A medicine order verification method, device, equipment and storage medium

Abstract

The present invention discloses a method, device, equipment and storage medium for verifying a medicine order. The method includes: in the process of sorting medicines according to first order data, receiving the real-time total weight measured for the sorted medicines; querying the second order data and third order data of historical completed transactions; predicting a first candidate total weight for the medicines in the first order data according to the second order data; predicting a second candidate total weight for the medicines in the first order data according to the third order data; fusing the first candidate total weight and the second candidate total weight into a target total weight; calculating the degree to which the target total weight deviates from the real-time total weight as a weight deviation; and verifying whether the sorted medicines comply with the first order data based on the weight deviation. This embodiment improves the accuracy of verification, provides a reference for verification for staff, reduces the frequency of errors during verification, and reduces the occurrence of problems such as wrong shipment, missed shipment, and multiple shipment.

Description

Technical Field

[0001] The present invention relates to the technical field of warehousing, and in particular to a method, device, equipment and storage medium for verifying a medicine order. Background Art

[0002] E-commerce platforms sell various medicines to users. Users place orders from the medicine sales platform, and the medicine sales platform generates corresponding orders. The order will record a list of medicines. The staff picks the goods according to the order and verifies whether the sorted medicines are correct based on the list. When the verification is completed, the medicines are packaged and mailed to the user by express delivery.

[0003] Due to the large volume of orders on e-commerce platforms, the staff have a heavy workload in sorting and packing, and errors are prone to occur during verification, leading to problems such as wrong shipments, missed shipments, and multiple shipments. Summary of the Invention

[0004] The present invention provides a method, device, equipment and storage medium for verifying a medicine order, so as to solve the problem of how to improve the accuracy of verifying the medicines in the order.

[0005] According to one aspect of the present invention, a method for verifying a medicine order is provided, comprising:

[0006] In the process of sorting the medicines according to the first order data, receiving a real-time total weight measured for the sorted medicines;

[0007] Query the second and third order data of historical completed transactions. The drugs in the second order data are the same as those in the first order data in terms of category and quantity. The drug quantity in the third order data is one and is of a single category of drugs in the first order data.

[0008] predicting a first candidate total weight for the medicine in the first order data based on the second order data;

[0009] predicting a second candidate total weight for the drugs in the first order data based on the third order data;

[0010] fusing the first candidate total weight and the second candidate total weight into a target total weight;

[0011] Calculating the degree to which the target total weight deviates from the real-time total weight as a weight deviation;

[0012] The sorted medicines are checked to see whether they comply with the first order data based on the weight deviation.

[0013] According to another aspect of the present invention, there is provided a device for verifying a medicine order, comprising:

[0014] A real-time total weight measurement module, configured to receive a real-time total weight of the sorted medicines during the process of sorting the medicines according to the first order data;

[0015] An order data query module, configured to query second and third order data of historically completed transactions, wherein the drugs in the second order data are the same in category and quantity as the drugs in the first order data, and the quantity of the drugs in the third order data is one and is of a single category of the drugs in the first order data;

[0016] A first candidate weight total amount prediction module is configured to predict a first candidate weight total amount for the medicine in the first order data based on the second order data;

[0017] A second candidate weight total amount prediction module is used to predict a second candidate weight total amount for the drugs in the first order data based on the third order data;

[0018] a target total weight fusion module, configured to fuse the first candidate total weight and the second candidate total weight into a target total weight;

[0019] a weight deviation calculation module, configured to calculate the degree to which the target total weight deviates from the real-time total weight as a weight deviation;

[0020] An order data verification module is used to verify whether the sorted medicines comply with the first order data based on the weight deviation.

[0021] According to another aspect of the present invention, an electronic device is provided, comprising:

[0022] at least one processor; and

[0023] a memory communicatively connected to the at least one processor; wherein,

[0024] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the drug order verification method described in any embodiment of the present invention.

[0025] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and the computer program is used to enable a processor to implement the method for verifying a drug order according to any embodiment of the present invention when executed.

[0026] In this embodiment, in the process of sorting medicines according to the first order data, the real-time total weight measured for the sorted medicines is received; the second order data and the third order data of the historical completed transactions are queried, the medicines in the second order data are the same as the medicines in the first order data in category and quantity, the quantity of medicines in the third order data is one, and is a single category of the medicines in the first order data; the first candidate total weight is predicted for the medicines in the first order data based on the second order data; the second candidate total weight is predicted for the medicines in the first order data based on the third order data; the first candidate total weight and the second candidate total weight are merged into a target total weight; the degree to which the target total weight deviates from the real-time total weight is calculated as the weight deviation; and the sorted medicines are checked whether they comply with the first order data based on the weight deviation. This embodiment comprehensively predicts the theoretical target total weight of all drugs in the first order data from two aspects: real identical orders and virtual identical orders based on orders of single category and single quantity. Regardless of whether it is an order of single category and single quantity or a fusion of the total amount of the two aspects, the weight error is minimized as much as possible to ensure the accuracy of the target total weight. The deviation between the target total weight and the actual total weight measured for the sorted drugs is used to verify whether the sorted drugs comply with the first order data. This can improve the accuracy of the verification, provide weight verification results for sorting and packaging of drugs, provide a reference for verification for staff, reduce the frequency of errors during verification, and reduce the occurrence of problems such as wrong shipment, missed shipment, and multiple shipment.

[0027] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0029] Figure 1 This is a flow chart of a method for verifying a medicine order according to the first embodiment of the present invention;

[0030] Figure 2 This is a schematic structural diagram of a drug order verification device provided in accordance with a second embodiment of the present invention;

[0031] Figure 3 It is a structural diagram of an electronic device provided according to the third embodiment of the present invention. DETAILED DESCRIPTION

[0032] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0033] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0034] Example 1

[0035] Figure 1 This is a flow chart of a method for verifying a drug order provided in the first embodiment of the present invention. This embodiment is applicable to the case where the drugs in an order are verified based on the total amount of the same order and the total amount of the predicted order of a single drug category. The method can be executed by a drug order verification device, which can be implemented in the form of hardware and / or software, and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0036] Step 101: During the process of sorting medicines according to first order data, receive the real-time total weight measured for the sorted medicines.

[0037] The user logs in to the e-commerce platform that sells medicines on the client, selects one or more medicines on the e-commerce platform to place an order, and completes the payment. At this time, the system of the e-commerce platform will generate corresponding first order data, where the first order data includes the order number, order time, medicine category, quantity of single-category medicines, amount information (amount of single-category medicines, discount, total amount), recipient, telephone number, delivery address and other information.

[0038] The e-commerce platform's system pushes multiple orders, including the first order data, to the warehouse, and instructs staff to perform wave picking in the warehouse based on the multiple orders. Wave picking combines different orders into one wave according to certain standards, and guides picking once. That is, wave picking is to classify and pick multiple orders.

[0039] Wave picking can include the following types:

[0040] 1. Order quantity in batches

[0041] Orders are sorted into batches on a first-come, first-served basis. When the accumulated order volume reaches a pre-set quantity indicator, this quantity indicator is calculated based on the number of drug categories in each order, the degree of overlap between drug categories in orders, and the number of drugs in each category. Within the tolerance of the sorting equipment, the accumulated orders are aggregated into a wave.

[0042] 2. Delivery routes are divided into batches

[0043] Whether delivering medicines by private vehicles or through a third-party logistics company, the delivery addresses are usually arranged according to specific delivery routes or regions. Therefore, the delivery route can be considered as a factor in wave division.

[0044] Since each delivery route has an agreed loading time, this batching method also implies a requirement for the wave completion time.

[0045] 3. Time window batching

[0046] When the order completion time is relatively tight, you can intercept data every certain time window according to the order completion time requirements, and summarize the accumulated orders during this period into a wave.

[0047] 4. Energy type batching

[0048] Intelligent batching is to aggregate orders and divide them into waves based on a certain indicator as the optimization goal.

[0049] For the first order data, if all the medicines in the first order data have been sorted out during the wave sorting process, the staff can put all the medicines sorted out in the first order data on the electronic scale to obtain the weight of all the medicines sorted out in the first order data, and record it as the real-time total weight.

[0050] At this time, the e-commerce platform system can receive the real-time total weight of all sorted medicines measured and input by the staff, or receive the real-time total weight of all sorted medicines measured and sent by the electronic scale.

[0051] Step 102: Query the second order data and the third order data of the historical completed transactions.

[0052] In actual applications, as a complete and independent commodity, medicines, in addition to the medicines themselves, also have outer packaging (such as bottles, cartons, etc.), packaging (such as aluminum foil, etc.), excipients (such as desiccants, etc.), instructions and other materials. These materials are selected from different materials because of the different values of the medicines themselves. The weight of different materials varies greatly, which makes the proportion of the weight of the medicine itself to the overall weight of the medicine vary greatly, ranging from as little as 10% to as much as 80%.

[0053] In addition to the fact that the drugs themselves have strict production specifications and relatively constant weight, manufacturers may adjust the materials of external packaging, encapsulation packaging, excipients, instructions and other materials based on factors such as product design and fluctuations in raw material costs, causing the weight of these materials to fluctuate to a certain extent. The fluctuations in material weight caused by these adjustments are mostly not made public.

[0054] In addition, in all historical orders, users may provide feedback to the e-commerce platform in the event of wrong delivery, omission, or excess delivery of drugs, thereby correcting the order and completing the transaction (commonly known as receiving the goods). They may also not be aware of the wrong delivery, omission, or excess delivery of drugs, or they may not proactively provide feedback to the e-commerce platform in the event of wrong delivery, omission, or excess delivery of drugs, resulting in the order not being corrected and the transaction being completed directly (commonly known as receiving the goods). Therefore, the drugs in orders for which transactions have been completed (received) in the past are not necessarily accurate.

[0055] In this embodiment, the weight of all medicines in the first order data can be evaluated from two dimensions, one dimension is the same order, and the other dimension is the order of a single category and a single quantity.

[0056] In the strategies for selling medicines on e-commerce platforms, there are often activities that guide users to purchase medicines of the same category (including quantity). For example, medicines of multiple categories (including quantity) are sold in combination (such as a combination for treating influenza including antipyretics, antiviral drugs, etc.), and multiple medicines of one category are packaged as a course of treatment for sale, with M pieces for N yuan, M yuan off for purchases over N yuan, etc. As a result, a large number of identical orders are accumulated in the e-commerce platform system.

[0057] In the sales history of e-commerce platforms, orders for single categories account for the majority, and single category orders are mainly low-quantity orders (such as less than or equal to 5 pieces). Therefore, a large number of single category and single quantity orders will accumulate in the e-commerce platform system.

[0058] Furthermore, although the weight of single-category and single-quantity drugs can be obtained by averaging the weights of the drugs in single-category and multiple-quantity orders, this averaging model will amplify the weight error when evaluating the total amount of drugs in the first order data. Therefore, single-category and multiple-quantity orders can be ignored in another dimension.

[0059] Then, under these two dimensions, you can use the first order data as a reference to query the second and third order data of historical completed transactions from all historical orders.

[0060] The medicines in the second order data are the same as those in the first order data in terms of category and quantity, that is, the second order data and the first order data are the same order.

[0061] For example, the first order data includes 10 boxes of Category A medicines, 2 bottles of Category B medicines, and 10 bags of Category C medicines, and the second order data also includes 10 boxes of Category A medicines, 2 bottles of Category B medicines, and 10 bags of Category C medicines.

[0062] The quantity of medicine in the third order data is one, and it is a single category of medicine in the first order data.

[0063] For example, the first order data includes 10 boxes of Category A medicine, 2 bottles of Category B medicine and 10 bags of Category C medicine. One of the third order data includes 1 box of Category A medicine, another third order data includes 1 bottle of Category B medicine, and another third order data includes 1 bag of Category C medicine.

[0064] In a specific implementation, the first identification information, the first manufacturer information, the first production batch number, and the first approval number of each category of drugs in the first order data can be queried.

[0065] The second identification information, the second manufacturer information, the second production batch number, and the second approval number of each category of drugs in the historical order data of the completed transactions are queried.

[0066] The first identification information is identical to the second identification information, the first manufacturer information is identical to the second manufacturer information, the first production batch number is identical to the second production batch number, and the first approval number is compared with the second approval number.

[0067] If the first identification information is the same as the second identification information (i.e., the same drug), the first manufacturer information is the same as the second manufacturer information (i.e., the same manufacturer), the first production batch number is the same as the second production batch number (i.e., the same production batch number), and the first approval number is the same as the second approval number (i.e., the same approval number), then it is determined that the category of the drug in the first order data is the same as the category of the drug in the historical order data.

[0068] Count the number of medicines in each category in historical order data.

[0069] If the medicines in the historical order data are the same as those in the first order data in terms of category and quantity, the historical order data is determined to be the second order data.

[0070] If the historical order data contains a single category and a quantity of one drug, and the drug is the same as any category of the drug in the first order data, then the historical order data is determined to be the third order data.

[0071] In this embodiment, by limiting the same drug, the same manufacturer, the same production batch number, and the same approval number, the fluctuation in the weight of materials other than the drug itself when the manufacturer produces the drug can be minimized as much as possible, thereby improving the accuracy of evaluating the weight of all drugs in the first order data, thereby improving the accuracy of verifying the sorted drugs in the first order data.

[0072] Step 103: predict a first candidate total weight for the medicines in the first order data based on the second order data.

[0073] In this embodiment, the second order data is an order that is actually the same as the first order data. The weight of all medicines in the second order data can be linearly or nonlinearly fused to predict the weight of all medicines in theory in the first order data and obtain the first candidate total weight.

[0074] In one example, when a worker sorts medicines according to an order, the worker records the weight of all sorted medicines in the order.

[0075] Therefore, the weight of the medicines that have been sorted in the history and recorded in each second order data can be queried and recorded as the historical total weight. The average of all historical total weights can be calculated as the first candidate total weight predicted for the medicines in the first order data.

[0076] Of course, the above-mentioned method for calculating the first candidate total weight is merely an example. When implementing this embodiment, other methods for calculating the first candidate total weight may be set according to actual circumstances. For example, the historical total weights of the drugs that have been sorted and recorded in each second order data item may be queried, and weights may be assigned to the historical total weights based on the time of the second order data item, wherein the distance between the time of the second order data item and the current time item is negatively correlated with the weight, and the historical total weights may be multiplied and summed (i.e., weighted summation) to obtain the predicted first candidate total weight for the drugs in the first order data item, etc. This embodiment does not limit this. In addition, in addition to the above-mentioned method for calculating the first candidate total weight, those skilled in the art may also adopt other methods for calculating the first candidate total weight according to actual needs, and this embodiment does not limit this.

[0077] Step 104: predict a second candidate total weight for the drugs in the first order data based on the third order data.

[0078] In this embodiment, the third order data serves as an order of a single category and a single quantity in the first order data. The weights of the drugs in the third order data can be combined based on the categories of the drugs and the quantities of the drugs in each category in the first order data. That is, multiple third order data are used to simulate the same order as the first order data, and the weights of the drugs in multiple third order data under the same order are combined, so as to predict the weight of all the drugs theoretically in the first order data and obtain the second candidate total weight.

[0079] In one example, on the one hand, the quantity of each category of medicines in the first order data can be queried, and on the other hand, the weight of the medicines that have been historically sorted and recorded in the second order data can be queried to obtain the historical unit weight.

[0080] For drugs of the same category, the largest N historical unit weights and / or the smallest M historical unit weights are deleted, where N and M are both positive integers. This can reduce interference such as measurement errors of electronic scales, foreign objects stuffed in drugs, and drug damage.

[0081] If the deletion is completed, for drugs of the same category, the average value of all historical monomer weights is calculated to obtain the predicted monomer weight.

[0082] For each category of medicine in the first order data, the product between the predicted unit weight and the quantity is calculated to obtain the predicted category weight.

[0083] If the weight of any predicted category is 0 and does not constitute the same order, it can be determined that the total weight of the second candidate predicted drug for the first order data is 0.

[0084] If all predicted category weights are not 0 and constitute the same order, then all predicted category weights may be added together to obtain the total second candidate weight predicted for the drug in the first order data.

[0085] Of course, the above-mentioned method of calculating the second candidate total weight is only an example. When implementing this embodiment, other methods of calculating the second candidate total weight can be set according to actual conditions. For example, the third order data of the same time period are combined into the same order as the first order data, and the total weight of these third order data is evaluated. The total weight is assigned a weight according to the time period, wherein the distance between the time period and the current time is negatively correlated with the weight, and the total weight is multiplied and summed (i.e., weighted summation) to obtain the second candidate total weight predicted for the drug of the first order data, etc. This embodiment does not limit this. In addition, in addition to the above-mentioned method of calculating the second candidate total weight, those skilled in the art can also adopt other methods of calculating the second candidate total weight according to actual needs, and this embodiment does not limit this.

[0086] Step 105: Merge the first candidate total weight and the second candidate total weight into a target total weight.

[0087] When at least one of the second order and the third order of any category is not empty, that is, the first candidate total weight is not 0 and / or the first candidate total weight is not 0, the first candidate total weight and the second candidate total weight can be fused in a linear or nonlinear manner to obtain a target total weight that is comprehensively predicted in two dimensions.

[0088] Taking linear fusion as an example, considering that the second order data may be empty and the third order data of a certain category may be empty, the first weight of the first candidate total weight and the second weight of the second candidate total weight can be configured adaptively according to different situations.

[0089] In one case, the second order data is empty, so that the first candidate total weight is 0, and the third order data of all categories are not empty, so that the second candidate total weight is not 0.

[0090] If the total weight of the first candidate is 0 and the total weight of the second candidate is not 0, the first candidate total weight is configured with 0 as the first weight and the second candidate total weight is configured with 1 as the second weight to ensure that subsequent operations are performed normally.

[0091] In another case, the second order data is not empty, so that the first candidate total weight is not 0, and the third order data of all categories are not empty, so that the second candidate total weight is not 0.

[0092] If the total weight of the first candidate is not 0 and the total weight of the second candidate is not 0, a first value is configured for the first candidate total weight as a first weight, and a second value is configured for the second candidate total weight as a second weight.

[0093] Among them, considering that the weight error of drugs in the real same order is lower than the weight error of drugs in the virtual same order, the first value can be set to be greater than the second value, for example, the first value is 0.6 and the second value is 0.4, so as to increase the importance of the first candidate total weight and reduce the error of the target total weight.

[0094] In another case, the second order data is not empty, so that the first candidate total weight is not 0, and the third order data of at least one category is empty, so that the second candidate total weight is 0.

[0095] If the total weight of the first candidate is not 0 and the total weight of the second candidate is 0, the first candidate total weight is configured as 1 as the first weight and the second candidate total weight is configured as 0 as the second weight to ensure that subsequent operations are performed normally.

[0096] The product between the first candidate total weight and the first weight is calculated to obtain the first weighted total weight, and the product between the second candidate total weight and the second weight is calculated to obtain the second weighted total weight, thereby calculating the sum of the first weighted total weight and the second weighted total weight as the target total weight.

[0097] Then, the process of target total weight is expressed as:

[0098] T=k1*a+k2*b

[0099] Wherein, T is the target total weight, a is the first candidate total weight, b is the second candidate total weight, k1 is the first weight, and k2 is the second weight.

[0100] If a=0, b≠0, then k1=0, k2=1; if a≠0, W2≠0, then k1=α, k2=β, α is the first value (such as 0.6), β is the second value (such as 0.4); if a≠0, b=0, then k1=1, k2=0.

[0101] In addition, when both the second order and the third order of any category are empty, you can ignore the verification of whether the sorted medicines comply with the first order data under the weight dimension, and wait for the staff to refer to the results of other dimensions except weight as a reference to verify whether the sorted medicines comply with the first order data. You can also predict the target total weight of the medicines in the first order data based on the weight recorded when the medicines of each category in the first order data are put into storage.

[0102] In a specific implementation, if the first candidate total weight is 0 and the first candidate total weight is 0, the quantity of each category of medicines in the first order data can be queried, as well as the inventory unit weight of each category of medicines recorded in the first order data. The inventory unit weight is the weight recorded on the outer packaging of the medicine by the manufacturer when producing the medicine. It may be the same as the actual weight of the medicine, or it may deviate from the actual weight of the medicine.

[0103] For each category of medicine in the first order data, the product of the inventory unit weight and the quantity is calculated to obtain the inventory category weight.

[0104] The weights of all inventory categories are added together to obtain the target total weight of the medicines predicted for the first order data.

[0105] Step 106: Calculate the degree to which the target total weight deviates from the real-time total weight as the weight deviation.

[0106] In this embodiment, the degree to which the target total weight deviates from the real-time total weight may be calculated based on the real-time total weight as the weight deviation.

[0107] In one example, the absolute value of the difference between the target total weight and the real-time total weight can be taken as the weight difference, and the ratio between the weight difference and the real-time total weight can be calculated to obtain the degree to which the target total weight deviates from the real-time total weight and use it as the weight deviation.

[0108] In this example, the weight deviation is calculated as:

[0109] P=abs(TS) / S

[0110] Among them, P is the weight deviation, T is the target total weight, S is the real-time total weight, and abs is the absolute value.

[0111] Of course, the above-mentioned method for calculating the weight deviation is merely an example. When implementing this embodiment, other methods for calculating the weight deviation may be set according to actual circumstances. For example, the absolute value of the difference between the target total weight and the real-time total weight may be taken as the weight difference, and a specified ratio may be taken for the weight difference to determine the degree to which the target total weight deviates from the real-time total weight, and the weight deviation may be obtained, etc. This embodiment does not limit this. In addition, in addition to the above-mentioned method for calculating the weight deviation, those skilled in the art may also adopt other methods for calculating the weight deviation according to actual needs, and this embodiment does not limit this.

[0112] Step 107: Check whether the sorted medicines comply with the first order data based on the weight deviation.

[0113] In this embodiment, it is possible to evaluate whether the weight deviation is a reasonable deviation, thereby verifying whether the sorted medicines comply with the first order data in terms of weight.

[0114] In a specific implementation, the weight deviation may be compared with a preset threshold value, which is an empirical value and represents a reasonable range of the deviation.

[0115] If the weight deviation is greater than the preset threshold, it means that the weight deviation is large and exceeds the reasonable range. It can be determined that the sorted medicines do not meet the first order data in terms of weight, and there may be cases of missed, over-delivered, or wrongly delivered medicines.

[0116] If the weight deviation is less than or equal to the preset threshold, it means that the weight deviation is small and within a reasonable range, and it can be determined that the sorted medicines in terms of weight meet the first order data.

[0117] Furthermore, verifying whether the sorted drugs conform to the first order data in terms of weight can be one of the dimensions for evaluating whether the sorted drugs conform to the first order data. In addition to weight, the conformity of the sorted drugs to the first order data can also be evaluated from dimensions such as image data. By combining and integrating these dimensions, the final result of verifying whether the sorted drugs conform to the first order data is obtained.

[0118] If the sorted medicines are finally verified to be consistent with the first order data, the staff is allowed to pack the sorted medicines, affix the information of the first order data, and wait for shipment.

[0119] If the sorted drugs are finally verified to be consistent with the first order data, the staff is prohibited from packing the sorted drugs and is prompted to review the sorted drugs.

[0120] In this embodiment, in the process of sorting medicines according to the first order data, the real-time total weight measured for the sorted medicines is received; the second order data and the third order data of the historical completed transactions are queried, the medicines in the second order data are the same as the medicines in the first order data in category and quantity, the quantity of medicines in the third order data is one, and is a single category of the medicines in the first order data; the first candidate total weight is predicted for the medicines in the first order data based on the second order data; the second candidate total weight is predicted for the medicines in the first order data based on the third order data; the first candidate total weight and the second candidate total weight are merged into a target total weight; the degree to which the target total weight deviates from the real-time total weight is calculated as the weight deviation; and the sorted medicines are checked whether they comply with the first order data based on the weight deviation. This embodiment comprehensively predicts the theoretical target total weight of all drugs in the first order data from two aspects: real identical orders and virtual identical orders based on orders of single category and single quantity. Regardless of whether it is an order of single category and single quantity or a fusion of the total amount of the two aspects, the weight error is minimized as much as possible to ensure the accuracy of the target total weight. The deviation between the target total weight and the actual total weight measured for the sorted drugs is used to verify whether the sorted drugs comply with the first order data. This can improve the accuracy of the verification, provide weight verification results for sorting and packaging of drugs, provide a reference for verification for staff, reduce the frequency of errors during verification, and reduce the occurrence of problems such as wrong shipment, missed shipment, and multiple shipment.

[0121] Example 2

[0122] Figure 2 This is a schematic diagram of the structure of a drug order verification device provided by the second embodiment of the present invention. Figure 2 As shown, the device includes:

[0123] A real-time total weight measurement module 201 is configured to receive a real-time total weight of the sorted medicines during the process of sorting the medicines according to the first order data;

[0124] An order data query module 202 is configured to query second and third order data of historical completed transactions, wherein the drugs in the second order data are the same as those in the first order data in terms of category and quantity, and the drug quantity in the third order data is one and is of a single category in the first order data;

[0125] A first candidate weight total amount prediction module 203 is configured to predict a first candidate weight total amount for the medicine in the first order data based on the second order data;

[0126] A second candidate weight total amount prediction module 204 is configured to predict a second candidate weight total amount for the drugs in the first order data based on the third order data;

[0127] a target total weight fusion module 205 , configured to fuse the first candidate total weight and the second candidate total weight into a target total weight;

[0128] a weight deviation calculation module 206 for calculating the degree to which the target total weight deviates from the real-time total weight as a weight deviation;

[0129] The order data verification module 207 is used to verify whether the sorted medicines comply with the first order data based on the weight deviation.

[0130] In one embodiment of the present invention, the order data query module 202 includes:

[0131] a first parameter query module, configured to query first identification information, first manufacturer information, first production batch number, and first approval number of each category of drugs in the first order data;

[0132] The second parameter query module is used to query the second identification information, the second manufacturer information, the second production batch number, and the second approval number of each category of drugs in the historical order data of the completed transactions;

[0133] a same-category determination module, configured to determine that the category of the drug in the first order data is the same as the category of the drug in the historical order data if the first identification information is the same as the second identification information, the first manufacturer information is the same as the second manufacturer information, the first production batch number is the same as the second production batch number, and the first approval number is the same as the second approval number;

[0134] A historical quantity statistics module is used to count the quantity of medicines of each category in the historical order data;

[0135] a same order determination module, configured to determine that the historical order data is the second order data if the drugs in the historical order data are the same as the drugs in the first order data in terms of category and quantity;

[0136] A single product order determination module is used to determine that the historical order data is third order data if the historical order data contains a single category and a quantity of one drug, and the drug is the same category as any drug in the first order data.

[0137] In one embodiment of the present invention, the first candidate weight amount prediction module 203 includes:

[0138] A historical total weight query module, used to query the historical total weight of the medicines that have been sorted and recorded in each of the second order data;

[0139] a total weight average value calculation module, configured to calculate an average value of all the historical total weights as a first candidate total weight for drug prediction of the first order data;

[0140] The second candidate weight total amount prediction module 204 includes:

[0141] a drug quantity query module, configured to query the quantity of drugs of each category in the first order data;

[0142] A historical unit weight query module, used to query the historical unit weights of the drugs that have been sorted in the past and recorded in the second order data;

[0143] A historical unit weight deletion module is used to delete the largest N historical unit weights and / or the smallest M historical unit weights for drugs of the same category;

[0144] A predicted monomer weight calculation module is used to calculate the average value of all the historical monomer weights of drugs of the same category if the deletion is completed to obtain a predicted monomer weight;

[0145] a predicted category weight calculation module, configured to calculate, for each category of medicine in the first order data, the product of the predicted unit weight and the quantity to obtain a predicted category weight;

[0146] a zero value determination module, configured to determine that the total predicted second candidate weight of the medicine in the first order data is 0 if any of the predicted category weights is 0;

[0147] The predicted category weight summing module is used to add up all the predicted category weights if all the predicted category weights are not 0, so as to obtain the second candidate total weight predicted for the medicine in the first order data.

[0148] In one embodiment of the present invention, the target total weight fusion module 205 includes:

[0149] a weight configuration module, configured to configure a first weight for the first candidate total weight and a second weight for the second candidate total weight;

[0150] a first weighted total weight calculation module, configured to calculate the product of the first candidate total weight and the first weight to obtain a first weighted total weight;

[0151] a second weighted total weight calculation module, configured to calculate the product of the second candidate total weight and the second weight to obtain a second weighted total weight;

[0152] The target total weight calculation module is used to calculate the sum of the first weighted total weight and the second weighted total weight as the target total weight.

[0153] In one embodiment of the present invention, the weight configuration module includes:

[0154] a first assignment module, configured to, if the first candidate total weight is 0 and the second candidate total weight is not 0, assign 0 to the first candidate total weight and assign 1 to the second candidate total weight as a first weight;

[0155] a second assignment module, configured to, if the first candidate total weight is not 0 and the second candidate total weight is not 0, assign a first value to the first candidate total weight as a first weight and assign a second value to the second candidate total weight as a second weight, wherein the first value is greater than the second value;

[0156] The third assignment module is used to configure 1 as the first weight for the first candidate total weight and 0 as the second weight for the second candidate total weight if the first candidate total weight is not 0 and the first candidate total weight is 0.

[0157] In one embodiment of the present invention, the weight deviation calculation module 206 includes:

[0158] a weight difference calculation module, configured to take an absolute value of a difference between the target total weight and the real-time total weight as the weight difference;

[0159] a ratio calculation module, configured to calculate a ratio between the weight difference and the real-time total weight, and obtain a degree of deviation of the target total weight from the real-time total weight as a weight deviation;

[0160] The order data verification module 207 includes:

[0161] a non-conformity determination module, configured to determine that the sorted medicines do not conform to the first order data if the weight deviation is greater than a preset threshold;

[0162] The compliance determination module is used to determine whether the sorted medicines comply with the first order data if the weight deviation is less than or equal to a preset threshold.

[0163] In one embodiment of the present invention, it further comprises:

[0164] an order quantity query module, configured to query the quantity of medicines of each category in the first order data if the first candidate total weight is 0 and the first candidate total weight is 0;

[0165] An inventory unit weight query module, used to query the inventory unit weight of each category of medicine recorded in the first order data;

[0166] an inventory category weight calculation module, configured to calculate, for each category of medicine in the first order data, the product of the inventory unit weight and the quantity to obtain the inventory category weight;

[0167] The inventory category weight summing module is used to add up the weights of all the inventory categories to obtain a target total weight predicted for the medicines in the first order data.

[0168] The drug order verification device provided in the embodiment of the present invention can execute the drug order verification method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the drug order verification method.

[0169] Example 3

[0170] Figure 3 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0171] like Figure 3 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0172] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0173] Processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any other suitable processor, controller, microcontroller, etc. Processor 11 executes the various methods and processes described above, such as the method for verifying a medication order.

[0174] In some embodiments, the method for verifying a drug order may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for verifying a drug order described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the method for verifying a drug order in any other appropriate manner (e.g., by means of firmware).

[0175] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0176] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0177] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0178] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0179] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0180] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0181] Example 4

[0182] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the drug order verification method provided by any embodiment of the present invention.

[0183] The computer program product may be implemented by writing computer program code for performing the operations of the present invention in one or more programming languages, or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0184] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0185] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for verifying a medicine order, characterized in that: include: In the process of sorting the medicines according to the first order data, receiving a real-time total weight measured for the sorted medicines; Query the second and third order data of historical completed transactions. The drugs in the second order data are the same as those in the first order data in terms of category and quantity. The drug quantity in the third order data is one and is of a single category of drugs in the first order data. predicting a first candidate total weight of the medicine in the first order data based on the second order data; predicting a second candidate total weight for the medicine in the first order data based on the third order data; fusing the first candidate total weight and the second candidate total weight into a target total weight; Calculating the degree to which the target total weight deviates from the real-time total weight as a weight deviation; The sorted medicines are checked to see whether they comply with the first order data based on the weight deviation.

2. The method according to claim 1, characterized in that The query of the second order data and the third order data of the historical completed transactions includes: querying the first identification information, the first manufacturer information, the first production batch number, and the first approval number of each category of drugs in the first order data; Query the second identification information, the second manufacturer information, the second production batch number, and the second approval number of each category of drugs in the historical order data of completed transactions; If the first identification information is the same as the second identification information, the first manufacturer information is the same as the second manufacturer information, the first production batch number is the same as the second production batch number, and the first approval number is the same as the second approval number, then it is determined that the category of the drug in the first order data is the same as the category of the drug in the historical order data; Counting the quantity of medicines of each category in the historical order data; If the medicine in the historical order data is the same as the medicine in the first order data in terms of category and quantity, then the historical order data is determined to be the second order data; If the historical order data contains a single category and a quantity of one drug, and the drug is the same category as any drug in the first order data, then the historical order data is determined to be the third order data.

3. The method according to claim 1, characterized in that The predicting a first candidate total weight of the medicine in the first order data based on the second order data includes: Querying the historical total weight of the medicines that have been sorted in the past, which is recorded in each of the second order data; Calculating an average of all the historical total weights as a first candidate total weight predicted for the medicine of the first order data; The predicting a second candidate total weight of the medicine in the first order data based on the third order data includes: Querying the quantity of each category of medicines in the first order data; querying the historical unit weights of the drugs that have been sorted in the past, which are recorded in the third order data; For drugs of the same category, the largest N historical monomer weights and / or the smallest M historical monomer weights are deleted; If the deletion is completed, for drugs of the same category, the average value of all the historical monomer weights is calculated to obtain the predicted monomer weight; For each category of medicine in the first order data, calculating the product of the predicted unit weight and the quantity to obtain a predicted category weight; If any of the predicted category weights is 0, determining that the second candidate total weight predicted for the medicine in the first order data is 0; If all the predicted category weights are not 0, then all the predicted category weights are added together to obtain a second candidate total weight predicted for the medicines in the first order data.

4. The method according to any one of claims 1 to 3, characterized in that The fusing the first candidate total weight and the second candidate total weight into a target total weight includes: respectively assigning a first weight to the first candidate total weight and a second weight to the second candidate total weight; Calculating the product of the first candidate total weight and the first weight to obtain a first weighted total weight; Calculating the product of the second candidate total weight and the second weight to obtain a second weighted total weight; The sum of the first weighted total weight and the second weighted total weight is calculated as the target total weight.

5. The method according to claim 4, characterized in that The configuring a first weight for the first candidate total weight and configuring a second weight for the second candidate total weight respectively includes: If the total weight of the first candidate is 0 and the total weight of the second candidate is not 0, then the first candidate total weight is assigned a first weight of 0 and the second candidate total weight is assigned a second weight of 1; If the first candidate total weight is not 0 and the second candidate total weight is not 0, assigning a first value as a first weight to the first candidate total weight and a second value as a second weight to the second candidate total weight, wherein the first value is greater than the second value; If the total weight of the first candidate is not 0 and the total weight of the second candidate is 0, the first candidate total weight is configured as 1 and the second candidate total weight is configured as 0.

6. The method according to any one of claims 1 to 3 and 5, characterized in that The calculating the degree to which the target total weight deviates from the real-time total weight as the weight deviation includes: taking an absolute value of the difference between the target total weight and the real-time total weight as the weight difference; Calculating a ratio between the weight difference and the real-time total weight to obtain a degree by which the target total weight deviates from the real-time total weight as a weight deviation; Verifying whether the sorted medicines comply with the first order data based on the weight deviation includes: If the weight deviation is greater than a preset threshold, it is determined that the sorted medicine does not comply with the first order data; If the weight deviation is less than or equal to a preset threshold, it is determined that the sorted medicines comply with the first order data.

7. The method according to any one of claims 1 to 3 and 5, characterized in that Also includes: If the first candidate total weight is 0 and the second candidate total weight is 0, query the quantity of each category of medicines in the first order data; Query the inventory unit weight of each category of medicine recorded in the first order data; For each category of medicine in the first order data, calculate the product of the inventory unit weight and the quantity to obtain the inventory category weight; The weights of all the inventory categories are added together to obtain the target total weight predicted for the medicines in the first order data.

8. A device for verifying a medicine order, characterized in that: include: A real-time total weight measurement module, configured to receive a real-time total weight of the sorted medicines during the process of sorting the medicines according to the first order data; An order data query module, configured to query second and third order data of historically completed transactions, wherein the drugs in the second order data are the same in category and quantity as the drugs in the first order data, and the quantity of the drugs in the third order data is one and is of a single category of the drugs in the first order data; a first candidate total weight prediction module, configured to predict a first candidate total weight for the medicine in the first order data based on the second order data; a second candidate total weight prediction module, configured to predict a second candidate total weight for the medicine in the first order data based on the third order data; a target total weight fusion module, configured to fuse the first candidate total weight and the second candidate total weight into a target total weight; a weight deviation calculation module, configured to calculate the degree to which the target total weight deviates from the real-time total weight as a weight deviation; An order data verification module is used to verify whether the sorted medicines comply with the first order data based on the weight deviation.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the method for verifying a medicine order according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and the computer program is used to enable a processor to implement the method for verifying a drug order according to any one of claims 1 to 7 when executed.

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