CN114022251A - Order management system based on cloud computing - Google Patents

Order management system based on cloud computing Download PDF

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CN114022251A
CN114022251A CN202111324549.2A CN202111324549A CN114022251A CN 114022251 A CN114022251 A CN 114022251A CN 202111324549 A CN202111324549 A CN 202111324549A CN 114022251 A CN114022251 A CN 114022251A
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贾信明
林昱洲
杨宏
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Hua Analysis Technology Shanghai Co ltd
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Abstract

The invention provides an order management system based on cloud computing, which comprises a cloud computing module and an order analysis module, wherein the cloud computing module is used for receiving order information; the cloud computing module is used for storing electronic orders of users; the order analysis module is used for screening the electronic orders to obtain the electronic orders meeting preset screening conditions, and is used for obtaining portrait information of a user based on the electronic orders meeting the preset screening conditions: respectively calculating the similarity between the screened electronic orders and each preset user label, and taking the user label with the similarity larger than a preset similarity threshold value as an portrait label of the user; all of the portrait tags constitute portrait information for the user. By screening the electronic orders and then calculating the portrait information of the user, the portrait generation speed of the user is improved. The portrait information of the user is obtained, so that accurate advertisement pushing can be conveniently carried out on the user according to the portrait information subsequently, and the value contained in the electronic order is mined.

Description

Order management system based on cloud computing
Technical Field
The invention relates to the field of order management systems, in particular to an order management system based on cloud computing.
Background
Order management is a part of an electronic commerce process, and the progress and completion condition of an order are monitored by managing the order issued by a client, so that the operation efficiency of the electronic commerce is improved, and the market competitiveness of an e-commerce enterprise is improved.
The existing order management system generally records information of each link of an order in a transaction process, but the information is lack of a process of analyzing the information, so that the value of the information cannot be mined.
Disclosure of Invention
In view of the above problems, the present invention provides an order management system based on cloud computing, comprising a cloud computing module and an order analysis module;
the cloud computing module is used for storing electronic orders of users;
the order analysis module is used for screening the electronic orders to obtain the electronic orders meeting preset screening conditions, and is used for obtaining portrait information of a user based on the electronic orders meeting the preset screening conditions:
respectively calculating the similarity between the screened electronic orders and each preset user label, and taking the user label with the similarity larger than a preset similarity threshold value as an portrait label of the user;
all of the portrait tags constitute portrait information for the user.
Preferably, the order management system based on cloud computing further comprises an electronic order acquisition module;
the electronic order acquisition module is used for acquiring purchase information input by a user from the e-commerce user terminal and generating an electronic order based on the purchase information;
the electronic order obtaining module is also used for sending the electronic order to the cloud computing module.
Preferably, the order management system based on cloud computing further comprises an electronic order management module;
the electronic order management module is used for managing the electronic orders stored by the cloud computing module.
Preferably, the managing the electronic orders stored by the cloud computing module includes:
modifying the electronic order, deleting the electronic order and inquiring the electronic order.
Preferably, the electronic order comprises ex-warehouse data and logistics data, wherein the ex-warehouse data comprises a warehouse number, ex-warehouse time and ex-warehouse dealers; the logistics data includes a site number, a time to arrive at the site, a time to leave the site, and dispenser information.
Preferably, the order management system based on cloud computing further comprises an electronic order updating module;
the electronic order updating module comprises a warehouse-out information updating unit and a logistics information updating unit;
the ex-warehouse information updating unit is used for updating ex-warehouse data of the electronic orders stored in the cloud computing module;
the logistics information updating unit is used for updating logistics data of the electronic orders stored in the cloud computing module.
Preferably, the electronic order comprises a plurality of first data items, and each first data item corresponds to a first data attribute value;
the user tag includes a plurality of second data items, each second data item corresponding to a data threshold.
Preferably, the calculating the similarity between the electronic order and each preset user tag includes:
the similarity is calculated by the following formula:
Figure BDA0003346540600000021
wherein simdx represents the similarity between the electronic order and the user tag, nft represents the total number of second data items contained in the user tag, and iru represents the set of the second data items contained in the user tag and the same type of data items in the first data items contained in the electronic order; valu(s) represents the data attribute value corresponding to the data item s contained in the iru in the electronic order, and valu (s, thre) represents the data threshold value corresponding to the data item s contained in the iru in the user tag, if valu(s) meets the numerical requirement on valu (s, thre), jud [ valu(s), valu (s, thre) ] is 1, otherwise jud [ valu(s), valu (s, thre) ] is 0.
Preferably, whether valu(s) satisfies the numerical requirement for valu (s, thre) is determined as follows:
if the value requirement of valu (s, thre) is that the data attribute value is greater than the data threshold and valu(s) > valu (s, thre), it means that valu(s) satisfies the value requirement of valu (s, thre), otherwise, it means that valu(s) does not satisfy the value requirement of valu (s, thre);
if the value requirement of valu (s, thre) indicates that the data attribute value is equal to the data threshold value, and valu(s) equals to valu (s, thre), it indicates that valu(s) satisfies the value requirement of valu (s, thre), otherwise, it indicates that valu(s) does not satisfy the value requirement of valu (s, thre);
if the value requirement of valu (s, thre) is that the data attribute value is smaller than the data threshold, and valu(s) < valu (s, thre), it means that valu(s) satisfies the value requirement of valu (s, thre), otherwise, it means that valu(s) does not satisfy the value requirement of valu (s, thre).
In the invention, the portrait information of the user is calculated after the electronic orders are screened, which is beneficial to avoiding invalid electronic orders from participating in the generation process of the portrait of the user and improving the generation speed of the portrait of the user. Meanwhile, the electronic order is obtained from the cloud computing module, and the portrait information of the user is obtained according to the electronic order, so that accurate advertisement pushing is conveniently carried out on the user according to the portrait information subsequently, and the value contained in the electronic order is mined.
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The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
Fig. 1 is a diagram of an exemplary embodiment of an order management system based on cloud computing according to the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As shown in fig. 1, in an embodiment, the invention provides an order management system based on cloud computing, which includes a cloud computing module and an order analysis module;
the cloud computing module is used for storing electronic orders of users;
the order analysis module is used for screening the electronic orders to obtain the electronic orders meeting preset screening conditions, and is used for obtaining portrait information of a user based on the electronic orders meeting the preset screening conditions:
respectively calculating the similarity between the screened electronic orders and each preset user label, and taking the user label with the similarity larger than a preset similarity threshold value as an portrait label of the user;
all of the portrait tags constitute portrait information for the user.
In the invention, the portrait information of the user is calculated after the electronic orders are screened, which is beneficial to avoiding invalid electronic orders from participating in the generation process of the portrait of the user and improving the generation speed of the portrait of the user. Meanwhile, the electronic order is obtained from the cloud computing module, and the portrait information of the user is obtained according to the electronic order, so that accurate advertisement pushing is conveniently carried out on the user according to the portrait information subsequently, and the value contained in the electronic order is mined.
Preferably, the screening the electronic order includes:
judging whether the electronic order meets a first condition, if so, judging whether the electronic order meets a second condition, and if not, indicating that the electronic order does not meet a preset screening condition;
if the electronic order meets the second condition, the electronic order meets the preset screening condition; if the electronic order does not meet the second condition, indicating that the electronic order does not meet the preset screening condition;
the first condition includes:
the generation date of the electronic order is later than the preset anchor point date;
the second condition includes:
the electronic order is a normal order.
Specifically, for example, if the generation date of the electronic order is 2021 year 10 month 1 day, but the anchor date is 2021 year 9 month 1 day, it indicates that the electronic order does not satisfy the first condition. In addition, if the electronic order is in the state that the user signs in and the refund item does not occur, the electronic order is a normal order, otherwise, the electronic order does not belong to the normal order.
The anchor point date is set mainly to avoid that an electronic order far away from the image information acquisition date participates in the processing process of the image information, because the behavior habit of the user is changed, for example, the frequency of purchasing low-price goods in the previous order of the user is high, but the frequency of purchasing high-price goods in the latest order is high, which indicates that the consumption capability of the user is changed. And advertisement information indicating that a high-priced commodity should be pushed to the user.
Preferably, the order management system based on cloud computing further comprises an electronic order acquisition module;
the electronic order acquisition module is used for acquiring purchase information input by a user from the e-commerce user terminal and generating an electronic order based on the purchase information;
the electronic order obtaining module is also used for sending the electronic order to the cloud computing module.
Specifically, the e-commerce user terminal comprises a webpage end and an app end. The purchase information input by the user includes the name of the goods selected by the user, the purchase quantity, the receiving address, the contact phone, the remark information, the user account ID and the like.
Preferably, the generating an electronic order based on the purchase information includes:
acquiring the time of receiving the purchase information;
acquiring an account ID of a user;
inputting the time and the account ID into a preset order number generation algorithm to generate an order number;
and adding the order number into the purchase information to generate an electronic order.
Preferably, the order management system based on cloud computing further comprises an electronic order management module;
the electronic order management module is used for managing the electronic orders stored by the cloud computing module.
Preferably, the electronic order management module comprises an identity authentication unit and an order management unit;
the identity authentication unit is used for verifying the identity of the staff using the order management unit in a face recognition mode;
the order management unit is used for managing the electronic orders stored by the cloud computing module through the identity-verified staff.
In the above embodiment, the authentication method without using the account password is mainly easy to cause the risk of data leakage of the order management system due to leakage of the account password. Therefore, the mode of using the face recognition is safer.
Preferably, the verifying the identity of the employee using the order management unit by using a face recognition method includes:
acquiring a face image of the employee;
acquiring first feature information contained in the face image;
and judging whether the employee passes the identity authentication or not based on the first characteristic information.
Preferably, the determining whether the employee passes the authentication based on the first feature information includes:
and matching the first characteristic information with second characteristic information of the employee with the use authority of the order management unit, which is prestored in the authority server, wherein if the matching is successful, the employee passes the identity authentication.
The second feature information is feature information included in a face image of an employee having the use authority of the order management unit.
Preferably, the acquiring first feature information included in the face image includes:
preprocessing the face image to obtain a preprocessed image;
converting the preprocessed image into a grayscale image;
carrying out noise reduction processing on the gray level image to obtain a noise reduction image;
detecting edge pixel points of the gray level image to obtain an edge pixel point image;
fusing the noise reduction image and the edge pixel point image to obtain a fused image;
extracting a region of interest from the fused image to obtain a target image;
and acquiring first characteristic information contained in the target image by using a preset image characteristic extraction algorithm.
In the above embodiment, by acquiring the fusion image, edge detail information in the noise-reduced image can be improved, and the contour of the object can be highlighted, so that the accuracy of the obtained first feature information can be improved.
Preferably, the preprocessing the face image to obtain a preprocessed image includes:
converting the face image into a Lab color space, and acquiring a brightness component image L of the face image in the Lab color space;
marking the pixel points which meet the following limiting conditions in the brightness component image L as pixel points to be processed:
Figure BDA0003346540600000051
wherein, L (n) represents the pixel value, mid, of the pixel point n to be processed in the brightness component image LLRepresenting the intermediate value of the pixel point in the brightness component image L, nei (n) representing the set of the pixel points in 8 neighborhoods of the pixel point n to be processed in the brightness component image L, L (m) representing the pixel value of the pixel point m in nei (n), thre1And thre2Respectively representing a preset first judgment threshold and a preset second judgment threshold, thre1∈[10,20]，thre2∈[15,25]，
The pixel value adjustment processing is carried out on the pixel point to be processed by using the following formula:
Figure BDA0003346540600000061
wherein, afl (n) represents the pixel value of the pixel point n to be processed after the pixel value adjustment processing is performed on the pixel point n to be processed in the brightness component image L, aveL represents the standard deviation of the pixel value of the pixel point in the brightness component image L, δ represents a preset adjustment coefficient, and δ belongs to (0.1, 0.15);
adjusting pixel values of all to-be-processed pixel points in the brightness component image L to obtain an adjusted brightness component image afL;
afL is converted back to the RGB color space to obtain a pre-processed image.
In the embodiment, the pixels with too high brightness in the face image can be subjected to brightness adjustment processing by converting into the Lab color space for preprocessing, so that the accuracy of extracting subsequent characteristic information is favorably prevented from being influenced by image overexposure. In the Lab color space, because L is a luminance component, the determination of the pixel point to be processed is more accurate than that of the pixel point directly processed in the RGB color space, because the luminance of the pixel point is affected by 3 components in the RGB color space, and only the luminance component in the Lab color space, the result of the preprocessing is more accurate.
Preferably, the performing noise reduction processing on the grayscale image to obtain a noise-reduced image includes:
performing wavelet decomposition processing on the denoised image to obtain a wavelet high-frequency coefficient phi and a wavelet low-frequency coefficient theta;
the wavelet high-frequency coefficient phi is processed as follows:
if | Φ | > is more than or equal to smwtr, denoising the wavelet high-frequency coefficient Φ by using the following formula:
Figure BDA0003346540600000062
if | Φ | is less than smwtr, denoising the wavelet high-frequency coefficient Φ by using the following formula:
Figure BDA0003346540600000063
in the formula, a phi represents a wavelet high-frequency coefficient obtained after denoising processing is carried out on the wavelet high-frequency coefficient phi; sgn represents a sign function, smwtr represents a preset comparison threshold, tr represents a preset smoothing coefficient, and tr belongs to (0.11, 0.31);
performing wavelet reconstruction on alpha phi and theta to obtain an intermediate image;
calculating difference parameters between pixel points in the intermediate image and corresponding pixel points in the gray image:
Figure BDA0003346540600000071
mdig (c) and gry (c) respectively represent pixel values of a pixel point c in the intermediate image and the gray image, sumU represents a set of pixel points in the gray image, mdig(s) and gry(s) respectively represent pixel values of a pixel point s in sumU in the intermediate image and the gray image; cvidx (c) a disparity parameter representing pixel c;
if the cvidx (c) is greater than the preset difference threshold, the pixel point c is represented as a noise pixel point;
and performing noise reduction processing on all noise pixel points in the gray level image by using a non-local mean noise reduction algorithm to obtain a noise reduction image.
In the above embodiment, the wavelet denoising is performed to obtain the intermediate image, and then the noise pixel point is determined according to the intermediate image, so that the determination of the noise pixel point is more accurate compared with the conventional method for determining the noise pixel point based on the neighborhood window. The existing judgment mode based on the neighborhood window is easy to identify edge pixel points as noise points, because the pixel values and the gradient amplitudes of the edge pixel points are also the extreme values of all the pixel points in the neighborhood, and the noise pixel points are also the characteristics. However, in the invention, noise reduction processing is performed in the medium and small wave domains, so that the edge information of the image can be retained, and the effective removal of the image noise can be realized, and if the intermediate image is obtained by using Gaussian filtering, the pixel points in the intermediate image are easily over-blurred and are mistakenly judged as noise pixel points, so that the judgment of the noise pixel points is not accurate enough. The noise reduction processing is performed on the noise pixel points by using the non-local mean noise reduction algorithm, so that the processing on the noise pixel points can be realized while the edge information of the image is kept as much as possible.
Preferably, the performing edge pixel point detection on the gray image to obtain an edge pixel point image includes:
using a Prewitt operator to detect edge pixel points of the gray level image to obtain edge pixel points;
storing all the edge pixel points into a set S1;
deleting the noise pixel points in the set S1 to obtain a set S2;
and keeping the pixel values of the pixel points in the S2 unchanged in the gray-scale image, and setting the pixel values of the other pixel points to be 0 to obtain an edge pixel point image.
Preferably, the fusing the noise-reduced image and the edge pixel point image to obtain a fused image includes:
and adjusting and calculating the pixel value of a pixel point in the fused image by using the following formula:
mixph(q)＝w1×lownoiph(q)+w2×nbl(q)
wherein, mixph represents the fusion image, mixph (q) represents the pixel value of the pixel point q in the fusion image, lowoilph (q) and nbl (q) represent the pixel value of the pixel point q in the noise-reduced image and the edge pixel point image respectively, w1And w2Representing preset weight parameters.
In the embodiment, because the noise reduction processing is not performed when the edge pixel points are obtained from the gray-scale image, part of noise points are also identified as the edge pixel points, but the identification of the noise pixel points is realized in the process of obtaining the noise-reduced image, so that the noise pixel points which are wrongly identified as the edge pixel points are removed by utilizing the characteristic, accurate edge pixel points are left, and then the edge information in the noise-reduced pixel points is further highlighted in an image fusion mode, so that the detail information in the subsequent feature-extracted fusion image is improved.
Preferably, the extracting of the region of interest from the fused image to obtain the target image includes:
obtaining foreground pixel points in the fusion image by using an image segmentation algorithm;
and forming a target image by all the foreground pixel points.
In the above embodiment, the region of interest is a foreground region, and thus, may be obtained by an image segmentation algorithm.
Preferably, the managing the electronic orders stored by the cloud computing module includes:
modifying the electronic order, deleting the electronic order and inquiring the electronic order.
Preferably, the electronic order comprises ex-warehouse data and logistics data, wherein the ex-warehouse data comprises a warehouse number, ex-warehouse time and ex-warehouse dealers; the logistics data includes a site number, a time to arrive at the site, a time to leave the site, and dispenser information.
Preferably, the order management system based on cloud computing further comprises an electronic order updating module;
the electronic order updating module comprises a warehouse-out information updating unit and a logistics information updating unit;
the ex-warehouse information updating unit is used for updating ex-warehouse data of the electronic orders stored in the cloud computing module;
the logistics information updating unit is used for updating logistics data of the electronic orders stored in the cloud computing module.
Preferably, the electronic order comprises a plurality of first data items, and each first data item corresponds to a first data attribute value;
the user tag includes a plurality of second data items, each second data item corresponding to a data threshold.
Preferably, the calculating the similarity between the electronic order and each preset user tag includes:
the similarity is calculated by the following formula:
Figure BDA0003346540600000081
wherein simdx represents the similarity between the electronic order and the user tag, nft represents the total number of second data items contained in the user tag, and iru represents the set of the second data items contained in the user tag and the same type of data items in the first data items contained in the electronic order; valu(s) represents the data attribute value corresponding to the data item s contained in the iru in the electronic order, and valu (s, thre) represents the data threshold value corresponding to the data item s contained in the iru in the user tag, if valu(s) meets the numerical requirement on valu (s, thre), jud [ valu(s), valu (s, thre) ] is 1, otherwise jud [ valu(s), valu (s, thre) ] is 0.
Preferably, whether valu(s) satisfies the numerical requirement for valu (s, thre) is determined as follows:
if the value requirement of valu (s, thre) is that the data attribute value is greater than the data threshold and valu(s) > valu (s, thre), it means that valu(s) satisfies the value requirement of valu (s, thre), otherwise, it means that valu(s) does not satisfy the value requirement of valu (s, thre);
if the value requirement of valu (s, thre) indicates that the data attribute value is equal to the data threshold value, and valu(s) equals to valu (s, thre), it indicates that valu(s) satisfies the value requirement of valu (s, thre), otherwise, it indicates that valu(s) does not satisfy the value requirement of valu (s, thre);
if the value requirement of valu (s, thre) is that the data attribute value is smaller than the data threshold, and valu(s) < valu (s, thre), it means that valu(s) satisfies the value requirement of valu (s, thre), otherwise, it means that valu(s) does not satisfy the value requirement of valu (s, thre).
Preferably, the data items include an article ID, a purchase amount, an article unit price, an article total price, and an article browsing time.
In the above embodiment, the first data item contained in the electronic order may be stored as set U1, and the second data item contained in the user tag may be stored as set U2, with itu being the intersection of U1 and U2. U2 is a subset of U1.
Specifically, the number of data items included in the electronic order is the largest, while the data items included in different types of user tags are different, and the data thresholds and the numerical requirements corresponding to the data items in the different types of user tags are also different.
For example, when the user tag is a person with money, the included data items are the unit price of the article and the article browsing time; the threshold value of the unit price of the commodity is t1, and the threshold value of the commodity browsing time is t 2; the value of the unit price of the item is required to be more than t1, and the value of the browsing time of the item is required to be less than the threshold value t2, so that when the cushion order meets the requirements, the label of the "money" is used as the portrait label of the user.
While embodiments of the invention have been shown and described, it will be understood by those skilled in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
It should be noted that the functional units/modules in the embodiments of the present invention may be integrated into one processing unit/module
In a block, each unit/module may exist alone physically, or two or more units/modules may be integrated into one unit/module. The integrated units/modules may be implemented in the form of hardware, or may be implemented in the form of software functional units/modules.
From the above description of embodiments, it is clear for a person skilled in the art that the embodiments described herein can be implemented in hardware, software, firmware, middleware, code or any appropriate combination thereof. For a hardware implementation, a processor may be implemented in one or more of the following units: an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a processor, a controller, a microcontroller, a microprocessor, other electronic units designed to perform the functions described herein, or a combination thereof. For a software implementation, some or all of the procedures of an embodiment may be performed by a computer program instructing associated hardware.
In practice, the program may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a computer. Computer-readable media can include, but is not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.

Claims (9)

1. The order management system based on cloud computing is characterized by comprising a cloud computing module and an order analysis module;
the cloud computing module is used for storing electronic orders of users;
the order analysis module is used for screening the electronic orders to obtain the electronic orders meeting preset screening conditions, and is used for obtaining portrait information of a user based on the electronic orders meeting the preset screening conditions:
respectively calculating the similarity between the screened electronic orders and each preset user label, and taking the user label with the similarity larger than a preset similarity threshold value as an portrait label of the user;
all of the portrait tags constitute portrait information for the user.
2. The cloud-computing-based order management system of claim 1, further comprising an electronic order acquisition module;
the electronic order acquisition module is used for acquiring purchase information input by a user from the e-commerce user terminal and generating an electronic order based on the purchase information;
the electronic order obtaining module is also used for sending the electronic order to the cloud computing module.
3. The cloud-computing-based order management system of claim 1, further comprising an electronic order management module;
the electronic order management module is used for managing the electronic orders stored by the cloud computing module.
4. The cloud-computing-based order management system according to claim 3, wherein the managing of the electronic orders stored by the cloud computing module comprises:
modifying the electronic order, deleting the electronic order and inquiring the electronic order.
5. The cloud-computing-based order management system according to claim 1, wherein the electronic order comprises warehouse-out data and logistics data, and the warehouse-out data comprises a warehouse number, warehouse-out time and warehouse-out manager; the logistics data includes a site number, a time to arrive at the site, a time to leave the site, and dispenser information.
6. The cloud-computing-based order management system of claim 5, further comprising an electronic order update module;
the electronic order updating module comprises a warehouse-out information updating unit and a logistics information updating unit;
the ex-warehouse information updating unit is used for updating ex-warehouse data of the electronic orders stored in the cloud computing module;
the logistics information updating unit is used for updating logistics data of the electronic orders stored in the cloud computing module.
7. The cloud-computing-based order management system of claim 1, wherein the electronic order comprises a plurality of first data items, each first data item corresponding to a first data attribute value;
the user tag includes a plurality of second data items, each second data item corresponding to a data threshold.
8. The cloud-computing-based order management system according to claim 7, wherein the computing of the similarity between the electronic order and each preset user tag comprises:
the similarity is calculated by the following formula:
Figure FDA0003346540590000021
wherein simdx represents the similarity between the electronic order and the user tag, nft represents the total number of second data items contained in the user tag, and iru represents the set of the second data items contained in the user tag and the same type of data items in the first data items contained in the electronic order; valu(s) represents the data attribute value corresponding to the data item s contained in the iru in the electronic order, and valu (s, thre) represents the data threshold value corresponding to the data item s contained in the iru in the user tag, if valu(s) meets the numerical requirement on valu (s, thre), jud [ valu(s), valu (s, thre) ] is 1, otherwise jud [ valu(s), valu (s, thre) ] is 0.
9. The cloud-based order management system of claim 8, wherein whether valu(s) meets the numerical requirement of valu (s, thre) is determined by:
if the value requirement of valu (s, thre) is that the data attribute value is greater than the data threshold and valu(s) > valu (s, thre), it means that valu(s) satisfies the value requirement of valu (s, thre), otherwise, it means that valu(s) does not satisfy the value requirement of valu (s, thre);
if the value requirement of valu (s, thre) indicates that the data attribute value is equal to the data threshold value, and valu(s) equals to valu (s, thre), it indicates that valu(s) satisfies the value requirement of valu (s, thre), otherwise, it indicates that valu(s) does not satisfy the value requirement of valu (s, thre);
if the value requirement of valu (s, thre) is that the data attribute value is smaller than the data threshold, and valu(s) < valu (s, thre), it means that valu(s) satisfies the value requirement of valu (s, thre), otherwise, it means that valu(s) does not satisfy the value requirement of valu (s, thre).
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115879855A (en) * 2022-12-12 2023-03-31 泉州市融兴信息科技有限公司 Order data analysis system and method based on ERP management system
CN116205555A (en) * 2023-04-28 2023-06-02 深圳市丰泉科技有限公司 Logistics information visual management system and method based on big data

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115879855A (en) * 2022-12-12 2023-03-31 泉州市融兴信息科技有限公司 Order data analysis system and method based on ERP management system
CN116205555A (en) * 2023-04-28 2023-06-02 深圳市丰泉科技有限公司 Logistics information visual management system and method based on big data
CN116205555B (en) * 2023-04-28 2023-09-05 宁夏金丝路大数据科技有限责任公司 Logistics information visual management system and method based on big data

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