area and obtain the actual service value of the historical transportation services;

[0009] The estimated service value of the historical transportation service is obtained by predicting the service value of the historical transportation service according to the pricing rules.

[0010] The reported service value of the target transportation service is determined based on the actual and expected service values ​​of the historical transportation services.

[0011] Secondly, the service value identification device for transportation services provided in the embodiments of the present invention includes:

[0012] The pricing rule determination module is used to obtain the reported service value of the target transportation service and determine the pricing rule of the target transportation service based on the reported service value of the target transportation service.

[0013] The service area determination module is used to determine the service area corresponding to the target transportation service, thereby obtaining the target service area;

[0014] The acquisition module is used to determine the historical transportation services of the target service area and obtain the actual service value of the historical transportation services;

[0015] The value prediction module is used to predict the service value of the historical transportation service according to the pricing rules, and to obtain the expected service value of the historical transportation service.

[0016] The identification module is used to identify whether the reported service value of the target transportation service is normal based on the actual service value and the expected service value of the historical transportation service.

[0017] Thirdly, the electronic device provided in the embodiments of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the service value identification method for transportation services as described in any embodiment of the present invention.

[0018] Fourthly, the computer-readable storage medium provided in the embodiments of the present invention stores a computer program thereon, which, when executed by a processor, implements the service value identification method for transportation services as described in any embodiment of the present invention.

[0019] The solution of this invention can analyze the reported service value of a target transportation service in a target service area to obtain the pricing rules for the target transportation service. These pricing rules are then applied to historical transportation services in the target service area to predict the expected service value (i.e., the expected price) of historical transportation services calculated according to the target transportation service's pricing rules. The reported service value of the target transportation service is then identified based on the expected and actual service values ​​(i.e., the actual price) of the historical transportation services. By comparing the service values ​​of historical transportation services before and after the introduction of the target transportation service's pricing rules, the system achieves automatic identification and review of the reported service value of the target transportation service from a global perspective, improving the objectivity and accuracy of the review process and increasing review efficiency. Attached Figure Description

[0020] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1a This is a flowchart illustrating a service value identification method for transportation services provided in an embodiment of the present invention.

[0022] Figure 1b This is an example diagram of an application scenario of the service value identification method for transportation services provided in this embodiment of the invention;

[0023] Figure 2a This is a schematic diagram of a process for clustering the actual service value of historical transportation services to find cluster centers, provided by an embodiment of the present invention.

[0024] Figure 2b This is an example diagram of a linear analysis graph provided in an embodiment of the present invention;

[0025] Figure 2c This is an example diagram of the clustering results provided in an embodiment of the present invention;

[0026] Figure 3a This is another flowchart illustrating the service value identification method for transportation services provided in this embodiment of the invention;

[0027] Figure 3b This is an example diagram illustrating whether the reported service value of a target transportation service is normal, provided by an embodiment of the present invention.

[0028] Figure 4 This is a schematic diagram of a service value identification device for transportation services provided in an embodiment of the present invention;

[0029] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0030] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0032] Figure 1a This is a flowchart illustrating a service value identification method for transportation services provided in this embodiment of the invention. This method can be used in scenarios involving the review of submitted pricing for pickup or delivery services. The service value identification method can be executed by a service value identification device provided in this embodiment, which can be implemented using software and / or hardware. In one specific embodiment, the device can be integrated into an electronic device, such as a computer or server. The following embodiment uses the integration of this device into an electronic device as an example for illustration. (See also...) Figure 1a This embodiment will be described in detail from the following steps.

[0033] Step 110: Obtain the reported service value of the target transportation service and determine the pricing rules for the target transportation service based on the reported service value.

[0034] Target transportation services can be any service involved in the transportation of goods, including pickup and delivery services. (See also...) Figure 1b , Figure 1b This diagram illustrates an application scenario of the service value identification method for transportation services provided in this embodiment of the invention. In this scenario, a courier can pick up goods from the sender and transport them to the drop-off station, thus providing a pickup service; or transport goods from the drop-off station to the recipient, thus providing a delivery service. The services mentioned in this embodiment refer to paid services, requiring pricing for the services provided to users. For example, when providing a pickup service to a sender, a price needs to be set for the pickup service to collect a service fee from the sender.

[0035] For logistics management platforms, personnel providing transportation services (such as couriers) can be categorized based on different cooperation models. For example, they can be divided into dedicated delivery personnel and crowdsourced personnel. To enhance service flexibility and competitiveness, crowdsourced personnel can be allowed to set their own prices, meaning they can set their own prices for pickup and delivery services in various regions. However, independent pricing can be unreasonable. If the price is too low, crowdsourced personnel may not receive adequate compensation, leading to staff turnover; if the price is too high, customers may switch to lower-priced services offered by competitors, resulting in customer churn. Therefore, it is necessary to identify and review independent pricing to ensure it remains within a reasonable range.

[0036] In practical applications, the target transportation service can be a transportation service provided by transportation personnel with independent pricing authority. The reported service value of the target transportation service can refer to the intended or planned price for providing the target transportation service. The reported service value can be calculated by the logistics management platform based on the service-related data included in the target transportation service. The service-related data can include attribute data of the goods to be transported and / or service address data. The attribute data of the goods to be transported can include, for example, the type, weight, and volume of the goods. The service address data can include, for example, the pickup address, the pickup difficulty at the pickup location, the delivery address, and the delivery difficulty at the delivery location. The logistics management platform can report the target transportation service and its reported service value to the electronic device. The electronic device can then calculate the pricing rules for the target transportation service based on the target transportation service and its reported service value. The pricing rules can refer to the pricing method determined based on various pricing factors. Pricing factors refer to the factors considered when calculating the price. Pricing factors can include, for example, at least one of the following: the type, weight, and volume of the goods to be transported; the pickup address; the pickup difficulty at the pickup location; the delivery address; and the delivery difficulty at the delivery location.

[0037] For example, when a delivery person performs a delivery service, they can report the service to a logistics management platform through the delivery terminal. The logistics management platform determines the service price based on data such as the weight and volume of the delivered items, the delivery location, and the difficulty of delivery to the location. The platform then reports the determined service price and delivery service details to an electronic device, which determines the pricing rules for the delivery service based on the reported data. For instance, if the pricing factor is weight and the unit of measurement is kilograms, the pricing rules could specify the charge per kilogram.

[0038] Step 120: Determine the service area corresponding to the target transportation service to obtain the target service area.

[0039] The service area corresponding to the target transportation service can be determined based on the service addresses included in the target transportation service. When the target transportation service is a pickup service, the service address can be the pickup address; when the target transportation service is a delivery service, the service address can be the delivery address. For ease of processing, in this embodiment, the service address can be represented in a standardized or uniform way. For example, for a service address, the corresponding third-level address (e.g., province, city, county) can be determined, and the service address can be represented using the corresponding third-level address to obtain the target service area (e.g., county-level area).

[0040] Step 130: Determine the historical transportation services of the target service area and obtain the actual service value of the historical transportation services.

[0041] Historical transportation services within the target service area can be all or part of past services of the same type as the target transportation service. For example, when the target transportation service is a delivery service to a county, the historical transportation services within the target service area could be all past delivery services in that county, or delivery services within that county over a specific period. The actual service value of historical transportation services within the target service area can be obtained by querying the logistics management platform or a designated database. The actual service value of historical transportation services can refer to the actual charges for those services, or the value of services identified as normal, effective, or executed. To improve the accuracy of service value identification, multiple historical transportation services can be considered, each with a corresponding actual service value.

[0042] Step 140: Predict the service value of historical transportation services according to the pricing rules to obtain the estimated service value of historical transportation services.

[0043] That is, the service value of historical transportation services is recalculated according to the pricing rules of the target transportation service. After recalculation, each historical transportation service has two service values: actual service value and expected service value. The expected service value is the reference data in the service value identification process.

[0044] Step 150: Identify whether the reported service value of the target transportation service is normal based on the actual service value and the expected service value of historical transportation services.

[0045] Specifically, the difference between the actual service value and the expected service value of historical transportation services can be determined, and based on this difference, it can be determined whether the reported service value of the target transportation service is normal. For example, if the difference is large (e.g., exceeding a set value), the reported service value of the target transportation service can be determined to be abnormal; if the difference is small (e.g., not exceeding a set value), the reported service value of the target transportation service can be determined to be normal.

[0046] This embodiment analyzes the reported service value of a target transportation service within a target service area to obtain the pricing rules for that service. These rules are then applied to historical transportation services within the target service area to predict the expected service value (i.e., the expected price) of those historical services, calculated according to the target pricing rules. The reported service value of the target transportation service is then compared to its actual value (i.e., the actual price) to determine if the reported service value is normal. By comparing the service values ​​of historical transportation services before and after the introduction of the target pricing rules, the system achieves automatic identification and review of the reported service value from a global perspective, improving the objectivity, accuracy, and efficiency of the review process.

[0047] In a specific embodiment, clustering can be used to process the actual service value of historical transportation services to find cluster centers. These cluster centers can then be used to help identify whether the reported service value of a target transportation service is normal. The process of clustering the actual service value of historical transportation services to find cluster centers is described below. Figure 2a As shown, Figure 2a This is a schematic diagram of a process for clustering historical transportation services based on their actual service value to find cluster centers, as provided in an embodiment of the present invention. The process may specifically include:

[0048] Step 210: Obtain the actual service value of historical transportation services for each preset service area.

[0049] The preset service area can include all areas involved in the goods transportation service. Preset service areas can be represented using a standardized or uniform representation. For example, a preset service area can be represented by a corresponding three-level address. Each preset service area can be a county-level area, allowing the acquisition of historical transportation services and their actual service value for each county-level area. Each historical transportation service can include attribute data of the goods to be transported and / or service address data. Attribute data of the goods to be transported can include, for example, item type, weight, and volume. Service address data can include, for example, the pickup address, pickup difficulty at the pickup location, delivery address, and delivery difficulty at the delivery location.

[0050] Specifically, the actual service value of all historical transportation services can be obtained first, and then the actual service value of historical transportation services can be grouped according to the service area. The actual service value of historical transportation services belonging to the same service area can be grouped together to obtain the actual service value of historical transportation services in each preset service area.

[0051] Step 220: Determine the actual location data of the corresponding preset service area based on the actual service value of the historical transportation services of each preset service area.

[0052] Specifically, the actual value deviation of each pricing factor can be removed from the actual service value of historical transportation services in each preset service area to obtain the residual actual value of each pricing factor. Based on the residual actual value of each pricing factor in each preset service area, the actual pricing coefficient for each pricing factor in each preset service area can be calculated. Finally, the actual location data for the corresponding preset service area can be determined based on the actual pricing coefficients for each pricing factor in each preset service area. The actual value deviation of a pricing factor can be understood as the deviation of the pricing value under that pricing factor from the normal or preset value. The actual pricing coefficient for a pricing factor can be understood as the actual pricing weight of that pricing factor, the actual unit price corresponding to that pricing factor, etc.

[0053] For example, the target transportation service is a delivery service, and the pricing factors include weight and delivery difficulty at the delivery location. For any preset service area R1, the actual location data of area R1 can be determined according to the following method.

[0054] (1) The weight (i.e., the weight of the goods) and the actual service value corresponding to each historical transportation service in region R1 can be obtained, and the initial average weight coefficient can be determined based on the weight and actual service value corresponding to each historical transportation service.

[0055]

[0056] Where n represents the number of historical transportation services in region R1, and n is an integer greater than 2. The actual service value n represents the actual service value corresponding to the nth historical transportation service, and the weight n represents the weight corresponding to the nth historical transportation service.

[0057] Based on the average weight coefficient Construct a linear function, and then build a linear analysis plot based on that function. The linear analysis plot includes a weighted average line. For example, a linear analysis plot could be like this: Figure 2b As shown, the horizontal axis represents weight, and the vertical axis represents the actual service value.

[0058] The weight and actual service value corresponding to each historical transportation service can be expressed as: Figure 2b Given a point, calculate the straight-line distance between each point and the weight average line, and find the point with the largest straight-line distance to the weight average line. This point is the maximum distance point, and the straight-line distance between it and the weight average line is taken as the maximum distance d. max The straight-line distance between each point and the average weight line is compared with the maximum distance d. max The ratio is taken as the deviation fraction of the corresponding point. Assume the straight-line distance between the i-th point and the weight average line is denoted by d. iIf we express that the deviation score of the i-th point is... Points where the deviation score exceeds a preset score are designated as deviation points (the actual service value corresponding to each deviation point is the actual value deviation). After removing these deviation points, the average weight coefficient is recalculated based on the remaining points to obtain the final average weight coefficient 'a' (i.e., the actual pricing coefficient corresponding to the weight). The preset score is a limit score set for the deviation score, and it can be set according to actual needs, such as 90%, 95%, etc.

[0059] (2) The delivery difficulty and actual service value corresponding to each historical transportation service in region R1 can be obtained, and the initial average delivery difficulty coefficient can be determined based on the delivery difficulty and actual service value corresponding to each historical transportation service.

[0060]

[0061] Here, delivery difficulty n represents the delivery difficulty corresponding to the nth historical transportation service. The delivery difficulty can be preset or determined based on the delivery address.

[0062] Based on average delivery difficulty coefficient Construct a linear function, and then construct a linear analysis graph based on the linear function. The linear analysis graph includes a difficulty average line. The horizontal axis of the linear analysis graph can represent the delivery difficulty, and the vertical axis can represent the actual service value.

[0063] The delivery difficulty and actual service value corresponding to each historical transportation service can be represented as a point in a linear analysis graph. The straight-line distance between each point and the average difficulty line is calculated, and the point with the largest straight-line distance from the average difficulty line is found to obtain the maximum distance point. The straight-line distance between the maximum distance point and the average difficulty line is taken as the maximum distance. The ratio of the straight-line distance between each point and the average difficulty line to the maximum distance is taken as the deviation score of the corresponding point. Points with deviation scores exceeding the preset score are taken as deviation points (the actual service value corresponding to the deviation point is the actual value deviation value). After removing the deviation points, the average delivery difficulty coefficient is recalculated based on the remaining points to obtain the final average delivery difficulty coefficient b (i.e., the actual pricing coefficient corresponding to the delivery difficulty).

[0064] (3) Determine (a,b) as the actual point data of region R1.

[0065] That is, after determining the average weight coefficient and average delivery difficulty coefficient based on the actual service value of historical transportation services in the preset service area, the coordinate data formed by the average weight coefficient and average delivery difficulty coefficient is used as the actual location data of the corresponding preset service area.

[0066] Similarly, the actual location data for each of the other preset service areas can be calculated.

[0067] It should be noted that the above method for calculating the actual location data for each preset service area is only an example. In actual applications, deviation points may not be excluded, or other methods may be used to exclude deviation points or determine the coefficients of each pricing factor. For example, points whose straight-line distance from the average line exceeds a preset distance can be directly excluded as deviation points, and the median of the ratio of actual service value to weight can be determined as the actual pricing coefficient corresponding to the weight. In addition, other factors or more factors can be taken as pricing factors. For example, volume can also be taken as a pricing factor. When the pricing factors include weight, delivery difficulty, and volume, each location data will consist of three numbers, that is, the location data can be a three-dimensional coordinate data.

[0068] By eliminating the actual value deviations corresponding to each pricing factor and calculating the actual pricing coefficients corresponding to each pricing factor in each preset service area based on the actual value residual value of each pricing factor in each preset service area, the actual location data of each preset service area can be determined. This can avoid the interference of deviations on normal data and improve the accuracy of the determined actual location data.

[0069] Step 230: Determine the number of center points.

[0070] The number of centroids is the number of cluster centers. The number of centroids can be determined by pre-setting the number of cluster centers or by inputting the number of cluster centers in real time. The number of centroids can be pre-set or input in real time according to actual situation or needs.

[0071] For example, cities can be categorized based on factors such as size, population, and economic level, with the number of categorizations determining the number of centroids. For instance, in a practical application, if cities are divided into three categorizations based on these factors, the number of centroids would be 3. Determining the number of centroids according to city categorization makes the clustering results more interpretable, resulting in more meaningful and discriminative clustering outcomes. For example, if cities are divided into small, medium, and large categories, the clustering results can intuitively reflect the differences and similarities between cities of these three categories.

[0072] Step 240: Based on the number of center points and the actual location data of each preset service area, cluster the preset service areas to obtain the number of cluster centers.

[0073] Specifically, clustering can be performed using the following method:

[0074] (1) Randomly select a number of cluster centers from each preset service area to obtain the current cluster center;

[0075] (2) Determine the distance between each preset service area and each current cluster center based on the actual location data of each preset service area and the actual location data of each current cluster center. Find the preset service area with the smallest distance to each current cluster center. That is, a cluster center will find a preset service area with the smallest distance to it. Sum the distances between each current cluster center and the preset service area with the smallest distance to it to obtain the minimum clustering distance under the current clustering scheme.

[0076] (3) Repeat steps (1) and (2) above until the clustering cutoff condition is met (e.g., the number of clusterings reaches the preset number, the minimum clustering distance is less than the preset value, etc.). The cluster center corresponding to the smallest minimum clustering distance is taken as the final cluster center, and the number of cluster centers is output.

[0077] For example, with 3 cluster centers, after multiple iterations, the clustering result can be as follows: Figure 2c As shown, the final output can be cluster center 1, cluster center 2 and cluster center 3.

[0078] After obtaining the number of cluster centers, all preset service areas can be grouped based on the actual location data of the preset service areas and the actual location data of each cluster center. For example, the cluster center closest to each preset service area can be found from among the cluster centers, and each preset service area can be assigned to the group containing its closest cluster center.

[0079] In this embodiment, by mapping the actual service value of historical transportation services in each preset service area to specific locations, and clustering each preset service area based on the mapped data, the cluster center of each category is identified. This provides auxiliary data for subsequent identification of whether the reported service value of the target transportation service is normal. Moreover, the clustering process can be implemented in advance and directly retrieved when needed, thus improving the efficiency of service value identification.

[0080] The following examples, using the cluster centers found in the above embodiments, further illustrate the service value identification method for transportation services provided by this invention. Figure 3a As shown, the method in this embodiment may include:

[0081] Step 301: Obtain the reported service value of the target transportation service, and determine the pricing rules for the target transportation service based on the reported service value of the target transportation service.

[0082] The target transportation service may include attribute data of the items to be transported and / or service address data. Attribute data of the items to be transported may include, for example, item type, weight, and volume. Service address data may include, for example, the pickup address, pickup difficulty at the pickup location, delivery address, and delivery difficulty at the delivery location. The attribute data of the items to be transported, service address data, and the reported service value included in the target transportation service can be analyzed to determine the pricing rules for the target transportation service.

[0083] Step 302: Determine the service area corresponding to the target transportation service to obtain the target service area.

[0084] Step 303: Determine the historical transportation services of the target service area and obtain the actual service value of the historical transportation services.

[0085] There are multiple historical transportation services in the target service area, and each historical transportation service has a corresponding actual service value. The actual service value of a historical transportation service can be the service value that has been identified as normal, effective, or executed.

[0086] Step 304: Predict the service value of historical transportation services according to the pricing rules of the target transportation service to obtain the estimated service value of historical transportation services.

[0087] The pricing rules for the target transportation service are then substituted into the historical transportation services to obtain the estimated service value for each historical transportation service.

[0088] Step 305: Calculate the actual pricing coefficients corresponding to each pricing factor based on the actual service value of historical transportation services, and determine the actual location data of the target service area based on the actual pricing coefficients corresponding to each pricing factor.

[0089] The actual pricing coefficient corresponding to each pricing factor can be understood as the actual pricing weight corresponding to each pricing factor, or the actual unit price corresponding to each pricing factor, etc.

[0090] Specifically, for any pricing factor, the actual value deviation corresponding to that pricing factor can be removed from the actual service value of historical transportation services in the target service area to obtain the actual value residual value corresponding to that pricing factor; similarly, the actual value residual value corresponding to each pricing factor can be obtained; the actual pricing coefficient corresponding to each pricing factor is calculated based on the actual value residual value corresponding to each pricing factor; and the actual location data of the target service area is determined based on the actual pricing coefficient corresponding to each pricing factor. For example, the actual location data of the target service area can be represented by coordinate data or array data. The actual pricing coefficient corresponding to each pricing factor can be used as one dimension of the coordinate data, or the actual pricing coefficient corresponding to each pricing factor can be used as a component of the array data. The target service area can be a service area within a preset service area. Therefore, the process of determining the actual location data of the target service area can refer to the process of determining the actual location data of the preset service area in the above embodiment, and will not be repeated here.

[0091] In practical applications, since the target service area is one of the preset service areas, the actual location data of the target service area has already been determined when searching for the cluster center. Here, the actual location data of the predetermined target service area can be directly obtained to improve processing efficiency.

[0092] Step 306: Calculate the expected pricing coefficients for each pricing factor based on the expected service value of historical transportation services, and determine the expected location data of the target service area based on the expected pricing coefficients for each pricing factor.

[0093] The expected pricing coefficients corresponding to each pricing factor can be understood as the expected pricing weights or expected unit prices corresponding to each pricing factor obtained after introducing the pricing rules of the target transportation service into the historical transportation services of the target service area.

[0094] Specifically, for any pricing factor, the expected value deviation corresponding to that pricing factor can be removed from the expected service value of historical transportation services in the target service area to obtain the expected residual value of that pricing factor. Similarly, the expected residual value corresponding to each pricing factor can be obtained. The expected pricing coefficient for each pricing factor is calculated based on its expected residual value. The expected location data for the target service area is determined based on the expected pricing coefficient for each pricing factor. For example, the expected location data for the target service area can be represented using coordinate data or array data. The expected pricing coefficient for each pricing factor can be used as one dimension of the coordinate data, or it can be used as a component of the array data.

[0095] Step 307: Determine the distance between the target service area and the corresponding cluster center based on the actual location data of the target service area and the actual location data of each cluster center.

[0096] Step 308: Determine the cluster center to which the target service area belongs from each cluster center based on the distance between the target service area and the corresponding cluster center.

[0097] The cluster center with the smallest distance to the target service area can be found from all the cluster centers, and this cluster center is taken as the cluster center to which the target service area belongs. For example, after the previous clustering, three cluster centers are obtained. Assuming that the target service area is the smallest in distance from cluster center 1, then cluster center 1 can be determined as the cluster center to which the target service area belongs.

[0098] Step 309: Obtain the actual location data of the cluster centers to which the target service area belongs, and obtain the parameter location data.

[0099] The actual location data of the cluster centers to which the target service area belongs is used as the parameter location data of the target service area.

[0100] Step 310: Determine the distance between the target service area and the cluster center based on the parameter point data and the actual point data of the target service area to obtain the first distance; and determine the distance between the target service area and the cluster center based on the parameter point data and the expected point data of the target service area to obtain the second distance.

[0101] Step 311: Determine the offset corresponding to the reported service value of the target transportation service based on the first distance and the second distance.

[0102] The offset corresponding to the reported service value can be understood as the deviation in service value caused by introducing the pricing rules of the target transportation service into the historical transportation services of the target service area. For example, the ratio of the first distance to the second distance can be used as the offset corresponding to the reported service value, or the ratio of the first distance to the second distance can be subtracted by 1 to obtain the offset corresponding to the reported service value, or the difference between the first distance and the second distance can be used as the offset corresponding to the reported service value. The specific calculation method can be set according to actual needs, and no specific limitation is made here.

[0103] Step 312: Determine whether the offset is within the preset offset range. If it is within the preset offset range, proceed to step 315. If it is not within the preset offset range, proceed to step 313.

[0104] If the offset is within the preset offset range, it indicates that the service value deviation caused by introducing the pricing rules of the target transportation service into the historical transportation services of the target service area is small and within an acceptable range, and the reported service value of the target transportation service is normal. If the offset is not within the preset offset range, it indicates that the service value deviation caused by introducing the pricing rules of the target transportation service into the historical transportation services of the target service area is large and exceeds the acceptable range, and the reported service value of the target transportation service is abnormal. In this case, the reported service value of the target transportation service may be too high or too low.

[0105] Step 313: Determine that the reported service value of the target transportation service is abnormal.

[0106] Step 314: Issue an anomaly alert for value reporting.

[0107] Specifically, when the electronic device is a computer with display function, it can display a value submission anomaly prompt; when the electronic device is a computer or server without display function, it can send a value submission anomaly prompt to the terminal corresponding to the quotation approver (i.e., the quotation approval terminal). The value submission anomaly prompt can include the target transportation service, the submitted service value of the target transportation service, and the identification result that the submitted service value is abnormal (i.e., abnormal).

[0108] In practical applications, the degree of anomaly can also be identified, and different prompts can be made based on the degree of anomaly. For example, the anomaly range can be determined based on a preset offset range, and the anomaly range can be divided into multiple intervals, with different intervals corresponding to different degrees of anomaly. Different prompts can be set for different degrees of anomaly. For example, the anomaly range can be divided into a mild anomaly interval, a moderate anomaly interval, and a severe anomaly interval. When the offset corresponding to the reported service value falls within the mild anomaly interval, the approver can be prompted to pay attention to the reported service value; when the offset corresponding to the reported service value falls within the moderate anomaly interval, the approver can be prompted to pay close attention to the reported service value; when the offset corresponding to the reported service value falls within the severe anomaly interval, the approver can be prompted not to approve the reported service value.

[0109] Step 315: Determine that the reported service value of the target transportation service is normal.

[0110] If the declared service value of the target transportation service is normal, then the declared service value of the target transportation service has been approved, and pricing and charges can be made according to the declared service value. After the declared service value of the target transportation service is executed, the target transportation service will become a historical transportation service, and the declared service value will become the actual service value.

[0111] The following example uses delivery service X as the target transportation service and region R1 as the target service area. Assume that there are 100 historical delivery services in region R1. The pricing factors include weight and delivery difficulty of the delivery location. Then, the pricing rules for delivery service X (such as pricing per unit weight and pricing per unit delivery difficulty) can be determined based on delivery service X and its proposed pricing. Based on the pricing rules for delivery service X, the expected pricing of these 100 historical delivery services can be predicted. These 100 historical delivery services have already been generated, so they also have actual pricing. That is, each of these 100 historical delivery services has an actual pricing and an expected pricing.

[0112] Next, based on the actual pricing of these 100 historical delivery services, determine the actual pricing coefficient 'a' corresponding to weight and the actual pricing coefficient 'b' corresponding to delivery difficulty, and determine (a,b) as the actual location data for region R1; based on the projected pricing of these 100 historical delivery services, determine the projected pricing coefficient 'A' corresponding to weight and the projected pricing coefficient 'B' corresponding to delivery difficulty, and determine (A,B) as the projected location data for region R1.

[0113] Assuming that the cluster center of region R1 is cluster center 1, meaning that the location data of cluster center 1 serves as the reference location data for region R1, an example of determining whether the reported service value of the target transportation service is normal can be as follows: Figure 3b As shown, the first distance can be determined based on the actual location data (a, b) and the actual location data of cluster center 1, and the second distance can be determined based on the expected location data (A, B) and the actual location data of cluster center 1. The offset corresponding to the proposed price of delivery service X can be determined based on the first and second distances. When the offset is not within the preset offset range, the proposed price of delivery service X is determined to be abnormal. After determining that the proposed price of delivery service X is abnormal, a value proposal anomaly prompt can be issued. For example, the approver can be prompted to pay attention to the proposed price of delivery service X, or the approver can be prompted not to approve the proposed price of delivery service X.

[0114] This embodiment analyzes the reported service value of a target transportation service within a target service area to obtain the pricing rules for that service. These rules are then applied to historical transportation services within the target service area to predict the expected service value (i.e., the expected price) of those historical services, calculated according to the target pricing rules. The reported service value of the target transportation service is then compared to its actual value (i.e., the actual price) to determine if the reported service value is normal. By comparing the service values ​​of historical transportation services before and after the introduction of the target pricing rules, the system automatically identifies and reviews the reported service value from a global perspective, improving objectivity, accuracy, and efficiency. Furthermore, when an abnormal reported service value is detected, an anomaly alert is issued, allowing relevant personnel to promptly identify and correct or reject abnormal pricing reports.

[0115] Figure 4 This is a schematic diagram of a service value identification device for transportation services provided in an embodiment of the present invention. This device can be used to execute the service value identification method for transportation services provided in an embodiment of the present invention, such as... Figure 4 As shown, the device may specifically include:

[0116] The pricing rule determination module 401 is used to obtain the reported service value of the target transportation service and determine the pricing rule of the target transportation service based on the reported service value of the target transportation service.

[0117] Service area determination module 402 is used to determine the service area corresponding to the target transportation service and obtain the target service area;

[0118] The acquisition module 403 is used to determine the historical transportation services of the target service area and obtain the actual service value of the historical transportation services;

[0119] The value prediction module 404 is used to predict the service value of the historical transportation service according to the pricing rules, and to obtain the expected service value of the historical transportation service.

[0120] The identification module 405 is used to identify whether the reported service value of the target transportation service is normal based on the actual service value and the expected service value of the historical transportation service.

[0121] In one embodiment, the identification module 405 is specifically used for:

[0122] Calculate the actual pricing coefficient corresponding to each pricing factor based on the actual service value of the historical transportation services.

[0123] Calculate the expected pricing coefficients corresponding to each pricing factor based on the expected service value of the historical transportation services;

[0124] The report value of the target transportation service is determined based on the actual pricing coefficient and the expected pricing coefficient corresponding to each pricing factor.

[0125] In one embodiment, the identification module 405 calculates the actual pricing coefficient corresponding to each pricing factor based on the actual service value of the historical transportation service, including: removing the actual value deviation value corresponding to each pricing factor from the actual service value of the historical transportation service to obtain the actual value residual value corresponding to each pricing factor, and calculating the actual pricing coefficient corresponding to each pricing factor based on the actual value residual value corresponding to each pricing factor.

[0126] The identification module 405 calculates the expected pricing coefficients corresponding to each pricing factor based on the expected service value of the historical transportation service, including: removing the expected value deviation value corresponding to each pricing factor from the expected service value of the historical transportation service to obtain the expected value residual value corresponding to each pricing factor, and calculating the expected pricing coefficients corresponding to each pricing factor based on the expected value residual value corresponding to each pricing factor.

[0127] In one embodiment, the identification module 405 identifies whether the reported service value of the target transportation service is normal based on the actual pricing coefficients corresponding to each pricing factor and the expected pricing coefficients corresponding to each pricing factor, including:

[0128] Obtain the location data of the cluster centers to which the target service area belongs, and obtain the parameter location data;

[0129] The actual location data of the target service area is determined based on the actual pricing coefficients corresponding to each pricing factor, and the expected location data of the target service area is determined based on the expected pricing coefficients corresponding to each pricing factor.

[0130] The distance between the target service area and the cluster center is determined based on the actual location data and the parameter location data to obtain a first distance; and the distance between the target service area and the cluster center is determined based on the expected location data and the parameter location data to obtain a second distance;

[0131] The report value of the target transportation service is determined based on the first distance and the second distance.

[0132] In one embodiment, the device further includes a clustering module, which is used for:

[0133] Obtain the actual service value of historical transportation services in each preset service area;

[0134] The actual location data of the corresponding preset service area are determined based on the actual service value of the historical transportation services of each preset service area.

[0135] Based on the actual location data of each preset service area, the preset service areas are clustered to obtain the cluster center to which the target service area belongs.

[0136] In one embodiment, the clustering module performs clustering processing on the preset service areas based on the actual location data of each preset service area to obtain the cluster center to which the target service area belongs, including:

[0137] Determine the number of center points;

[0138] Based on the number of center points and the actual location data of each preset service area, the preset service areas are clustered to obtain the number of cluster centers.

[0139] Based on the actual location data of the target service area and the actual location data of each cluster center, the distance between the target service area and the corresponding cluster center is determined;

[0140] The cluster center to which the target service area belongs is determined from the various cluster centers based on the distance between the target service area and the corresponding cluster center.

[0141] In one embodiment, the identification module 405 identifies whether the reported service value of the target transportation service is normal based on the first distance and the second distance, including:

[0142] The offset corresponding to the reported service value of the target transportation service is determined based on the first distance and the second distance;

[0143] When the offset is within the preset offset range, it is determined that the reported service value of the target transportation service is normal.

[0144] When the offset is not within the preset offset range, it is determined that the reported service value of the target transportation service is abnormal.

[0145] In one embodiment, the device further includes:

[0146] The prompting module is used to provide an abnormal value reporting prompt if the reported service value of the target transportation service is abnormal.

[0147] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is merely an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the functional modules described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0148] The apparatus of this invention can analyze the reported service value of a target transportation service in a target service area to obtain the pricing rule for the target transportation service. This pricing rule is then applied to historical transportation services in the target service area to predict the expected service value (i.e., the expected price) of the historical transportation services calculated according to the target transportation service's pricing rule. Based on the expected service value and the actual service value (i.e., the actual price) of the historical transportation services, the apparatus identifies whether the reported service value of the target transportation service is normal. By comparing the service values ​​of historical transportation services before and after the introduction of the target transportation service's pricing rule, the apparatus achieves automatic identification and review of the normality of the reported service value of the target transportation service from a global perspective, improving the objectivity and accuracy of the review and increasing review efficiency.

[0149] This invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the service value identification method for transportation services provided in any of the above embodiments.

[0150] This invention also provides a computer-readable medium having a computer program stored thereon, which, when executed by a processor, implements the service value identification method for transportation services provided in any of the above embodiments.

[0151] The following is for reference. Figure 5 It shows a schematic diagram of the structure of a computer system 500 suitable for implementing an electronic device according to embodiments of the present invention. Figure 5 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the invention.

[0152] like Figure 5As shown, the computer system 500 includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 502 or programs loaded from storage section 508 into random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of the computer system 500. The CPU 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0153] The following components are connected to I / O interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN card, modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to I / O interface 505 as needed. A removable medium 511, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 510 as needed so that computer programs read from it can be installed into storage section 508 as needed.

[0154] In particular, according to the embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 509, and / or installed from removable medium 511. When the computer program is executed by central processing unit (CPU) 501, it performs the functions defined above in the system of this invention.

[0155] It should be noted that the computer-readable medium shown in this invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0156] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0157] The modules and / or units described in this invention can be implemented in software or hardware. The described modules and / or units can also be housed in a processor; for example, a processor can be described as including a pricing rule determination module, a service area determination module, an acquisition module, a value prediction module, and an identification module. The names of these modules do not necessarily limit the module itself.

[0158] In another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiments; or it may exist independently and not assembled into the device. The computer-readable medium carries one or more programs, which, when executed by the device, cause the device to include:

[0159] The process involves: obtaining the reported service value of a target transportation service; determining the pricing rules for the target transportation service based on the reported service value; determining the service area corresponding to the target transportation service to obtain the target service area; determining the historical transportation services of the target service area and obtaining the actual service value of the historical transportation services; predicting the service value of the historical transportation services according to the pricing rules to obtain the expected service value of the historical transportation services; and identifying whether the reported service value of the target transportation service is normal based on the actual service value and expected service value of the historical transportation services.

[0160] According to the technical solution of this invention, the reported service value of a target transportation service in a target service area can be analyzed to obtain the pricing rules for the target transportation service. These pricing rules are then applied to historical transportation services in the target service area to predict the expected service value (i.e., the expected price) of historical transportation services calculated according to the pricing rules. The reported service value of the target transportation service is then identified based on the expected and actual service values ​​(i.e., the actual price) of the historical transportation services. By comparing the service values ​​of historical transportation services before and after the introduction of the pricing rules for the target transportation service, automatic identification and review of the reported service value of the target transportation service is achieved from a global perspective, improving the objectivity and accuracy of the review and increasing review efficiency.

[0161] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0162] It should be noted that the collection, gathering, updating, analysis, processing, use, transmission, and storage of user personal information involved in this disclosed technical solution all comply with relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. Necessary measures are taken to prevent unauthorized access to user personal information data and to safeguard user personal information security, network security, and national security.

[0163] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can occur depending on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for identifying the service value of a transportation service, characterized in that, include: Obtain the reported service value of the target transportation service, and determine the pricing rules for the target transportation service based on the reported service value of the target transportation service; Determine the service area corresponding to the target transportation service to obtain the target service area; Determine the historical transportation services of the target service area and obtain the actual service value of the historical transportation services; The estimated service value of the historical transportation service is obtained by predicting the service value of the historical transportation service according to the pricing rules. The reported service value of the target transportation service is determined based on the actual and expected service values ​​of the historical transportation services.

2. The service value identification method according to claim 1, characterized in that, The step of identifying whether the reported service value of the target transportation service is normal based on the actual service value and the estimated service value of the historical transportation services includes: Calculate the actual pricing coefficient corresponding to each pricing factor based on the actual service value of the historical transportation services. Calculate the expected pricing coefficients corresponding to each pricing factor based on the expected service value of the historical transportation services; The report value of the target transportation service is determined based on the actual pricing coefficient and the expected pricing coefficient corresponding to each pricing factor.

3. The service value identification method according to claim 2, characterized in that, The step of calculating the actual pricing coefficient corresponding to each pricing factor based on the actual service value of the historical transportation service includes: removing the actual value deviation value corresponding to each pricing factor from the actual service value of the historical transportation service to obtain the actual value residual value corresponding to each pricing factor, and calculating the actual pricing coefficient corresponding to each pricing factor based on the actual value residual value corresponding to each pricing factor. The step of calculating the expected pricing coefficients corresponding to each pricing factor based on the expected service value of the historical transportation service includes: removing the expected value deviation value corresponding to each pricing factor from the expected service value of the historical transportation service to obtain the expected value residual value corresponding to each pricing factor, and calculating the expected pricing coefficients corresponding to each pricing factor based on the expected value residual value corresponding to each pricing factor.

4. The service value identification method according to claim 2, characterized in that, Identifying whether the reported service value of the target transportation service is normal based on the actual pricing coefficient and the expected pricing coefficient corresponding to each pricing factor includes: Obtain the actual location data of the cluster centers to which the target service area belongs, and obtain the parameter location data; The actual location data of the target service area is determined based on the actual pricing coefficients corresponding to each pricing factor, and the expected location data of the target service area is determined based on the expected pricing coefficients corresponding to each pricing factor. The distance between the target service area and the cluster center is determined based on the actual location data and the parameter location data to obtain a first distance; and the distance between the target service area and the cluster center is determined based on the expected location data and the parameter location data to obtain a second distance; The report value of the target transportation service is determined based on the first distance and the second distance.

5. The service value identification method according to claim 4, characterized in that, The cluster center to which the target service area belongs is determined in the following way: Obtain the actual service value of historical transportation services in each preset service area; The actual location data of the corresponding preset service area are determined based on the actual service value of the historical transportation services of each preset service area. Based on the actual location data of each preset service area, the preset service areas are clustered to obtain the cluster center to which the target service area belongs.

6. The service value identification method according to claim 5, characterized in that, The step of clustering the preset service areas based on their actual location data to obtain the cluster center of the target service area includes: Determine the number of center points; Based on the number of center points and the actual location data of each preset service area, the preset service areas are clustered to obtain the number of cluster centers. Based on the actual location data of the target service area and the actual location data of each cluster center, the distance between the target service area and the corresponding cluster center is determined; The cluster center to which the target service area belongs is determined from the various cluster centers based on the distance between the target service area and the corresponding cluster center.

7. The service value identification method according to claim 4, characterized in that, The step of identifying whether the reported service value of the target transportation service is normal based on the first distance and the second distance includes: The offset corresponding to the reported service value of the target transportation service is determined based on the first distance and the second distance; When the offset is within the preset offset range, it is determined that the reported service value of the target transportation service is normal. When the offset is not within the preset offset range, it is determined that the reported service value of the target transportation service is abnormal.

8. A service value identification device for transportation services, characterized in that, include: The pricing rule determination module is used to obtain the reported service value of the target transportation service and determine the pricing rule of the target transportation service based on the reported service value of the target transportation service. The service area determination module is used to determine the service area corresponding to the target transportation service, thereby obtaining the target service area; The acquisition module is used to determine the historical transportation services of the target service area and obtain the actual service value of the historical transportation services; The value prediction module is used to predict the service value of the historical transportation service according to the pricing rules, and to obtain the expected service value of the historical transportation service. The identification module is used to identify whether the reported service value of the target transportation service is normal based on the actual service value and the expected service value of the historical transportation service.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the service value identification method for transportation services as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the service value identification method for transportation services as described in any one of claims 1 to 7.

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