A commodity procurement management system

Abstract

The present invention belongs to the field of management systems and discloses a commodity procurement management system, which includes an office computer, a smart phone, and a cloud server; the smart phone is used to obtain location information and the user's biometric information and transmit them to the cloud server; the cloud server is used to generate a device identification code according to the location information and the biometric information and send the device identification code to the smart phone; the office computer is used to obtain the user's login instruction, and after receiving the login instruction, send the login instruction to the smart phone; the smart phone is used to send the device identification code to the office computer after receiving the login instruction; the office computer is used to send the device identification code to the cloud server; the cloud server is used to return an identity token to the office computer according to the device identification code sent by the office computer; the office computer is used to communicate with the cloud server through the identity token. The present invention improves the security of the commodity procurement management system.

Description

Technical Field

[0001] The present invention relates to the field of management systems, and particularly to a commodity procurement management system. Background Art

[0002] A commodity procurement management system refers to an information system designed to assist enterprises or organizations in effectively managing their commodity procurement processes. The system aims to simplify and optimize the procurement process, improve procurement efficiency, reduce procurement costs, and ensure the smooth operation of the supply chain.

[0003] The commodity procurement management system generally conducts login verification through the method of account password. However, this verification method has relatively high security risks. When the account password is illegally obtained by other personnel without the right to use, it is easy to cause the illegal login of the commodity procurement management system, resulting in the leakage of procurement management data. An improved direction is to add a camera or a fingerprint collector to the office computer in use and conduct login through face recognition or fingerprint recognition, which can improve the security of the commodity procurement management system to a certain extent. However, since most office computers are desktop computers and do not have devices such as cameras and fingerprint collectors, it is necessary to install corresponding devices additionally, which increases the construction cost of the management system. Summary of the Invention

[0004] The purpose of the present invention is to disclose a commodity procurement management system to solve the technical problem of how to improve the security of the procurement management system at a lower cost.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions:

[0006] The present invention provides a commodity procurement management system, including an office computer, a smart phone, and a cloud server;

[0007] The smart phone is used to obtain the location information and the biometric information of the user, and transmit the location information and the biometric information to the cloud server;

[0008] The cloud server is used to generate a device identification code according to the location information and the biometric information, and send the device identification code to the smart phone;

[0009] The office computer is used to obtain the login instruction of the user, and after receiving the login instruction, send the login instruction to the smart phone;

[0010] The smart phone is used to send the device identification code to the office computer after receiving the login instruction;

[0011] The office computer is used to send the device identification code to the cloud server;

[0012] The cloud server is used to return an identity token to the office computer according to the device identification code sent by the office computer;

[0013] The office computer is used to communicate with the cloud server through the identity token.

[0014] Preferably, the office computer communicates with the smart phone by wireless communication.

[0015] Preferably, a commodity procurement management client is installed in the office computer, and the commodity procurement management client includes an inventory management module;

[0016] The inventory management module includes a setting unit, an inbound / outbound management unit, a prompting unit, and a statistical analysis unit;

[0017] The setting unit is used for the user to set the maximum inventory value and the minimum inventory value of the commodity;

[0018] The inbound / outbound management unit is used for the user to modify the inventory quantity of the commodity when the commodity is out of stock and in stock;

[0019] The prompting unit is used to issue a first prompt when the inventory quantity of the commodity is less than the minimum inventory value, and is used to issue a second prompt when the inventory quantity of the commodity is greater than the maximum inventory value;

[0020] The statistical analysis unit is used to generate procurement suggestions according to the historical sales data of the commodity.

[0021] Preferably, the commodity procurement management client further includes a commodity expiration date monitoring module;

[0022] The commodity expiration date monitoring module includes an input unit and a monitoring unit;

[0023] The input unit is used for the user to input the production date and expiration date of the commodity;

[0024] The monitoring unit is used to monitor the quality of the commodity according to the production date and expiration date.

[0025] Preferably, the commodity procurement management client further includes a communication module;

[0026] The communication module is used to send the maximum inventory value and the minimum inventory value to the cloud server;

[0027] The cloud server is used to store the maximum inventory value and the minimum inventory value.

[0028] Preferably, the location information includes the location where the smart phone is located;

[0029] The biometric information includes the facial image of the user.

[0030] Preferably, generating the device identification code according to the location information and the biometric information includes:

[0031] First step: Determine whether the distance between the location of the smartphone and the preset location is less than the set distance threshold. If not, exit the process of generating the device identification code. If so, proceed to the second step;

[0032] Second step: Determine whether the user has the usage permission based on the user's facial image. If not, exit the process of generating the device identification code. If so, proceed to the third step;

[0033] Third step: Randomly generate a device identification code and store the generated device identification code.

[0034] Preferably, according to the device identification code sent by the office computer, return an identity token to the office computer, including:

[0035] Determine whether the device identification code sent by the office computer is within the valid period. If so, determine whether the device identification code sent by the office computer belongs to one of the device identification codes stored by itself. If so, generate an identity token and send the identity token to the office computer. If not, do not return an identity token to the office computer.

[0036] Preferably, obtain the user's login instruction, including:

[0037] Obtain the click information of the user on the login interface of the commodity procurement management client, and determine whether the user inputs a login instruction according to the click information.

[0038] Preferably, the click information includes the click coordinates when the user uses the mouse to click on the login interface;

[0039] Determine whether the user inputs a login instruction according to the click information, including:

[0040] Determine whether the click coordinates are within the preset coordinate range. If so, it means that the user inputs a login instruction.

[0041] Beneficial effects:

[0042] Compared with the prior art, in the process of login verification of the commodity procurement management system of the present invention, it neither directly logs in and verifies by inputting the account password on the office computer, nor logs in and verifies by additionally installing devices such as cameras or fingerprint collectors on the office computer. Instead, it uses the user's smartphone to perform fast and convenient login verification through the smartphone. This not only makes the login verification process safer than the account password verification method, but also eliminates the need to install additional devices, avoiding increasing the construction cost of the management system. Description of the Drawings

[0043] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0044] Figure 1 It is a schematic diagram of a commodity procurement management system of the present invention.

[0045] Figure 2 It is a schematic diagram of the process of generating a device identification code by the present invention according to location information and biometric information. Detailed implementation manners

[0046] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Usually, the components of the embodiments of the present invention described and shown in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed present invention, but only represents the selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present invention.

[0047] As Figure 1 shown in an embodiment, the present invention provides a commodity procurement management system, including an office computer, a smart phone, and a cloud server;

[0048] The smart phone is used to obtain location information and the biometric information of the user, and transmit the location information and the biometric information to the cloud server;

[0049] The cloud server is used to generate a device identification code according to the location information and the biometric information, and send the device identification code to the smart phone;

[0050] The office computer is used to obtain the login instruction of the user, and after receiving the login instruction, send the login instruction to the smart phone;

[0051] The smart phone is used to send the device identification code to the office computer after receiving the login instruction;

[0052] The office computer is used to send the device identification code to the cloud server;

[0053] The cloud server is used to return an identity token to the office computer according to the device identification code sent by the office computer;

[0054] The office computer is used to communicate with the cloud server through an identity token.

[0055] During the login verification process, the present invention neither directly performs login verification by entering the account password on the office computer, nor performs login verification by installing additional devices such as cameras or fingerprint collectors on the office computer. Instead, it uses the user's smartphone to perform quick and convenient login verification. This not only makes the login verification process safer than the account password verification method but also eliminates the need to install additional devices, avoiding increasing the construction cost of the management system.

[0056] Specifically, the user here refers to the person using the office computer, who can be an inventory manager.

[0057] Preferably, the office computer communicates with the smartphone in a wireless communication manner.

[0058] Specifically, the office computer can communicate with the smartphone through WiFi or Bluetooth.

[0059] Preferably, a commodity procurement management client is installed on the office computer, and the commodity procurement management client includes an inventory management module;

[0060] The inventory management module includes a setting unit, an inbound and outbound management unit, a prompt unit, and a statistical analysis unit;

[0061] The setting unit is used for the user to set the maximum inventory value and the minimum inventory value of the commodity;

[0062] The inbound and outbound management unit is used for the user to modify the inventory quantity of the commodity when the commodity is out of stock and in stock;

[0063] The prompt unit is used to issue a first prompt when the inventory quantity of the commodity is less than the minimum inventory value, and is used to issue a second prompt when the inventory quantity of the commodity is greater than the maximum inventory value;

[0064] The statistical analysis unit is used to generate procurement suggestions based on the historical sales data of the commodity.

[0065] Specifically, setting the maximum inventory value and the minimum inventory value of the commodity includes:

[0066] After successfully logging in to the commodity procurement management client, the user sets the maximum and minimum inventory values of the commodity in the setting unit through the keyboard.

[0067] Specifically, when the commodity is out of stock and in stock, the user modifies the inventory quantity of the commodity, including:

[0068] When the commodity is out of stock or in stock, the user enters the changed quantity of the commodity;

[0069] Modify the inventory of goods according to the quantity of change.

[0070] For example, when goods are shipped out, if 10 pieces of goods are shipped out, the quantity of change is 10. Subtract 10 from the inventory quantity before the goods are shipped out to obtain the modified inventory quantity of the goods.

[0071] Specifically, the first prompt is a prompt for prompting the purchaser to purchase and replenish goods in a timely manner, and the second prompt is a prompt for prompting the salesperson to accelerate sales.

[0072] Specifically, generate a purchase recommendation based on the historical sales data of the goods, including:

[0073] Input the historical sales data of the goods into a pre-trained sales volume prediction model to obtain the predicted sales volume;

[0074] Generate a purchase recommendation based on the predicted sales volume.

[0075] For example, at the end of a quarter, input the historical sales data of the goods into a pre-trained sales volume prediction model to obtain the predicted sales volume for the next quarter;

[0076] Then subtract the inventory quantity from the predicted sales volume of the goods for the next quarter to obtain the purchase quantity, and use the purchase quantity as the purchase recommendation.

[0077] The sales volume prediction model can be a decision tree model, a support vector machine model, XGBoost, etc.

[0078] For example, the random forest model in the decision tree model can determine the final prediction result by constructing multiple decision trees and voting.

[0079] Preferably, the goods procurement management client further includes a goods expiration date monitoring module;

[0080] The goods expiration date monitoring module includes an input unit and a monitoring unit;

[0081] The input unit is used for the user to input the production date and expiration date of the goods;

[0082] The monitoring unit is used to monitor the quality of the goods according to the production date and expiration date.

[0083] Specifically, monitoring the quality of the goods according to the production date and expiration date includes:

[0084] Obtain the current date, and subtract the production date from the current date to obtain the length of the ex-factory time of the goods;

[0085] Judge whether the length of the ex-factory time is less than the expiration date of the goods. If not, when the goods are shipped out, prompt the user and prohibit the shipping out operation.

[0086] Preferably, the commodity procurement management client further includes a communication module;

[0087] The communication module is used to send the maximum inventory value and the minimum inventory value to the cloud server;

[0088] The cloud server is used to store the maximum inventory value and the minimum inventory value.

[0089] Specifically, the prompt unit regularly sends an update request to the cloud server through the communication module;

[0090] After receiving the update request, the cloud server sends the maximum inventory value and the minimum inventory value to the communication module;

[0091] The communication module transmits the maximum inventory value and the minimum inventory value to the prompt unit;

[0092] The prompt unit determines whether the inventory quantity of the commodity is less than the minimum inventory value and whether the inventory quantity of the commodity is greater than the maximum inventory value according to the maximum inventory value and the minimum inventory value received from the communication module.

[0093] Preferably, the location information includes the location where the smart phone is located;

[0094] The biometric information includes the facial image of the user.

[0095] Specifically, the location information can be obtained through the location chip on the smart phone.

[0096] The facial image can be obtained through the front camera of the smart phone.

[0097] Preferably, as Figure 2 shown, generating a device identification code according to the location information and the biometric information includes:

[0098] First step, determine whether the distance between the location where the smart phone is located and the preset location is less than the set distance threshold. If not, exit the process of generating the device identification code. If so, enter the second step;

[0099] Second step, determine whether the user has the usage permission according to the facial image of the user. If not, exit the process of generating the device identification code. If so, enter the third step;

[0100] Third step, randomly generate a device identification code and store the generated device identification code.

[0101] Specifically, randomly generating a device identification code includes:

[0102] Randomly generate a device identification code of a preset length, and determine whether the generated device identification code is repeated with the device identification code that has been stored. If so, regenerate it.

[0103] For example, randomly generate a device identification code with a length of 10 digits.

[0104] In addition, the cloud server also manages the validity period of the generated device identification code. When the existence duration of the device identification code (i.e., the time length from the generation moment to the current moment) is greater than the set time length (such as 1 day), the device identification code is deleted from the database.

[0105] Such a device identification code management method can further improve the security of using the device identification code and reduce the security risk of the device identification code with an overly long generation time being illegally stolen.

[0106] Specifically, the set distance threshold can be 500 meters, 300 meters, etc. Since it is generally located within the city during work, there is a relatively large positioning error compared to outdoor positioning. Therefore, a relatively large distance threshold can be set.

[0107] Preferably, judging whether the user has the usage permission according to the user's facial image includes:

[0108] Obtain the image feature TA of the user's facial image;

[0109] Calculate the similarity between TA and the image features of the facial images of each user with usage permission stored in the cloud server respectively;

[0110] Obtain the maximum similarity among all the similarities;

[0111] Judge whether the maximum similarity is greater than the set similarity comparison value; if so, use the user ID corresponding to the maximum similarity as the finally determined user ID. If not, it means the user does not have the usage permission.

[0112] In the cloud server, the user ID and facial image of each user with usage permission are stored, and the user ID and the facial image are in one-to-one correspondence.

[0113] Preferably, the set similarity comparison value can be 0.7.

[0114] Preferably, obtaining the image feature TA of the user's facial image includes:

[0115] Perform noise reduction calculation on the user's facial image to obtain a first calculation image;

[0116] Perform image segmentation processing on the first noise reduction image to obtain a second calculation image;

[0117] Use an extraction algorithm to obtain the image feature TA of the second calculated image.

[0118] Specifically, the extraction algorithm can be the SIFT algorithm, the ORB algorithm, etc.

[0119] The SIFT algorithm is a classic feature point extraction algorithm that extracts key points and their feature descriptors and has scale, rotation, and illumination invariance.

[0120] The ORB algorithm is a fast algorithm that combines FAST feature point detection and BRIEF (Binary Robust Independent Elementary Features) feature description.

[0121] Specifically, the image features of the facial images of each user with usage permission stored in the cloud server are obtained using the same extraction algorithm as TA.

[0122] Preferably, perform noise reduction calculation on the user's facial image to obtain the first calculated image, including:

[0123] Grayscale the user's facial image to obtain a grayscale image;

[0124] Perform noise reduction on the grayscale image to obtain the first calculated image.

[0125] By performing noise reduction processing, the noise level in the target image extracted by the privilege escalation algorithm can be effectively reduced, which is beneficial to obtaining more accurate image features.

[0126] Preferably, perform noise reduction on the grayscale image to obtain the first calculated image, including:

[0127] Calculate the noise reduction eigenvalue of the grayscale image;

[0128] Starting from 1, continuously number the pixel points in the grayscale image in descending order of the noise reduction eigenvalue. The larger the noise reduction eigenvalue, the larger the number;

[0129] Perform noise reduction based on the following process:

[0130] S1, Initialize the value of K to 1;

[0131] S2, Perform noise reduction on the pixel point numbered K in the grayscale image to obtain the grayscale value of the pixel point numbered K after noise reduction;

[0132] S3, In the grayscale image, modify the grayscale value of the pixel point numbered K to the grayscale value after noise reduction obtained in S2 to obtain image I K ;

[0133] S4. Increment the value of K by 1 and proceed to S5;

[0134] S5. In the image I K-1 perform noise reduction on the pixel point numbered K to obtain the gray value of the pixel point numbered K after noise reduction;

[0135] S6. In the grayscale image, modify the gray value of the pixel point numbered K to the gray value after noise reduction obtained in S5 to obtain the image I K ;

[0136] S7. Determine whether the value of K is equal to the total number of pixel points in the grayscale image. If so, proceed to S8; if not, proceed to S4;

[0137] S8. Take the image I K as the first calculation image.

[0138] Existing noise reduction algorithms generally obtain the noise reduction result by performing noise reduction on each pixel point separately on the grayscale image. This noise reduction method does not effectively utilize the noise reduction results of the pixel points around the pixel point to be noise-reduced, resulting in an inaccurate noise reduction result. The present invention improves the existing noise reduction algorithm. After each noise reduction, the gray value obtained by noise reduction is used to replace the gray value of the pixel point at the corresponding position in the original image. In this way, the noise reduction result of the already noise-reduced pixel point can be effectively utilized, enabling the current noise reduction to be based on the more accurate gray values of the neighboring pixel points, which is conducive to obtaining a more accurate noise reduction result.

[0139] In addition, the present invention also restricts the noise reduction order of the pixel points, so that the pixel points with more reference information in the neighborhood are preferentially noise-reduced, so that the effective noise reduction result can be included in the image I K earlier and to a greater extent, enabling more effective gray value information to be utilized in subsequent noise reduction.

[0140] Preferably, the calculation formula for the noise reduction eigenvalue is:

[0141]

[0142] noschr s represents the noise reduction eigenvalue of the pixel point s, nus represents the set of pixel points in a square window with a side length of 5 centered on s, grayvl j represents the gray value of the pixel point j, avgry represents the comparison value of the gray values of the pixel points in nus, N represents the total number of pixel points in the grayscale image, grayvlm represents the maximum value of the gray values of the pixel points in nus, count srepresents the total number of pixel points in nus that meet the gradient screening rule, and η represents the weighting factor.

[0143] The noise reduction eigenvalue not only takes into account the change in gray values around the pixel point, but also considers the characteristics in terms of gradient. Thus, when the change in pixel points around pixel point s is more drastic and the number of pixel points that meet the gradient screening rule is larger, the noise reduction eigenvalue is larger, indicating that there is more effective information including edge information around pixel point s, and noise reduction processing is performed with higher priority.

[0144] Preferably, the weighting factor is

[0145] Preferably, the process of determining whether a pixel point meets the gradient screening rule includes:

[0146] For pixel point s, obtain the image gradient grd of pixel point s s ;

[0147] Calculate the absolute value of the difference between grd s and the image gradient of each pixel point in the 8-neighborhood of s;

[0148] Obtain the maximum value absma of all the absolute values;

[0149] Determine whether absma is greater than the set image gradient comparison value;

[0150] If so, it means that s meets the gradient screening rule.

[0151] Preferably, the image gradient comparison value is grdm represents the maximum value of the image gradients of pixel points in the grayscale image.

[0152] Preferably, for noise reduction of the pixel point numbered K, obtaining the grayscale value of the pixel point numbered K after noise reduction includes:

[0153] Calculate the grayscale value of the pixel point numbered K after noise reduction using the following formula:

[0154]

[0155] cgray K represents the grayscale value of the pixel point numbered K after noise reduction, nuk represents the set of pixel points in a square window with a side length of 5 centered on the pixel point numbered K, α 1 、α 2 、α 3 respectively represent the first weight, the second weight, and the third weight, wei 1 、wei 2 and wei 3respectively represent the first coefficient, the second coefficient, and the third coefficient, maa represents when j ∈ nuk, |grayvl j -grayvl s | of the maximum value, x K and y K respectively represent the abscissa and ordinate of the pixel point numbered K, x j and y j respectively represent the abscissa and ordinate of the pixel point j, mab represents when j ∈ nuk, of the maximum value, grd j represents the image gradient of the pixel point j.

[0156] In the process of noise reduction, the present invention not only takes into account the difference between the gray values of two pixel points and the difference between their positions, but also takes into account the gradient of the pixel points. When the difference between the gray values of the pixel points is smaller, the difference between their positions is smaller, and the gradient is larger, the influence of the gray value of the pixel point j on the gray value after noise reduction is greater. In this way, noise reduction can be considered from multiple aspects, and a more accurate noise reduction result can be obtained. If only the difference between the gray values and the difference between the positions are considered, then when the pixel point j is located at the edge of the image, the influence of the gray value of the pixel point j on the gray value after noise reduction is too small, which is not conducive to maintaining more effective edge details after noise reduction.

[0157] Preferably, the first weight, the second weight, and the third weight are respectively and

[0158] Preferably, according to the device identification code sent by the office computer, an identity token is returned to the office computer, including:

[0159] Judge whether the device identification code sent by the office computer is within the validity period. If so, judge whether the device identification code sent by the office computer belongs to one of the device identification codes stored in itself. If so, generate an identity token and send the identity token to the office computer. If not, do not return an identity token to the office computer.

[0160] Specifically, judging whether the device identification code sent by the office computer is within the validity period includes:

[0161] Obtain the corresponding generation time according to the identification code, subtract the generation time from the current time to obtain the existence duration of the device identification code. If the existence duration is greater than the set time length, it means that the device identification code is not within the validity period.

[0162] Preferably, generating an identity token includes:

[0163] Encode the user's ID and the user's permission information, and generate an identity token through a signature algorithm.

[0164] For example, the user's ID and the user's permission information can be encoded in the JSON Web Token (JWT) format.

[0165] The signature algorithms include algorithms such as HMAC and ECDSA.

[0166] For example, the identity token can be a token.

[0167] Preferably, perform image segmentation processing on the first noise-reduced image to obtain a second calculation image, including:

[0168] Use an image segmentation algorithm to perform image segmentation processing on the first noise-reduced image to obtain a second calculation image.

[0169] For example, adopt a threshold-based segmentation algorithm to perform image segmentation processing on the first noise-reduced image, and use the pixel points in the first noise-reduced image with gray values greater than the segmentation threshold of the segmentation algorithm as the pixel points in the second calculation image.

[0170] Preferably, obtain the user's login instruction, including:

[0171] Obtain the click information of the user on the login interface of the commodity procurement management client, and judge whether the user inputs a login instruction according to the click information.

[0172] Preferably, the click information includes the click coordinates of the user using the mouse to click on the login interface;

[0173] Judging whether the user inputs a login instruction according to the click information includes:

[0174] Judge whether the click coordinates are within a preset coordinate range. If so, it means that the user inputs a login instruction.

[0175] Preferably, communicate with the cloud server through the identity token, including:

[0176] When sending a request to the cloud server, carry the identity token in the header of the request.

[0177] After receiving the identity token, the office computer stores the identity token. When it is necessary to communicate with the cloud server and send a request to the cloud server, the identity token is included in the header of the request and sent to the cloud server. The cloud server will extract the identity token from the request header and verify it. The verification process includes checking the validity of the identity token, the correctness of the signature, and whether it has expired. If the identity token verification is successful, the cloud server will process the request and return the corresponding data. If the identity token verification fails (such as expiration, invalidity, or forgery), the cloud server will return the corresponding error message, usually the 401 Unauthorized status code.

[0178] Further, after receiving the identity token, the commodity procurement management client stores the identity token. When it is necessary to communicate with the cloud server and send a request to the cloud server, the identity token is included in the header of the request and sent to the cloud server.

[0179] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A commodity procurement management system, characterized in that: This includes office computers, smartphones, and cloud servers; The smartphone is used to obtain location information and the user's biometric information, and transmit the location information and biometric information to the cloud server; The cloud server is used to generate a device identification code based on the location information and biometric information, and send the device identification code to the smartphone; The office computer is used to obtain the user's login instruction, and after receiving the login instruction, sends the login instruction to the smart phone; The smartphone is used to send a device identification code to the office computer after receiving the login instruction; The office computer is used to send the device identification code to the cloud server; The cloud server is used to return an identity token to the office computer based on the device identification code sent by the office computer; The office computer is used to communicate with the cloud server via identity tokens; Location information includes the location of the smartphone; Biometric information includes a user’s facial image; Generate a device identification code based on positioning information and biometric information, including: The first step is to determine whether the distance between the location of the smartphone and the preset location is less than a set distance threshold. If not, then exit the process of generating the device identification code. If yes, then proceed to the second step. The second step is to determine whether the user has the permission to use the device based on the user's facial image. If not, the process of generating the device identification code is exited. If yes, the process proceeds to the third step. The third step is to randomly generate a device identification code and store the generated device identification code; Determine whether the user has permission to use the service based on the user's facial image, including: Obtain image features TA of the user's facial image; Calculate the similarity between the image features of TA and the facial images of each user with usage permission stored in the cloud server; Get the maximum similarity among all similarities; Determine whether the maximum similarity is greater than the set similarity comparison value; if so, the user ID corresponding to the maximum similarity is used as the final user ID; if not, it means that the user does not have the permission to use; Obtaining the image features TA of the user's facial image, including: Performing noise reduction calculation on the user's facial image to obtain a first calculated image; Performing image segmentation processing on the first denoised image to obtain a second calculated image; Using an extraction algorithm to obtain an image feature TA of a second calculated image; Performing noise reduction calculation on the user's facial image to obtain a first calculated image includes: Grayscale the user's facial image to obtain a grayscale image; Denoising the grayscale image to obtain a first calculated image; Denoising the grayscale image to obtain a first calculated image, including: Calculate the denoising eigenvalues ​​of the grayscale image; Starting from 1, the pixels in the grayscale image are numbered consecutively in the order of the noise reduction feature value from high to low. The larger the noise reduction feature value, the larger the number; Noise reduction is performed based on the following process: S1, initialize the value of K to 1; S2, performing noise reduction on a pixel numbered K in the grayscale image to obtain a grayscale value of the pixel numbered K after noise reduction; S3, in the grayscale image, the grayscale value of the pixel numbered K is modified to the grayscale value after noise reduction obtained in S2, and an image I is obtained. K ; S4, add 1 to the value of K and enter S5; S5, in image I K-1 , performing noise reduction on the pixel numbered K to obtain the grayscale value of the pixel numbered K after noise reduction; S6, in the grayscale image, the grayscale value of the pixel numbered K is modified to the grayscale value after noise reduction obtained in S5, and an image I is obtained. K ; S7, determine whether the value of K is equal to the total number of pixels in the grayscale image, if so, proceed to S8, if not, proceed to S4; S8, image I K As the first calculated image.

2. A commodity procurement management system according to claim 1, characterized in that: The office computer communicates with the smartphone using wireless communication.

3. A commodity procurement management system according to claim 1, characterized in that: A commodity purchasing management client is installed in the office computer, and the commodity purchasing management client includes an inventory management module; The inventory management module includes a setting unit, an in-and-out storage management unit, a prompt unit, and a statistical analysis unit; The setting unit is used for users to set the maximum and minimum inventory values ​​of commodities; The in-and-out management unit is used for users to modify the inventory of goods when goods are out-and-in; The prompt unit is used to issue a first prompt when the inventory quantity of the commodity is less than the minimum inventory value, and is used to issue a second prompt when the inventory quantity of the commodity is greater than the maximum inventory value; The statistical analysis unit is used to generate purchase recommendations based on the historical sales data of commodities.

4. A commodity procurement management system according to claim 3, characterized in that: The commodity procurement management client also includes a commodity validity monitoring module; The commodity validity period monitoring module includes an input unit and a monitoring unit; The input unit is used by users to input the production date and expiration date of the product; The monitoring unit is used to monitor the quality of the goods based on the production date and expiration date.

5. A commodity procurement management system according to claim 3, characterized in that: The commodity procurement management client also includes a communication module; The communication module is used to send the maximum inventory value and the minimum inventory value to the cloud server; The cloud server is used to store the maximum and minimum inventory values.

6. A commodity procurement management system according to claim 1, characterized in that: According to the device identification code sent by the office computer, an identity token is returned to the office computer, including: Determine whether the device identification code sent by the office computer is within the validity period. If so, determine whether the device identification code sent by the office computer belongs to one of the device identification codes stored in the office computer. If so, generate an identity token and send the identity token to the office computer. If not, do not return the identity token to the office computer.

7. A commodity procurement management system according to claim 3, characterized in that: Get the user's login instructions, including: Obtain the user's click information on the login interface of the commodity procurement management client, and determine whether the user enters a login instruction based on the click information.

8. A commodity procurement management system according to claim 7, characterized in that: Click information includes the click coordinates of the user using the mouse to click on the login interface; Determine whether the user enters a login instruction based on the click information, including: It is determined whether the click coordinates are within the preset coordinate range. If so, it means that the user inputs a login instruction.

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