CN112700322B - Order sampling detection method, order sampling detection device, electronic equipment and storage medium - Google Patents
Order sampling detection method, order sampling detection device, electronic equipment and storage medium Download PDFInfo
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/03—Credit; Loans; Processing thereof
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- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
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Abstract
The embodiment of the invention discloses an order sampling detection method, an order sampling detection device, electronic equipment and a storage medium. The order sampling detection method comprises the following steps: acquiring an order sample group of a merchant to be detected; randomly extracting a preset number of sampling orders from the order sample group for detection; the preset quantity is determined by adopting a sampling detection model, and the sampling detection model is determined based on historical orders of merchants; and determining the authenticity of the order sample group according to the detection result of the sampling order. The embodiment of the invention reduces the detection cost of the sampling inspection of the order sample group and ensures the accuracy of the sampling inspection of the order sample group, thereby improving the accuracy of determining the loan in result of the to-be-inspected merchant according to the authenticity of the order sample group.
Description
Technical Field
The embodiment of the invention relates to the technical field of Internet, in particular to an order sampling detection method, an order sampling detection device, electronic equipment and a storage medium.
Background
In the supply chain financial lending business, the authenticity of the actual order of the lending merchant needs to be referenced to determine whether the lending is admitted or not and the amount of the deposit. Different merchants have different scales of order services, so the number of orders in a unit period is also different. In order to achieve the purpose of risk control, the authenticity of the order needs to be determined according to the order data of the merchant, counterfeits are removed, and the effective real order is brought into the reference range of lending.
If the authenticity detection is performed on all orders of the lending merchant, a huge workload is brought, so that a common method is to randomly extract a plurality of orders from all orders of the lending merchant for detection, and determine the authenticity of the whole order according to the detection results of the extracted orders. The number of orders randomly drawn determines the accuracy and efficiency of the overall spot check of the order.
However, because the order sizes of different borrowing merchants are different, if the same standard is adopted to randomly extract different borrowing merchants, the detection of merchant orders is inaccurate, and the accuracy of final borrowing admission decision is affected.
Disclosure of Invention
The embodiment of the invention provides an order sampling detection method, an order sampling detection device, electronic equipment and a storage medium, which can reduce the detection cost of sampling detection of an order sample group and ensure the accuracy of sampling detection of the order sample group.
In a first aspect, an embodiment of the present invention provides an order sampling detection method, including:
acquiring an order sample group of a merchant to be detected;
randomly extracting a preset number of sampling orders from the order sample group for detection; the preset quantity is determined by adopting a sampling detection model, and the sampling detection model is determined based on historical orders of merchants;
And determining the authenticity of the order sample group according to the detection result of the sampling order.
In a second aspect, an embodiment of the present invention further provides an order sampling detection apparatus, including:
the order acquisition module is used for acquiring an order sample group of the merchant to be detected;
the sampling order extraction module is used for randomly extracting a preset number of sampling orders from the order sample group for detection; the preset quantity is determined by adopting a sampling detection model, and the sampling detection model is determined based on historical orders of merchants;
and the order authenticity determining module is used for determining the authenticity of the order sample group according to the detection result of the sampling order.
In a third aspect, an embodiment of the present invention further provides an electronic device, including:
one or more processors;
storage means for storing one or more programs,
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement an order sample detection method as described in any of the embodiments of the present invention.
In a fourth aspect, embodiments of the present invention also provide a computer readable storage medium having stored thereon a computer program which when executed by a processor implements an order sampling detection method according to any of the embodiments of the present invention.
The embodiment of the invention is based on the acquired order sample group of the merchant to be detected; randomly extracting a preset number of sampling orders from an order sample group for detection; and determining the authenticity of the order sample group according to the detection result of the sampling order, wherein the preset quantity value of the sampling order is determined through a sampling detection model, and the sampling detection model is determined based on historical orders of merchants. Therefore, the detection cost of the sampling inspection of the order sample group is reduced, the accuracy of the sampling inspection of the order sample group is ensured, the accuracy of determining the authenticity of the whole order sample group according to the authenticity of the sampled order is ensured under the condition that the preset number of the sampling inspection is minimum, and the accuracy of determining the loan in result of the to-be-detected merchant according to the authenticity of the order sample group is further improved.
Drawings
FIG. 1 is a flow chart of an order sample detection method in accordance with a first embodiment of the present invention;
FIG. 2 is a flow chart of an order sample detection method in a second embodiment of the invention;
FIG. 3 is a schematic diagram of an order sampling detection system according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of an order sample detection device according to a third embodiment of the present invention;
Fig. 5 is a schematic structural diagram of an electronic device in a fourth embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present invention are shown in the drawings.
Example 1
Fig. 1 is a flowchart of an order sampling detection method according to a first embodiment of the present invention, where the embodiment is applicable to randomly extracting a part of sampled orders from an order sample group to detect, and determining a detection result of an overall order sample group according to the detection result of the sampled orders. The method may be performed by order sample detection means, which may be implemented in software and/or hardware, and may be configured in an electronic device, e.g. a background server or the like having communication and computing capabilities. As shown in fig. 1, the method specifically includes:
step 101, acquiring an order sample group of a merchant to be detected.
The merchant to be detected refers to a merchant submitting a loan application, and in order to judge the loan admittance of the merchant, the authenticity of the order sold by the merchant needs to be detected. The order sample group refers to order information that the merchant to be detected has completed on all sales platforms within a preset time range, wherein the preset time range is generally set to be recent, such as one month, three months or half year. The closer the preset time range is, the more the current business condition of the merchant can be reflected, and the authenticity and timeliness are achieved.
Specifically, sales order information of the merchant in all electronic commerce platforms is determined according to information of the merchant to be detected, and target order data in a preset time period is screened out according to conditions of the time period to be used as an order sample group. Exemplary, after obtaining the order sample group of the merchant to be detected on each platform, cleaning and collecting the order sample group are executed. For example, the important attention information of each order in the order sample group is extracted and stored according to a preset format. The important information includes addressee information, sender information, addressee address, sender address, logistics company name, logistics bill number, delivery time and the like. The cleaning source order data is illustratively carried out according to the requirement of the service platform for providing the logistics list verification, and the cleaned order data is used as an order sample group to be detected by the merchant.
102, randomly extracting a preset number of sampling orders from an order sample group for detection; the preset quantity is determined by adopting a sampling detection model, and the sampling detection model is determined based on historical orders of merchants.
Because the business conditions of all merchants are different, partial merchants truly conduct business, the orders of the merchants are all real orders, and partial merchants are used for improving the trust of users, making false sales data of the merchants, and manufacturing a large number of false orders, such as swiping the orders. If each order of the merchant to be detected is detected one by one, a great amount of detection manpower, material resources and time are consumed, and the problem of overlong waiting time and the like can occur for the merchant needing borrowing, so that the operation condition is influenced.
Thus, a sample detection model is built, a time range is set, the time is earlier than the time of the order to be detected, and the sample detection model is determined according to the historical order data conforming to the time. The method and the system can determine the sample preset quantity of sampling detection in the order sample group of the merchant to be detected according to the sampling detection model, so as to ensure that the detection result of the whole order sample group can be determined according to the detection result of the sample order of the preset quantity.
Specifically, determining the preset number of sampling orders required by the merchant to be detected according to a sampling detection model, randomly extracting the preset number of orders in an order sample group to be detected, and detecting. The preset number of the sampling orders is determined through the sampling detection model, so that the number of samples to be detected is greatly reduced, the manpower and time expenditure for detection is reduced, the processing speed and the working efficiency of the borrowing request of enterprises to merchants are improved, and the accuracy of the detection result of the order sample group is ensured according to the detection result of the sampling orders.
The sampling order is detected according to the authenticity of the logistics in the order. For merchants who make order data counterfeits, the technology of ordering is generally adopted, and the authenticity of logistics in order ordering is difficult to ensure. Specifically, the sampled order is detected to be true, the sampled order can be communicated with a third party logistics list verification service platform, information of the order to be verified is input to the logistics list verification service platform, the information comprises information of a receiver, information of a sender, an address of the receiver, the address of the sender, names of logistics companies, logistics list numbers, delivery time and arrival time and the like, inquiry and comparison are carried out, and if the order information is consistent with the information searched on the third party logistics list verification service platform, the order is considered to be a true order.
And step 103, determining the authenticity of the order sample group according to the detection result of the sampling order.
And after all the sampling orders are detected, determining the authenticity of the order sample group according to the detection result of the sampling orders. If the detection results of the sampled orders are all true, determining the order sample group as a true order sample group; if the detection result of any sampling order appears in the detection process of the sampling order is a false order, the detection of the subsequent sampling order is stopped immediately, and the order sample group can be determined to be a false order sample group.
In one possible embodiment, step 103 includes:
if the detection results of the sampled orders are all true, determining the order sample group as a true order sample group.
After the randomly extracted sampling orders are detected, statistics is carried out on detection results of the sampling orders, and if all detection results of the sampling orders are real orders, the order sample group is considered to be a real order sample group. This is determined by the principle of determining a predetermined number based on the sample detection model.
The method includes that a merchant to be detected who needs borrowing determines that the preset number of sampling orders is 100 according to a sampling detection model, detecting the 100 sampling orders one by one, and determining that the order sample group is a real order sample group if all the 100 sampling orders are detected to be real orders. If a false order occurs in the 100 sampled orders, the order sample group is directly determined to be a false order sample group.
In one possible embodiment, before step 102, further comprising:
dividing an order sample group according to the preset order quantity in a single batch to obtain at least two batch orders;
correspondingly, randomly extracting a preset number of sampling orders from the order sample group for detection, and determining the authenticity of the order sample group according to the detection result of the sampling orders, wherein the method comprises the following steps:
and randomly extracting a preset number of sampling orders from each batch of orders in sequence for detection, and determining the authenticity of each batch of orders according to the detection result of the sampling orders to obtain a real order sample group.
If a preset number of sampling orders are randomly extracted from all the order sample groups of the detection merchant, when detection errors occur, the whole order sample group needs to be detected again, and human resources are wasted. Therefore, in the embodiment of the present invention, the order sample group is divided to obtain at least two batch orders, where the number of orders in each batch order is preset in a single batch, and the number can be determined according to the actual order sample group and the setting in the sampling detection model, which is not limited herein. For example, the order sample group is divided into N/M batches according to the preset order quantity M in a single batch, and the last batch may be less than M. And each batch of orders are extracted and detected respectively, and even if detection errors occur in a certain batch of orders, the batch of orders are detected again, so that the reworking workload is greatly reduced.
Specifically, the order sample group is divided into a plurality of small batch orders, each divided batch order is independently extracted and detected, the authenticity of each batch order is determined according to the detection result of the sampling order in each batch order, and the real batch order result and the false batch order result are counted.
In one possible embodiment, after step 103, further comprising:
and determining an air control result of the merchant to be detected according to the authenticity result of the order sample group of the merchant to be detected based on a preset air control model, so as to determine a loan in result of the merchant to be detected according to the air control result.
The wind control model refers to a risk assessment system model for lending and admitting to merchants, and can be constructed according to the requirements of funds providers without limitation. Firstly, according to a sampling detection model, detecting a sampling order to be detected by a merchant to be detected to obtain an authenticity result of an order sample group, inputting the detected authenticity result into a wind control model, carrying out statistics and calculation by the wind control model according to the authenticity detection result of the order sample group, evaluating the current business condition, the integrity degree and the like of the merchant to be detected, and calculating a lending result of the merchant to be detected according to a preset risk control model, wherein the lending result comprises other parameters such as the lending amount, the risk degree and the like, and the method is not limited in particular according to actual requirements.
The embodiment of the invention is based on the acquired order sample group of the merchant to be detected; randomly extracting a preset number of sampling orders from an order sample group for detection; and determining the authenticity of the order sample group according to the detection result of the sampling order, wherein the preset quantity value of the sampling order is determined through a sampling detection model, and the sampling detection model is determined based on historical orders of merchants. Therefore, the detection cost of the sampling inspection of the order sample group is reduced, the accuracy of the sampling inspection of the order sample group is ensured, the accuracy of determining the authenticity of the whole order sample group according to the authenticity of the sampled order is ensured under the condition that the preset number of the sampling inspection is minimum, and the accuracy of determining the loan in result of the to-be-detected merchant according to the authenticity of the order sample group is further improved.
Example two
Fig. 2 is a flowchart of a method for constructing a sample detection model in a second embodiment of the present invention, which is applicable to a case where the number of sample orders for detection by randomly extracting a part of sample orders from an order sample group is determined, and the construction steps of the sample detection model in the first embodiment are described in detail on the basis of the first embodiment. As shown in fig. 2, the method includes:
Step 201, acquiring historical order verification data of a merchant.
Historical order validation data refers to order data that occurs for a period of time prior to generating an order to be checked. The historical order verification data is closer to the generation time of the order data to be detected, can effectively reflect the current business condition of the merchant, and has certain timeliness and accuracy. The time is not limited, and can be determined according to actual business operation time, business volume, historical factors and the like.
Specifically, a screening time period of the historical order verification data is set, and all the historical order verification data of the merchant in the time period are derived from an electronic merchant platform database where the merchant is located. The merchant may be the merchant to be detected or the same class and type of merchant as the merchant to be detected.
Step 202, determining the probability of a real batch order, the probability of a false batch order, the proportion of false orders in the real batch order and the proportion of false orders in the false batch order according to the historical order verification data; wherein the sum of the real lot order probability and the false lot order probability is 1.
Batch detection is performed on the historical order verification data, the order quantity of each batch can be preset, the limitation is not made here, and the authenticity detection is performed on each order in each batch, so that the final detection result of each batch is obtained, namely, the batch is a real batch or a false batch. Wherein the ratio of the number of real batches to the total number of batches is the real batch order probability, which is set as P 1 The method comprises the steps of carrying out a first treatment on the surface of the The ratio of the number of false batches to the total number of batches is the false batch order rate, which is set to P 2 The method comprises the steps of carrying out a first treatment on the surface of the In the real batch order, the ratio of the number of false orders to the total order number in the real batch order is set to p 1 The ratio of the number of real orders to the total number of orders in the real batch order is 1-p 1 The method comprises the steps of carrying out a first treatment on the surface of the In the false lot, the ratio of the number of false orders to the total number of orders in the false lot order is p 2 The ratio of the number of real orders to the total number of orders in the false batch order is 1-p 2 . Since the detection result of the order batch is only real batch and false batchTwo, therefore, the relation between the real lot order rate and the false lot order rate is P 2 ＝1-P 1 。
According to historical order verification data of a merchant, the probability of a real batch order, the probability of a false batch order, the proportion of a false order in the real batch order and the proportion of a false order in the false batch order of the sample detection model are obtained to serve as priori knowledge for evaluating the sample detection model of the merchant, and the sample to be detected is detected, so that the sample detection model has certain rationality and accuracy.
Step 203, determining that the probability of meeting the batch order to be detected as the real batch order is greater than the number of sampling orders under the condition of sampling detection confidence when the preset event occurs according to the probability of the real batch order, the probability of the false batch order, the proportion of the false order in the real batch order and the proportion of the false order in the false batch order based on the preset order quantity in the single batch and the sampling detection confidence, and the number is the preset number.
The preset event is to randomly extract part of sampling orders from the batch orders to be detected, wherein the part of sampling orders are real orders.
And reasonably setting the order quantity and the sampling detection confidence coefficient of a single batch according to the historical transaction order information of the merchant and the specific requirements of the risk control model, wherein the sampling detection confidence coefficient refers to the lowest probability that the set current batch order sample is a real batch order sample under the condition that a certain number of orders extracted from the current batch order sample are real orders.
By way of example, the order quantity for a single lot is set to M, here 200, the sample detection confidence is 90%, and the actual lot order probability, the false lot order probability, the proportion of false orders in the actual lot order, and the proportion of false orders in the false lot order have been obtained from the merchant's historical order verification data.
Carrying out random sample extraction on 200 orders of each batch, and sequentially verifying; in the extraction process, when one extraction sample is added, namely, according to a Bayesian formula, solving the probability that the current batch order is a real batch order under the condition that all the samples of the order to be detected extracted under the current batch are real order samples; and if the probability is greater than or equal to the sampling detection confidence, determining the number of the extracted samples corresponding to the probability as the preset number of the required solutions. If in the random sampling detection process, under the condition that all the samples of the to-be-detected orders extracted under the current batch are real order samples, when the probability that the current batch order is the real batch order does not reach the sampling detection confidence, false orders appear in the sampling order, and detection is stopped.
In one possible embodiment, step 203 includes:
based on the traversal algorithm, a preset number in the following formula is determined:
wherein K represents the order quantity of the partial sampling order randomly extracted from the batch order to be detected; p (true lot |a) represents the probability that the lot order to be detected is the true lot order when the preset event occurs; a (K) is to randomly extract K orders from the batch order to be detected when the batch order to be detected is a real batch order, wherein the K orders are all probabilities of the real orders, namelyb (K) is to randomly extract K orders from the batch order to be detected when the batch order to be detected is a false batch order, wherein the K orders are all probabilities of real orders, namely +.>P 1 For true batch order probability, P 2 For false lot order probability, p 1 For the proportion of false orders in real batch orders, p 2 Is the proportion of false orders in the false batch order.
Specifically, assume that the order quantity of a part of sampling orders is randomly extracted from batch orders to be detectedK, a (K) is the probability that K orders are randomly extracted from the batch order to be detected when the batch order to be detected is a real batch order and the K orders are all real orders, I.e. the real order quantity M (1-p) 1 ) I.e. a (K) is equal to M (1-p) in the real batch 1 ) The number of the combinations of K real orders randomly extracted from the real orders is divided by the number of the combinations of K real orders randomly extracted from all M orders in the real batch; b (K) is to randomly extract K orders from the batch order to be detected when the batch order to be detected is a false batch order, wherein K orders are all probability of real orders, and +.>I.e. the number of real orders present in a false batch order with order number M is M (1-p 2 ) B (K) is equal to M (1-p) in the order M false lot 2 ) The combined number of randomly extracted K real orders in the real orders of (c) divided by the combined number of randomly extracted K real orders in all orders in the dummy lot. When P (true lot |a) is required to meet an acceptable sample detection confidence level, a traversal search method is used to find the most suitable K value, namely, by programming traversal k= {1,2,3,.}, the first K value meeting P (true lot |a) is found to be greater than or equal to the sample detection confidence level. The meaning of the K value is that if all of the K samples sampled are real orders, then under sample detection confidence, the lot order belongs to the real order lot. If an unreal order occurs in the K specimens, the batch order is considered to belong to the unreal order batch.
In a possible embodiment, after determining the authenticity of the order sample group according to the detection result of the sampling order, on the basis of determining the preset number according to the sampling detection model constructed in the first embodiment, the method further includes:
and updating the probability of the real batch order, the probability of the false batch order, the proportion of the false order in the real batch order and the proportion of the false order in the false batch order according to the authenticity of the order sample group so as to update the sampling detection model.
Because the determination of the sampling detection model is performed according to the historical order verification data, after the authenticity of the order sample group is determined according to the detection result of the sampling order, the authenticity result of the order sample group also belongs to the historical order verification data, and the authenticity result of the order sample group is utilized to update the probability of determining the actual batch order, the probability of determining the false batch order, the proportion of the false order in the actual batch order and the proportion of the false order in the false batch order of the sampling detection model so as to ensure the accuracy and timeliness of priori knowledge. So as to avoid inaccurate results caused by business condition change of merchants. For example, a certain merchant submits the demand of borrowing and lending in the last year, the merchant performs the integrity management all the time in the operation process, the order is a real order, no false order exists, the system evaluates and judges according to the previous historical order and the to-be-detected order to obtain that the merchant has good operation condition, and a certain credit is provided, so that the merchant is borrowed and lended through the risk evaluation system. In order to further improve the product competitiveness, the commercial tenant in this year makes false work and falsifies, makes a lot of false order proceed charges, and also puts forward the demand for borrowing, if the system judges according to the previous data, the result judgment error will be caused, and at this time, the borrowing is carried out again with a larger risk.
Therefore, the probability of the real batch order, the probability of the false batch order, the proportion of the false order in the real batch order and the proportion of the false order in the false batch order are updated according to the authenticity of the order sample group, so that the sampling detection model is updated, and timeliness and accuracy of evaluation information of merchants are ensured.
The embodiment of the invention realizes the determination of the number of sampling orders from the order sample group based on the construction of the sampling detection model. Therefore, the detection cost of the sampling inspection of the order sample group is reduced, the accuracy of the sampling inspection of the order sample group is ensured, the accuracy of determining the authenticity of the whole order sample group according to the authenticity of the sampled order is ensured under the condition that the preset number of the sampling inspection is minimum, and the accuracy of determining the loan in result of the to-be-detected merchant according to the authenticity of the order sample group is further improved.
Fig. 3 is a schematic structural diagram of an order sampling detection system according to an embodiment of the present invention, where, as shown in fig. 3, the order sampling detection system includes a source data access module, a big data platform processing module, a sampling algorithm solving module, an order verification module, and an air control flow module. In the source data access module, the acquired merchant electronic platform order data and EPR system (electronic public relationsystem, network public gateway system) order data are synchronized to a data storage platform in the large data platform processing module, the order source data in the data storage platform are cleaned, target order data are collected, and then batch random sampling is carried out on the cleaned and collected order sample group. The size of the preset number K randomly extracted from each batch is determined according to a preset sampling detection model in a sampling algorithm solving module, and the sampling detection model is constructed based on a historical verification record.
The method comprises the steps that logistics list verification services in an order verification module are called for verifying K sampling orders which are randomly extracted from each batch, marking results are obtained after authenticity verification results are obtained, and the marking results are stored in a history verification record, wherein the history verification record can be used for determining calculation results of a wind control model in a wind control flow module on one hand so as to determine loan admittance results of merchants; on the other hand, the historical verification record in the sampling algorithm solving can be updated, so that the statistical parameters are updated, and further the sampling detection model is updated. When determining the authenticity result of each sampling order in the batch order, if any false order appears, ending the detection of the batch order, namely determining the batch order as a false batch order; if the K sampling orders randomly extracted from the batch are all real orders, determining that the batch order is a real batch order.
Example III
Fig. 4 is a schematic structural diagram of an order sampling detection device in a third embodiment of the present invention, which is applicable to a case where partial sampling orders are randomly extracted from an order sample group to detect, and a detection result of an overall order sample group is determined according to the detection result of the sampling orders. As shown in fig. 4, the apparatus includes:
An order acquisition module 310, configured to acquire an order sample group of merchants to be detected;
the sampling order extraction module 320 is configured to randomly extract a preset number of sampling orders from the order sample group for detection; the preset quantity is determined by adopting a sampling detection model, and the sampling detection model is determined based on historical orders of merchants;
an order authenticity determination module 330 is configured to determine authenticity of the order sample group according to a detection result of the sampled order.
The embodiment of the invention is based on the acquired order sample group of the merchant to be detected; randomly extracting a preset number of sampling orders from an order sample group for detection; and determining the authenticity of the order sample group according to the detection result of the sampling order, wherein the preset quantity value of the sampling order is determined through a sampling detection model, and the sampling detection model is determined based on historical orders of merchants. Therefore, the detection cost of the sampling inspection of the order sample group is reduced, the accuracy of the sampling inspection of the order sample group is ensured, the accuracy of determining the authenticity of the whole order sample group according to the authenticity of the sampled order is ensured under the condition that the preset number of the sampling inspection is minimum, and the accuracy of determining the loan in result of the to-be-detected merchant according to the authenticity of the order sample group is further improved.
Optionally, the apparatus further includes a sample detection model building module, including:
the historical order acquisition unit is used for acquiring historical order verification data of the merchant;
the historical probability determining unit is used for determining the probability of the real batch order, the probability of the false batch order, the proportion of the false order in the real batch order and the proportion of the false order in the false batch order according to the historical order verification data; wherein the sum of the real lot order probability and the false lot order probability is 1;
the preset quantity determining unit is used for determining the quantity of sampling orders under the condition that the probability of meeting the batch order to be detected as the real batch order is larger than the sampling detection confidence when a preset event occurs according to the probability of the real batch order, the probability of the false batch order, the proportion of the false order in the real batch order and the proportion of the false order in the false batch order, and the preset quantity based on the preset quantity of orders in the single batch and the sampling detection confidence;
the preset event is to randomly extract part of sampling orders from the batch orders to be detected, wherein the part of sampling orders are all real orders.
Optionally, the preset number determining unit is specifically configured to:
based on the traversal algorithm, a preset number in the following formula is determined:
wherein K represents the order quantity of the partial sampling order randomly extracted from the batch order to be detected; p (true lot |a) represents the probability that the lot order to be detected is the true lot order when the preset event occurs; a (K) is to randomly extract K orders from the batch order to be detected when the batch order to be detected is a real batch order, wherein the K orders are all probabilities of the real order, namelyb (K) is to randomly extract K orders from the batch order to be detected when the batch order to be detected is a false batch order, wherein the K orders are all probabilities of occurrence of real orders, namelyP 1 For the true lot order probability, P 2 For the probability of a false lot order,p 1 for the proportion of false orders in real batch orders, p 2 Is the proportion of false orders in the false batch order.
Optionally, the apparatus further comprises a model updating module for, after determining the authenticity of the order sample group based on the detection result of the sampled order,
and updating the probability of the real batch order, the probability of the false batch order, the proportion of the false order in the real batch order and the proportion of the false order in the false batch order according to the authenticity of the order sample group so as to update a sampling detection model.
Optionally, the order authenticity determining module is specifically configured to:
and if the detection results of the sampling orders are all true, determining the order sample group as a true order sample group.
Optionally, the device further includes an order dividing module, configured to divide the order sample group according to a preset order amount in a single batch before randomly extracting a preset number of sampling orders from the order sample group for detection, so as to obtain at least two batch orders;
correspondingly, the sampling order extraction module and the order authenticity determination module are specifically used for:
randomly extracting a preset number of sampling orders from each batch of orders in sequence for detection;
and determining the authenticity of the orders of each batch according to the detection result of the sampling order so as to obtain a real order sample group.
The device further comprises an air control result determining module, wherein the air control result determining module is used for determining the air control result of the to-be-detected merchant according to the authenticity result of the to-be-detected merchant order sample group based on a preset air control model after determining the authenticity of the order sample group according to the detection result of the sampling order, so as to determine the loan approval result of the to-be-detected merchant according to the air control result.
The order sampling detection device provided by the embodiment of the invention can execute the order sampling detection method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of executing the order sampling detection method.
Example IV
Fig. 5 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention. Fig. 5 illustrates a block diagram of an exemplary electronic device 12 suitable for use in implementing embodiments of the present invention. The electronic device 12 shown in fig. 5 is merely an example and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.
As shown in fig. 5, the electronic device 12 is in the form of a general purpose computing device. Components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory device 28, a bus 18 that connects the various system components, including the system memory device 28 and the processing unit 16.
Bus 18 represents one or more of several types of bus structures, including a memory device bus or memory device controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, micro channel architecture (MAC) bus, enhanced ISA bus, video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Electronic device 12 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by electronic device 12 and includes both volatile and nonvolatile media, removable and non-removable media.
The system storage 28 may include computer system readable media in the form of volatile memory such as Random Access Memory (RAM) 30 and/or cache memory 32. The electronic device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from or write to non-removable, nonvolatile magnetic media (not shown in FIG. 5, commonly referred to as a "hard disk drive"). Although not shown in fig. 5, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In such cases, each drive may be coupled to bus 18 through one or more data medium interfaces. The storage device 28 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of the embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in storage 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 42 generally perform the functions and/or methods of the embodiments described herein.
The electronic device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), one or more devices that enable a user to interact with the device 12, and/or any devices (e.g., network card, modem, etc.) that enable the device 12 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 22. Also, the electronic device 12 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet, through a network adapter 20. As shown in fig. 5, the network adapter 20 communicates with other modules of the electronic device 12 over the bus 18. It should be appreciated that although not shown in fig. 5, other hardware and/or software modules may be used in connection with electronic device 12, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
The processing unit 16 executes various functional applications and data processing by running programs stored in the system storage 28, for example, implementing the order sampling detection method provided by the embodiment of the present invention, including:
acquiring an order sample group of a merchant to be detected;
randomly extracting a preset number of sampling orders from the order sample group for detection; the preset quantity is determined by adopting a sampling detection model, and the sampling detection model is determined based on historical orders of merchants;
and determining the authenticity of the order sample group according to the detection result of the sampling order.
Example five
The fifth embodiment of the present invention further provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the order sampling detection method as provided by the embodiments of the present invention, including:
acquiring an order sample group of a merchant to be detected;
randomly extracting a preset number of sampling orders from the order sample group for detection; the preset quantity is determined by adopting a sampling detection model, and the sampling detection model is determined based on historical orders of merchants;
and determining the authenticity of the order sample group according to the detection result of the sampling order.
The computer storage media of embodiments of the invention may take the form of any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present invention may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (7)
1. An order sampling detection method, comprising:
acquiring an order sample group of a merchant to be detected;
randomly extracting a preset number of sampling orders from the order sample group for detection; the preset quantity is determined by adopting a sampling detection model, and the sampling detection model is determined based on historical orders of merchants;
the construction steps of the sampling detection model are as follows:
acquiring historical order verification data of a merchant;
determining the probability of a real batch order, the probability of a false batch order, the proportion of false orders in the real batch order and the proportion of false orders in the false batch order according to the historical order verification data; wherein the sum of the real lot order probability and the false lot order probability is 1;
based on the preset order quantity in the single batch and the sampling detection confidence, determining that the probability of meeting the condition that the batch order to be detected is the real batch order when the preset event occurs is larger than the number of sampling orders under the condition of the sampling detection confidence according to the real batch order probability, the false batch order probability, the proportion of false orders in the real batch order and the proportion of false orders in the false batch order, and the preset number;
The preset event is a part of sampling orders randomly extracted from the batch orders to be detected, and the part of sampling orders are all real orders;
based on the preset order quantity in the single batch and the sampling detection confidence, determining that the probability of meeting the condition that the batch order to be detected is the real batch order is greater than the number of sampling orders under the condition of the sampling detection confidence when the preset event occurs according to the real batch order probability, the false batch order probability, the proportion of the false order in the real batch order and the proportion of the false order in the false batch order, and the preset number comprises:
based on the traversal algorithm, a preset number in the following formula is determined:
wherein K represents the order quantity of the partial sampling order randomly extracted from the batch order to be detected; p (true lot |a) represents the probability that the lot order to be detected is the true lot order when the preset event occurs; a (K) is to randomly extract K orders from the batch order to be detected when the batch order to be detected is a real batch order, wherein the K orders are all probabilities of the real order, namelyb (K) is to randomly extract K orders from the batch order to be detected when the batch order to be detected is a false batch order, wherein the K orders are all probabilities of occurrence of real orders, namely P 1 For the true lot order probability, P 2 For false lot order probability, p 1 For the proportion of false orders in real batch orders, p 2 The proportion of false orders in the false batch orders;
determining the authenticity of the order sample group according to the detection result of the sampling order;
after determining the authenticity of the order sample group according to the detection result of the sampling order, the method further comprises the following steps:
and determining a wind control result of the commercial tenant to be detected according to the authenticity result of the order sample group of the commercial tenant to be detected based on a preset wind control model, so as to determine a loan in result of the commercial tenant to be detected according to the wind control result.
2. The method of claim 1, further comprising, after determining the authenticity of the sample collection of orders based on the detection of the sampled orders:
and updating the probability of the real batch order, the probability of the false batch order, the proportion of the false order in the real batch order and the proportion of the false order in the false batch order according to the authenticity of the order sample group so as to update a sampling detection model.
3. The method of claim 1, wherein determining the authenticity of the sample collection of orders based on the detection of the sampled orders comprises:
And if the detection results of the sampling orders are all true, determining the order sample group as a true order sample group.
4. The method of claim 1, further comprising, prior to randomly extracting a predetermined number of sample orders from the sample collection of orders for testing:
dividing the order sample group according to the preset order quantity in a single batch to obtain at least two batch orders;
correspondingly, randomly extracting a preset number of sampling orders from the order sample group for detection, and determining the authenticity of the order sample group according to the detection result of the sampling orders, wherein the method comprises the following steps:
and randomly extracting a preset number of sampling orders from each batch of orders in sequence for detection, and determining the authenticity of each batch of orders according to the detection result of the sampling orders to obtain a real order sample group.
5. An order sample detection device, comprising:
the order acquisition module is used for acquiring an order sample group of the merchant to be detected;
the sampling order extraction module is used for randomly extracting a preset number of sampling orders from the order sample group for detection; the preset quantity is determined by adopting a sampling detection model, and the sampling detection model is determined based on historical orders of merchants;
The sampling detection model construction module comprises:
the historical order acquisition unit is used for acquiring historical order verification data of the merchant;
the historical probability determining unit is used for determining the probability of the real batch order, the probability of the false batch order, the proportion of the false order in the real batch order and the proportion of the false order in the false batch order according to the historical order verification data; wherein the sum of the real lot order probability and the false lot order probability is 1;
the preset quantity determining unit is used for determining the quantity of sampling orders under the condition that the probability of meeting the batch order to be detected as the real batch order is larger than the sampling detection confidence when a preset event occurs according to the probability of the real batch order, the probability of the false batch order, the proportion of the false order in the real batch order and the proportion of the false order in the false batch order, and the preset quantity based on the preset quantity of orders in the single batch and the sampling detection confidence;
the method comprises the steps that a preset event is that partial sampling orders are randomly extracted from the batch orders to be detected, and the partial sampling orders are real orders;
the preset number determining unit is specifically configured to:
Based on the traversal algorithm, a preset number in the following formula is determined:
wherein K represents the order of randomly extracting part of sampling orders from the batch orders to be detectedA single number; p (true lot |a) represents the probability that the lot order to be detected is the true lot order when the preset event occurs; a (K) is to randomly extract K orders from the batch order to be detected when the batch order to be detected is a real batch order, wherein the K orders are all probabilities of the real order, namelyb (K) is to randomly extract K orders from the batch order to be detected when the batch order to be detected is a false batch order, wherein the K orders are all probabilities of occurrence of real orders, namelyP 1 For the true lot order probability, P 2 For false lot order probability, p 1 For the proportion of false orders in real batch orders, p 2 The proportion of false orders in the false batch orders;
the order authenticity determining module is used for determining the authenticity of the order sample group according to the detection result of the sampling order;
and the wind control result determining module is used for determining the wind control result of the merchant to be detected according to the authenticity result of the order sample group of the merchant to be detected based on a preset wind control model after determining the authenticity of the order sample group according to the detection result of the sampling order, so as to determine the loan in result of the merchant to be detected according to the wind control result.
6. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs,
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the order sample detection method of any of claims 1-4.
7. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements the order sampling detection method as claimed in any one of claims 1-4.
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