Raw material mixed batch feeding method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a raw material mixed batch feeding method and device, electronic equipment and a storage medium, and relates to the technical field of biological product manufacturing. According to the method, a target constraint function is dynamically constructed based on a sample raw material configured by a user and constraint information, and the target constraint function is optimally solved by using target attribute information of the sample raw material to obtain a feeding amount meeting a constraint condition. Scientific distribution of mixed batch feeding of raw materials is achieved, the accuracy of the mixed batch feeding process is remarkably improved, meanwhile, the consistency and stability of the quality of manufactured finished products are ensured, and the requirement for high-quality control of modern biological manufacturing production is met.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of biological product manufacturing, in particular to a raw material mixed batch feeding method, device, electronic equipment and storage medium. BACKGROUND

[0002] In the production process of biological products, such as traditional Chinese medicine, food, health products, and crop products, the quality and chemical composition of raw materials may fluctuate significantly due to factors such as source of origin, production batch, and storage conditions. This inherent variability of raw materials directly affects the quality characteristics of the final manufactured product. Traditional production methods rely mainly on the experience and judgment of operators and simple proportional conversion to determine the amount of raw materials, but this method cannot systematically quantify the quality differences between different batches of raw materials, resulting in significant quality fluctuations in the finished product. SUMMARY

[0003] Therefore, the purpose of the present application is to provide a raw material mixed batch feeding method, device, electronic equipment and storage medium, which can ensure the consistency and stability of the quality of the mixed batch feeding product.

[0004] To achieve the above-mentioned purpose, the technical solutions adopted by the embodiments of the present application are as follows: In a first aspect, the present application provides a raw material mixed batch feeding method, which comprises: In response to the user's feeding calculation operation, the sample raw material and constraint information configured by the user are obtained; According to the constraint information, the target constraint function and the target attribute information corresponding to the sample raw material are determined; Based on the target attribute information and the constraint information, the target constraint function is optimally solved to obtain the feeding amount of the sample raw material.

[0005] In an optional implementation, the constraint information includes the total feeding amount; and the determination of the target constraint function and the target attribute information corresponding to the sample raw material according to the constraint information comprises: If the user configures the total feeding amount constraint, the inventory quantity corresponding to the sample raw material is determined as the target attribute information corresponding to the sample raw material; The constraint function corresponding to the total feeding amount, the constraint function corresponding to the inventory quantity, and the mean regular function are determined as the target constraint function; The constraint function corresponding to the total feeding amount is:

[0006] The constraint function corresponding to the inventory quantity is:

[0007] The mean regular function is:

[0008]

[0009] is the total feeding amount; is the feeding amount to be calculated for the jth sample raw material; is the inventory amount of the jth sample raw material; is the deviation of the feeding amount of the jth sample raw material from the feeding mean; and n is the number of sample raw materials.

[0010] In an optional implementation, the constraint information includes a total feeding amount, a minimum index value of a quality index to be verified, and a maximum index value of the quality index to be verified; and the determining of the target constraint function and the target attribute information corresponding to the sample raw material according to the constraint information includes: If a user configures a quality index constraint, the inventory amount corresponding to the sample raw material and the standard index value of the quality index to be verified are determined as the target attribute information corresponding to the sample raw material; the constraint function corresponding to the inventory amount, and the mean regular function are determined as the target constraint function; The constraint function corresponding to the quality index to be verified is:

[0011] The constraint function corresponding to the inventory amount is:

[0012] The mean regular function is:

[0013]

[0014] is the total feeding amount; is the standard index value of the ith quality index to be verified corresponding to the jth sample raw material; is the minimum index value of the ith quality index to be verified; is the maximum index value of the ith quality index to be verified; is the feeding amount to be calculated for the jth sample raw material; is the inventory amount of the jth sample raw material; is the deviation of the feeding amount of the jth sample raw material from the feeding mean; and n is the number of sample raw materials.

[0015] In an optional implementation, the constraint information comprises a total feeding amount, a minimum index value of the quality index to be verified, and a maximum index value of the quality index to be verified; and the determining of the target constraint function and the target attribute information corresponding to the sample raw material according to the constraint information comprises: If the user configures the total feeding amount constraint and the quality index constraint, the inventory quantity corresponding to the sample raw material and the standard index value of the quality index to be verified are determined as the target attribute information corresponding to the sample raw material; The constraint function corresponding to the total feeding amount, the constraint function corresponding to the quality index to be verified, the constraint function corresponding to the inventory quantity, and the mean regular function are determined as the target constraint function; The constraint function corresponding to the total feeding amount is:

[0016] The constraint function corresponding to the inventory quantity is:

[0017] The mean regular function is:

[0018]

[0019] The constraint function corresponding to the quality index to be verified is:

[0020] The total feeding amount is; The standard index value of the i-th quality index to be verified corresponding to the j-th sample raw material is; The minimum index value of the i-th quality index to be verified is; The maximum index value of the i-th quality index to be verified is; The feeding amount of the j-th sample raw material to be calculated is; The inventory quantity of the j-th sample raw material is; The deviation of the feeding amount of the j-th sample raw material from the feeding mean value is; and n is the number of sample raw materials.

[0021] In an optional implementation, after the optimal solution of the target constraint function is performed based on the target attribute information and the constraint information to obtain the feeding amount of the sample raw material, the method further comprises: The mixed batch index value corresponding to the quality index and the quality index curve are determined according to the feeding amount of the sample raw material.

[0022] In an optional implementation, the method further comprises: In response to the user's feeding amount updating operation, the latest total feeding amount, the latest feeding amount corresponding to the sample raw material, and the standard index value of the all quality indexes corresponding to the sample raw material are obtained; According to the latest total feeding amount, the latest feeding amount, and the standard index value of each quality index corresponding to the sample raw material, the latest mixed batch index value corresponding to each quality index is determined; According to the latest mixed batch index value, the latest quality index curve is drawn.

[0023] In an optional embodiment, the method further comprises: In response to the user's automatic allocation operation, the user-configured unfixed sample raw material, the fixed sample raw material, the feeding amount of the fixed sample raw material, and the constraint information are obtained; According to the constraint information and the feeding amount of the fixed sample raw material, the target constraint function, the target attribute information corresponding to the unfixed sample raw material, and the target attribute information corresponding to the fixed sample raw material are determined; Based on the target attribute information corresponding to the unfixed sample raw material, the target attribute information corresponding to the fixed sample raw material, and the constraint information, the target constraint function is optimally solved to obtain the feeding amount of the unfixed sample raw material; According to the feeding amount of the unfixed sample raw material and the feeding amount of the fixed sample raw material, the mixed batch index value corresponding to the quality index and the quality index curve are determined.

[0024] In a second aspect, the present application provides a raw material mixed batch feeding device, which comprises: A response module is configured to obtain the sample raw material and the constraint information configured by the user in response to the user's feeding calculation operation; A processing module is configured to determine the target constraint function and the target attribute information corresponding to the sample raw material according to the constraint information, and to optimally solve the target constraint function based on the target attribute information and the constraint information to obtain the feeding amount of the sample raw material.

[0025] In a third aspect, the present application provides an electronic device comprising a processor and a memory, wherein the memory stores a computer program capable of being executed by the processor, and the processor can execute the computer program to implement the raw material mixed batch feeding method according to any one of the preceding embodiments.

[0026] In a fourth aspect, the present application provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the raw material mixed batch feeding method according to any one of the preceding embodiments.

[0027] Compared with the prior art, the raw material batch feeding method, device, electronic equipment and storage medium provided by the embodiment of the present application dynamically construct a target constraint function based on the sample raw material and constraint information configured by the user, and use the target attribute information of the sample raw material to optimally solve the target constraint function to obtain a feeding amount that meets the constraint condition. The present application realizes scientific allocation of raw material batch feeding, significantly improves the accuracy of the batch feeding process, and at the same time ensures the consistency and stability of the product quality, meeting the requirements of modern biological manufacturing production for high quality control.

[0028] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the following preferred embodiments are specifically described below, and the accompanying drawings are described in detail as follows. BRIEF DESCRIPTION OF DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0030] Figure 1 A flowchart of the raw material batch feeding method provided by the embodiment of the present application is shown.

[0031] Figure 2 A configuration diagram of the raw material batch feeding method provided by the embodiment of the present application is shown.

[0032] Figure 3 A batch feeding diagram of the raw material batch feeding method provided by the embodiment of the present application is shown.

[0033] Figure 4 Another flowchart of the raw material batch feeding method provided by the embodiment of the present application is shown.

[0034] Figure 5 Another configuration diagram of the raw material batch feeding method provided by the embodiment of the present application is shown.

[0035] Figure 6 A diagram of the quality index curve of the raw material batch feeding method provided by the embodiment of the present application is shown.

[0036] Figure 7 Another flowchart of the raw material batch feeding method provided by the embodiment of the present application is shown.

[0037] Figure 8 A block diagram of the raw material batch feeding device provided by the embodiment of the present application is shown.

[0038] Figure 9 A block diagram of an electronic device provided by an embodiment of the present invention is shown.

[0039] Icons: 400 - raw material mixed batch feeding device; 401 - response module; 402 - processing module; 500 - electronic equipment; 510 - memory; 520 - processor; 530 - communication module. DETAILED DESCRIPTION

[0040] The following will be combined with the accompanying drawings to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0041] 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 invention as claimed, but is merely intended to represent selected embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative work are within the scope of protection of the present invention.

[0042] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

[0043] During the production of biological products, raw material quality indicators often fluctuate to a certain extent, which directly affects the quality and stability of the final product. Because raw materials are affected by multiple factors such as source, batch, and storage conditions, different batches of raw materials often exhibit significant differences in quality parameters and active ingredient content. Therefore, in the production process of biological products, how to scientifically configure the feed ratio of each batch of raw materials based on their quality indicators to ensure uniform content and quality stability of the finished product has become a key technical challenge in quality control.

[0044] The traditional feeding method mainly relies on manual experience or simple proportion calculation, and cannot systematically consider the quality fluctuation of different batches of raw materials and its comprehensive influence on product quality. With the increasingly stringent quality control standards of biological products, it is urgent to establish a scientific and refined raw material proportioning optimization method.

[0045] Based on this, the raw material batch feeding method, device, electronic equipment and storage medium provided by the embodiments of the present application dynamically construct a target constraint function based on the sample raw materials and constraint information configured by the user, use the target attribute information of the sample raw materials to optimally solve the target constraint function, and obtain the feeding amount that meets the constraint condition. The present application realizes the scientific allocation of raw material batch feeding, significantly improves the accuracy of the batch feeding process, and at the same time ensures the consistency and stability of the product quality, meeting the requirements of modern biological manufacturing production for high quality control.

[0046] The embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0047] Please refer to Figure 1 , Figure 1 A flowchart of a raw material batch feeding method provided by an embodiment of the present application is shown. The method comprises the following steps: Step S100, in response to the user's feeding calculation operation, obtaining the sample raw materials and constraint information configured by the user.

[0048] Step S110, determining the target constraint function and the target attribute information corresponding to the sample raw materials according to the constraint information.

[0049] In the embodiments of the present application, when the user initiates the feeding calculation operation, the sample raw materials and constraint information configured by the user are obtained. The sample raw materials and constraint information are configured by the user according to the actual production requirements. Among them, the sample raw materials refer to different batches of raw materials participating in batch feeding. The constraint information is used to limit the calculation conditions or restriction rules of raw material batch feeding.

[0050] It should be understood that the function of the constraint information is to provide clear boundary conditions and optimization targets for the optimal solution of the feeding amount, and the introduction of the constraint information makes the raw material batch feeding method more flexible to adapt to different application scenarios.

[0051] The constraint information directly determines the form of the target constraint function and the selection range of the target attribute information corresponding to the sample raw materials. Among them, the target constraint function refers to the constraint condition set used to describe the mathematical model of the entire feeding optimization problem, including but not limited to total feeding amount constraint, inventory amount constraint, and mean regular constraint, quality index constraint, etc.

[0052] The target attribute information refers to specific parameters related to the sample raw materials, such as the inventory of each batch of raw materials, the standard index value of the quality index, and the like. It should be noted that the determination of the target attribute information depends on the specific constraint conditions configured by the user (i.e., the constraint information). For example, if the user only configures the total feeding amount constraint, the target attribute information includes the inventory of the sample raw materials, and if the user configures the quality index constraint at the same time, the target attribute information also needs to include the standard index value of the to-be-verified index.

[0053] In step S120, the target constraint function is optimally solved based on the target attribute information and the constraint information to obtain the feeding amount of the sample raw materials.

[0054] In the embodiment of the present application, the linear solver is used to optimally solve the target constraint function based on the target attribute information and the constraint information. That is, under the premise of meeting all the constraint conditions (i.e., the target attribute information and the constraint information), the feeding amount distribution scheme that makes the target constraint function reach the optimal solution is found, so that the feeding amount of each sample raw material is as close to the feeding average as possible. It can be understood that the core of this step is to convert the complex raw material batch feeding optimization problem into a mathematical modeling solution, thereby ensuring that the feeding amount solved can strictly meet the constraint configuration of the user.

[0055] It should be noted that when the target constraint function is optimally solved, if there is no solution, a no solution prompt is fed back to the user so as to relax the constraint condition and re-perform the feeding calculation operation. By introducing the relaxation variable (i.e., relaxing the constraint condition), the feeding amount of the sample raw materials is recalculated to ensure more flexible adaptation to different constraint conditions, thereby increasing the flexibility and robustness of the raw material batch feeding, which can cope with complex resource allocation and constraint conditions in the actual production environment, and ensure the feasibility and adaptability of the optimal solution of the target constraint function.

[0056] In summary, the raw material batch feeding method provided in the embodiment of the present application dynamically constructs the target constraint function based on the sample raw materials and the constraint information configured by the user, optimally solves the target constraint function based on the target attribute information of the sample raw materials, and obtains the feeding amount that meets the constraint condition. The present application realizes the scientific distribution of raw material batch feeding, significantly improves the accuracy of the batch feeding process, and at the same time ensures the consistency and stability of the product quality, thereby meeting the requirements of modern biological manufacturing production for high-quality control.

[0057] Optionally, in actual application, the user can configure the total feeding amount constraint and the quality index constraint at the same time, and the constraint information includes the total feeding amount, the minimum index value of the to-be-verified quality index, and the maximum index value of the to-be-verified quality index. As to how to determine the target constraint function and the target attribute information of the sample raw materials by using the total feeding amount constraint and the quality index constraint, a possible implementation manner is provided as follows. Figure 1The sub-step of step S110 can comprise: If the user configures the total feeding amount constraint and the quality index constraint, the inventory amount of the sample raw material corresponding to the to-be-verified quality index and the standard index value of the to-be-verified quality index are determined as the target attribute information corresponding to the sample raw material; the constraint function corresponding to the total feeding amount, the constraint function corresponding to the to-be-verified quality index, the constraint function corresponding to the inventory amount, and the mean regular function are determined as the target constraint function; The constraint function corresponding to the total feeding amount is:

[0058] The constraint function corresponding to the inventory amount is:

[0059] The mean regular function is:

[0060]

[0061] The constraint function corresponding to the to-be-verified quality index is:

[0062] is the total feeding amount; is the standard index value of the i-th to-be-verified quality index corresponding to the j-th sample raw material; is the minimum index value of the i-th to-be-verified quality index; is the maximum index value of the i-th to-be-verified quality index; is the feeding amount that needs to be calculated for the j-th sample raw material; is the inventory amount of the j-th sample raw material; is the deviation of the feeding amount of the j-th sample raw material from the feeding mean value; n is the number of sample raw materials.

[0063] In the embodiment of the application, the user configures the total feeding amount constraint and the quality index constraint (i.e., the verification index constraint) at the same time. The total feeding amount constraint is used to describe whether the feeding amount of the sample raw material is constrained by the total feeding amount, and the quality index constraint is used to describe whether the batched index value of the to-be-verified quality index after the sample raw material is batched satisfies the constraint of the minimum index value and the maximum index value of the to-be-verified quality index.

[0064] Specifically, the user requires that all the quality indexes are to-be-verified quality indexes, and requires that the batched index value of each to-be-verified quality index must be located in the verification interval determined by the minimum index value and the maximum index value of the to-be-verified quality index, i.e., the batched index value of the to-be-verified quality index is not less than the corresponding minimum index value, and the batched index value of the to-be-verified quality index is not greater than the corresponding maximum index value.

[0065] When the user configures the total feeding amount constraint and the quality index constraint at the same time, the target attribute information of the sample raw material includes the inventory amount of the sample raw material and the standard index value of the to-be-verified quality index corresponding to the sample raw material. The inventory amount reflects the maximum amount of each batch of sample raw material that can be used, and the standard index value reflects the quality characteristics of each batch of sample raw material, which is used to calculate the actual content of each quality index in the finished product.

[0066] The constraint function corresponding to the total feeding amount, the constraint function corresponding to the to-be-verified quality index, the constraint function corresponding to the inventory amount, and the mean regular function are optimally solved according to the total feeding amount, the minimum index value of the to-be-verified quality index, the maximum index value of the to-be-verified quality index, the inventory amount of each sample raw material, and the standard index value of the to-be-verified quality index. If there is a solution, the feeding amount of the sample raw material is obtained.

[0067] The constraint function corresponding to the total feeding amount is defined to ensure that the sum of the feeding amounts of each sample raw material is equal to the set total feeding amount. The constraint function corresponding to the inventory amount is used to limit the feeding amount of each batch of sample raw material to be greater than or equal to zero and less than or equal to the actual available inventory amount. The constraint function corresponding to the to-be-verified quality index is used to ensure that the actual content (i.e., the mixed batch index value) of each quality index in the finished product falls within the verification interval set by the user.

[0068] The role of the mean regular function is to optimize the feeding amount distribution so that it is as close to the average value as possible. Specifically, a deviation variable is introduced for each sample raw material to measure the difference between the feeding amount of the corresponding sample raw material and the average feeding amount (i.e., the feeding average). The feeding average is the ratio of the total feeding amount to the number of sample raw materials, and the deviation variable is jointly ensured by the constraint of the mean regular function to be less than or equal to the absolute value of the difference between the feeding amount of the sample raw material and the feeding average.

[0069] For example, the total feeding amount is 500, and the number of sample raw materials is 5, so the feeding average is 100. We want the feeding amount of each sample raw material to be as close to 100 (i.e., the feeding average) as possible. That is, under the condition of meeting the constraint of mixed batch feeding, each batch of sample raw material is used as much as possible, and is distributed as evenly as possible.

[0070] For each sample raw material, an auxiliary variable d is introduced to measure the deviation of the feeding amount of the sample raw material from the feeding average. Since linear programming cannot directly handle absolute values, two inequalities in the mean regular function are used to approximate this goal, so that the feeding amount of each sample raw material is as close to the feeding average of 100 as possible, thereby achieving uniform feeding.

[0071] As a possible implementation, the total feeding amount is set to 500, the number of sample raw materials is set to 5, the minimum index value of the to-be-verified quality index is set to 0, and the maximum index value of the to-be-verified quality index is set to 100. Figure 2For example, assuming that the raw materials to be calculated are red ginseng slices and attached pieces (black smooth pieces), the user selects sample raw materials from multiple batches of raw materials, and the first five batches of raw ginseng slices are determined as the sample raw materials corresponding to the red ginseng slices, and the first three batches of raw materials of the attached pieces are determined as the sample raw materials corresponding to the attached pieces.

[0072] wherein the total amount of red ginseng slices is set by the total amount of red ginseng, the quality indicators to be verified of the red ginseng slices include Rg1, Re and Rb1, the value range of the mixed batch index value corresponding to Rg1 is limited by setting the minimum index value and the maximum index value of Rg1, the value range of the mixed batch index value corresponding to Re is limited by setting the minimum index value and the maximum index value of Re, and the value range of the mixed batch index value corresponding to Rb1 is limited by setting the minimum index value and the maximum index value of Rb1.

[0073] The total amount of attached pieces is set by the total amount of attached pieces, and the quality indicators to be verified of the attached pieces include extractives, and the value range of the mixed batch index value corresponding to the extractives is limited by setting the minimum index value and the maximum index value of the extractives.

[0074] The constraint conditions of the red ginseng slices are used to optimally solve the target constraint function to obtain the feeding amount of the sample raw materials corresponding to the red ginseng slices, and then the feeding amount of the sample raw materials corresponding to the red ginseng slices is used to determine the mixed batch index values corresponding to the quality indicators of the red ginseng slices. Similarly, the constraint conditions of the attached pieces are used to optimally solve the target constraint function to obtain the feeding amount of the sample raw materials corresponding to the attached pieces, and then the feeding amount of the sample raw materials corresponding to the attached pieces is used to determine the mixed batch index values corresponding to the quality indicators of the attached pieces. Finally, the mixed batch index values corresponding to the quality indicators of the red ginseng slices and the mixed batch index values corresponding to the quality indicators of the attached pieces are used to draw a quality index curve (i.e. an index curve), as shown in Figure 3 .

[0075] In addition, the user can also set the mean weight (i.e. the value close to the mean value), and the value range of the mean weight is 1 to 100. The mean weight is used to represent the importance of uniformity when optimally solving the target constraint function, and the greater the value, the more attention is paid to uniformity, and the smaller the value, the less attention is paid to uniformity. That is, the mean weight is used to control the balance of the feeding amount, and the target is to minimize the difference between the feeding amounts of the sample raw materials, so that the feeding amounts of the sample raw materials tend to the mean value, thereby ensuring the efficiency and stability of the biological product production process.

[0076] It can be seen that the embodiment of the present application realizes the target of making the distribution of the feeding amount close to the mean value as far as possible under the premise of meeting the total feeding amount, the corresponding check interval of the quality index and the inventory amount by simultaneously incorporating the total feeding amount constraint and the quality index constraint into the target constraint function and combining the inventory amount, the standard index value and the mean value regularization function, thereby ensuring the accurate control of the total feeding amount and the accurate satisfaction of each quality index in the finished product and reasonably allocating the feeding amount of each batch of sample raw material, so as to significantly improve the stability and consistency of the manufacturing product quality. The method effectively solves the error problem that may be caused by the traditional manual experience method and meets the requirement of high quality control of modern biological manufacturing production.

[0077] In actual application, in the case that the optimal solution cannot be found when the user simultaneously configures the total feeding amount constraint and the quality index constraint, the constraint condition is relaxed by introducing a slack variable, so as to adjust the calculation result and ensure more flexible adaptation to different conditions. Based on this, the embodiment of the present application supports the user to separately configure the total feeding amount constraint and determines the feeding amount of the sample raw material by using the total feeding amount constraint configured by the user.

[0078] Optionally, in actual application, the constraint information includes the total feeding amount. As to how to determine the target constraint function and the target attribute information by using the total feeding amount constraint, a possible implementation manner is provided below. Figure 1 The sub-step of step S110 can include: If the user configures the total feeding amount constraint, the inventory amount corresponding to the sample raw material is determined as the target attribute information corresponding to the sample raw material, and the constraint function corresponding to the total feeding amount, the constraint function corresponding to the inventory amount and the mean value regularization function are determined as the target constraint function. The constraint function corresponding to the total feeding amount is:

[0079] The constraint function corresponding to the inventory amount is:

[0080] The mean value regularization function is:

[0081]

[0082] is the total feeding amount; is the feeding amount to be calculated for the jth sample raw material; is the inventory amount of the jth sample raw material; is the deviation of the feeding amount of the jth sample raw material from the feeding mean value; and n is the number of sample raw materials.

[0083] In the embodiment of the present application, when the user separately configures the total feeding amount constraint, the target attribute information of the sample raw material includes the inventory amount of the sample raw material, and the optimal solution is performed on the constraint function corresponding to the total feeding amount, the constraint function corresponding to the inventory amount, and the mean regular function according to the total feeding amount and the inventory amount of each sample raw material, and if there is a solution, the feeding amount of the sample raw material is obtained.

[0084] It can be seen that, by incorporating the total feeding amount constraint into the target constraint function and combining the inventory amount and the mean regular function to perform the optimal solution, the embodiment of the present application realizes the target of making the feeding amount distribution as close to the mean value as possible under the premise of meeting the total feeding amount and the inventory amount, thereby not only ensuring the accurate control of the total feeding amount, but also reasonably allocating the feeding amount of each batch of sample raw material, so as to significantly improve the stability and consistency of the product quality, and meet the requirement of high quality control of modern biological manufacturing production.

[0085] In actual application, in the case that the optimal solution cannot be found when the user separately configures the total feeding amount constraint, a slack variable is introduced to adjust the calculation result, so as to ensure more flexible adaptation to different conditions. Based on this, the embodiment of the present application supports the user to separately configure the quality index constraint, and uses the quality index constraint configured by the user to solve the feeding amount of the sample raw material.

[0086] Optionally, in actual application, the constraint information includes the total feeding amount, the minimum index value of the to-be-verified quality index, and the maximum index value of the to-be-verified quality index. As to how to use the quality index constraint to determine the target constraint function and the target attribute information, a possible implementation manner is provided below. Figure 1 The sub-step of step S110 can include: If the user configures the quality index constraint, the inventory amount corresponding to the sample raw material and the standard index value of the to-be-verified quality index are determined as the target attribute information corresponding to the sample raw material; The constraint function corresponding to the to-be-verified quality index, the constraint function corresponding to the inventory amount, and the mean regular function are determined as the target constraint function; The constraint function corresponding to the to-be-verified quality index is:

[0087] The constraint function corresponding to the inventory amount is:

[0088] The mean regular function is:

[0089]

[0090] The total feeding amount is: the standard index value of the i th to-be-verified quality index corresponding to the j th sample raw material; the minimum index value of the i th to-be-verified quality index; the maximum index value of the i th to-be-verified quality index; the feeding amount of the j th sample raw material; the inventory amount of the j th sample raw material; the deviation of the feeding amount of the j th sample raw material from the feeding average; n is the number of sample raw materials.

[0091] In the embodiment of the present application, when the user separately configures the quality index constraint, the target attribute information of each sample raw material includes the inventory amount of the sample raw material and the standard index value of the to-be-verified quality index corresponding to the sample raw material. The constraint function corresponding to the to-be-verified quality index, the constraint function corresponding to the inventory amount and the average regular function are optimally solved according to the total feeding amount, the inventory amount of each sample raw material and the standard index value of the to-be-verified quality index, and if there is a solution, the feeding amount of the sample raw material is obtained.

[0092] As a possible implementation manner, the user configures the quality index constraint, the user selects five sample raw materials, and the batch numbers of the sample raw materials are 1, 2, 3, 4 and 5 respectively. The total feeding amount is 500 kg, the to-be-verified quality index includes index one, index two and index three, and the inventory amount of the sample raw material and the standard index value of the to-be-verified quality index are as shown in Table 1.

[0093] Table 1

[0094] It is assumed that the feeding amount of the sample raw material with the batch number 1 is A[0], the feeding amount of the sample raw material with the batch number 2 is A[1], the feeding amount of the sample raw material with the batch number 3 is A[2], the feeding amount of the sample raw material with the batch number 4 is A[3], and the feeding amount of the sample raw material with the batch number 5 is A[4]. In combination with Table 1, the constraint function corresponding to the to-be-verified quality index is expanded as follows: (index one of batch number 1 * A[0]+ index one of batch number 2 * A[1]+ index one of batch number 3 * A[2]+ index one of batch number 4 * A[3]+ index one of batch number 5 * A[3]) / total feeding amount, the result of the calculation needs to be not less than the minimum index value of index one and not greater than the maximum index value of index one.

[0095] (Indicator 2 of batch number 1 * A[0] + Indicator 2 of batch number 2 * A[1] + Indicator 2 of batch number 3 * A[2] + Indicator 2 of batch number 4 * A[3] + Indicator 2 of batch number 5 * A[3]) / total feed amount. The calculated result must be no less than the minimum indicator value of indicator 2 and no greater than the maximum indicator value of indicator 2.

[0096] (Indicator 3 of batch number 1 * A[0] + Indicator 3 of batch number 2 * A[1] + Indicator 3 of batch number 3 * A[2] + Indicator 3 of batch number 4 * A[3] + Indicator 3 of batch number 5 * A[3]) / total feed amount. The calculated result must be no less than the minimum value of indicator 3 and no greater than the maximum value of indicator 3. Then, use the constraint function corresponding to the quality indicator to be verified, the constraint function corresponding to the inventory, and the mean regularization function to perform the optimal solution and obtain the feed amount of the five sample raw materials.

[0097] It should be noted that when configuring quality indicator constraints, users can choose to satisfy all quality indicators or prioritize any one quality indicator. For example, the quality indicators for red ginseng slices include Rg1, Re, and Rb1. Users can configure "all satisfied," meaning that the mixed batch values ​​of the three quality indicators must meet the corresponding verification interval requirements. The verification interval is determined based on the minimum and maximum quality indicator values.

[0098] Users can also configure "Rg1 priority", "Re priority" or "Rb1 priority". When solving the objective constraint function, the verification interval of the quality indicator with priority will be followed first. That is, the weight of the verification interval of the quality indicator with priority will be increased, and the weights of the verification intervals of other quality indicators will be allocated to (-∞, +∞). That is, the minimum and maximum indicator values ​​corresponding to the unselected quality indicators will be automatically ignored.

[0099] It can be seen that the embodiment of the present invention incorporates the quality indicator constraint into the objective constraint function, and combines the total feed quantity, inventory, standard indicator value of the quality indicator and the mean regularization function to perform the optimal solution. It achieves the goal of reasonably allocating the feed quantity of each batch of sample raw materials while meeting the verification interval of the quality indicator set by the user, making the feed quantity distribution as close to the mean as possible, thereby significantly improving the stability and consistency of the quality of the finished product and meeting the requirements of modern biomanufacturing production for high quality control.

[0100] Optionally, a possible implementation method is provided below for visually displaying the results of mixed batch feeding of raw materials. Figure 1 After step S120, the method further comprises the following steps: The mixed batch index value corresponding to the quality index and the quality index curve are determined according to the total feeding amount and the feeding amount of the sample raw material.

[0101] In the embodiment of the present application, the actual total feeding amount is determined according to the feeding amount of the sample raw material, the mixed batch index value corresponding to each quality index is determined according to the actual total feeding amount, the feeding amount of each sample raw material and the standard index value of the corresponding quality index of the sample raw material, and the quality index curve is generated based on the mixed batch index value corresponding to the quality index.

[0102] The mixed batch index value refers to the actual content value of each quality index in the finished product after the mixed batch feeding, and the quality index curve is used to intuitively show the change trend of the quality index. For example, after the feeding amount of a batch is calculated, the mixed batch index value of the quality index in the finished product can be calculated according to the standard index value of the quality index of each batch of raw material and the actual feeding amount ratio, and the quality index curve is generated for the user to analyze and monitor.

[0103] In actual application, the user will manually adjust the feeding amount of some sample raw materials in combination with the inventory amount of the sample raw material, the mixed batch index value corresponding to the quality index and other factors. When the user manually adjusts the feeding amount of the sample raw material, the present application will recalculate the mixed batch index value of the quality index and update the display of the quality index curve.

[0104] Optionally, for how to dynamically update the mixed batch index value of the quality index and the quality index curve, a possible implementation is provided as follows. Please refer to Figure 4 The method further includes the following steps: Step S200, in response to the feeding amount update operation of the user, the latest total feeding amount, the latest feeding amount corresponding to the sample raw material and the standard index value of all quality indexes corresponding to the sample raw material are obtained.

[0105] In the embodiment of the present application, when the user initiates the feeding amount update operation, the latest total feeding amount, the latest feeding amount corresponding to the sample raw material and the standard index value of all quality indexes corresponding to the sample raw material are obtained in response to the operation. The latest total feeding amount is the sum of the latest feeding amount corresponding to each sample raw material.

[0106] Step S210, the latest mixed batch index value corresponding to each quality index is determined according to the latest total feeding amount, the latest feeding amount and the standard index value of each quality index corresponding to the sample raw material.

[0107] Step S220, the latest quality index curve is drawn according to the latest mixed batch index value.

[0108] In the embodiment of the present application, taking the red ginseng decoction piece as an example, the user manually adjusts the feeding amount of the sample raw material with the batch number A17201208002, for example Figure 5The latest mixed batch index value of each quality index is calculated by multiplying the latest feeding amount of each sample raw material by the corresponding standard index value and summing the products. Finally, the latest quality index curve is drawn using the latest mixed batch index values of each quality index. Taking Figure 2 as an example, when the user manually adjusts the feeding amount, Figure 3 the index curve in Figure 6 will be updated to Figure 6 As can be seen from Figure 6 , after the user manually adjusts the feeding amount, the latest mixed batch index values of quality indexes Rg1 and Rb1 are within the user-set inspection interval, while the latest mixed batch index value of quality index Re has exceeded the maximum value set by the user, as indicated by the red box in .

[0109] It can be seen that the embodiment of the present application supports the user to dynamically update the feeding amount, realizes the dynamic adjustment capability of raw material mixed batch feeding, and thus can quickly respond to the changing needs in the production process, not only ensures the consistency and stability of the finished product quality indexes, but also effectively improves the adaptability and flexibility, meets the requirements of modern biological product production for efficient and accurate control. At the same time, by displaying the latest quality index curve in real time, it ensures that each quality index can be monitored and adjusted in real time during the production process, further enhances the user's control capability of the feeding process, and makes the mixed batch feeding process more transparent and controllable.

[0110] Optionally, in actual application, after the user fixes the feeding amount of certain sample raw materials, an automatic allocation operation is performed, that is, the feeding amount of certain sample raw materials is fixed, and the feeding amount of unfixed sample raw materials is calculated. For how to perform automatic allocation based on the fixed feeding amount of sample raw materials, a possible implementation manner is provided below. Please refer to Figure 7 , which further includes the following steps: Step S300, in response to the automatic allocation operation of the user, the user-configured unfixed sample raw materials, fixed sample raw materials, fixed sample raw material feeding amount and constraint information are obtained.

[0111] Step S310, the target constraint function, the target attribute information corresponding to the unfixed sample raw materials and the target attribute information corresponding to the fixed sample raw materials are determined according to the constraint information and the fixed sample raw material feeding amount.

[0112] Step S320, the target constraint function is optimally solved based on the target attribute information corresponding to the unfixed sample raw materials, the target attribute information corresponding to the fixed sample raw materials and the constraint information, to obtain the feeding amount of the unfixed sample raw materials.

[0113] Step S330, the mixed batch index value corresponding to the quality index and the quality index curve are determined according to the feeding amount of the unfixed sample raw materials and the feeding amount of the fixed sample raw materials.

[0114] In the embodiment of the present application, when the user initiates the automatic allocation operation, the user-configured unfixed sample raw materials, fixed sample raw materials, fixed sample raw material feeding amount and constraint information are obtained. The target constraint function is composed of multiple constraint functions, including but not limited to the constraint function corresponding to the total feeding amount, the constraint function corresponding to the quality index to be verified, the constraint function corresponding to the inventory, and the mean regular function. It should be pointed out that the feeding amount of the fixed sample raw material is regarded as a fixed parameter and directly included in the construction process of the target constraint function.

[0115] Under the premise of meeting the constraint conditions of the target attribute information corresponding to the unfixed sample raw materials, the target attribute information corresponding to the fixed sample raw materials and the constraint information, the feeding amount of the unfixed sample raw materials is reasonably allocated. In the process of optimally solving the target constraint function, the feeding amount of the fixed sample raw material is used as known data for calculation, and the output result is the feeding amount of the unfixed sample raw material. Finally, the mixed batch index value corresponding to the quality index is calculated according to the feeding amount of the fixed sample raw material and the feeding amount of the unfixed sample raw material, and the mixed batch index value is used to draw the quality index curve.

[0116] It can be seen that the embodiment of the present application realizes accurate calculation of the feeding amount of the unfixed sample raw material under the condition that the feeding amount of the sample raw material of a specific batch is known by supporting the user to specify part of the raw material feeding amount and dynamically matching the target constraint function. Not only ensures the rationality and accuracy of the feeding amount of the unfixed sample raw material, but also can more flexibly cope with the combination of different needs in the production process, further meeting the requirements of modern biological manufacturing production for high quality control.

[0117] Based on the same inventive concept, the basic principles and technical effects of the raw material mixed batch feeding device provided by the embodiments of the present application are the same as those of the above-mentioned embodiments. For brevity, some parts of the present embodiment are not mentioned, and the corresponding contents of the above-mentioned embodiments can be referred to.

[0118] Please refer to Figure 8 , Figure 8 A block diagram of the raw material mixed batch feeding device 400 provided by the embodiment of the present application is provided. The raw material mixed batch feeding device 400 includes a response module 401 and a processing module 402.

[0119] The response module 401 is used to obtain the sample raw material and constraint information configured by the user in response to the feeding calculation operation of the user; The processing module 402 is used to determine the target constraint function and the target attribute information corresponding to the sample raw material according to the constraint information; and optimally solve the target constraint function based on the target attribute information and the constraint information to obtain the feeding amount of the sample raw material; In summary, the raw material batch feeding device provided by the embodiment of the present application dynamically constructs a target constraint function based on the sample raw material and constraint information configured by the user, optimally solves the target constraint function by using the target attribute information of the sample raw material, and obtains a feeding amount that meets the constraint condition. The present application realizes scientific distribution of raw material batch feeding, significantly improves the accuracy of the batch feeding process, and ensures the consistency and stability of the product quality, thereby meeting the high-quality control requirements of modern biological manufacturing production.

[0120] Optionally, the constraint information includes a total feeding amount. The processing module 402 is specifically configured to, if the user configures the total feeding amount constraint, determine the inventory amount of the sample raw material as the target attribute information corresponding to the sample raw material; and determine the constraint function corresponding to the total feeding amount, the constraint function corresponding to the inventory amount, and the mean regular function as the target constraint function.

[0121] The constraint function corresponding to the total feeding amount is:

[0122] The constraint function corresponding to the inventory amount is:

[0123] The mean regular function is:

[0124]

[0125] The total feeding amount is T; The feeding amount to be calculated for the jth sample raw material is Tj; The inventory amount of the jth sample raw material is Ij; The deviation of the feeding amount of the jth sample raw material from the feeding mean is Dj; and n is the number of sample raw materials.

[0126] Optionally, the constraint information includes a total feeding amount, a minimum index value of a quality index to be verified, and a maximum index value of the quality index to be verified. The processing module 402 is specifically configured to, if the user configures the quality index constraint, determine the inventory amount of the sample raw material and the standard index value of the quality index to be verified as the target attribute information corresponding to the sample raw material; and determine the constraint function corresponding to the quality index to be verified, the constraint function corresponding to the inventory amount, and the mean regular function as the target constraint function.

[0127] The constraint function corresponding to the quality index to be verified is:

[0128] The constraint function corresponding to the inventory amount is:

[0129] The mean regular function is:

[0130]

[0131] is the total feeding amount; is the standard index value of the i-th to-be-verified quality index corresponding to the j-th sample raw material; is the minimum index value of the i-th to-be-verified quality index; is the maximum index value of the i-th to-be-verified quality index; is the feeding amount of the j-th sample raw material that needs to be calculated; is the inventory amount of the j-th sample raw material; is the deviation of the feeding amount of the j-th sample raw material from the feeding mean value; n is the number of sample raw materials.

[0132] Optionally, the constraint information includes the total feeding amount, the minimum index value of the to-be-verified quality index, and the maximum index value of the to-be-verified quality index. The processing module 402 is specifically configured to, if the user configures the total feeding amount constraint and the quality index constraint, determine the inventory amount of the sample raw material and the standard index value of the to-be-verified quality index as the target attribute information corresponding to the sample raw material; and determine the constraint function corresponding to the total feeding amount, the constraint function corresponding to the to-be-verified quality index, the constraint function corresponding to the inventory amount, and the mean regular function as the target constraint function.

[0133] The constraint function corresponding to the total feeding amount is:

[0134] The constraint function corresponding to the inventory amount is:

[0135] The mean regular function is:

[0136]

[0137] The constraint function corresponding to the to-be-verified quality index is:

[0138] is the total feeding amount; is the standard index value of the i-th to-be-verified quality index corresponding to the j-th sample raw material; is the minimum index value of the i-th to-be-verified quality index; is the maximum index value of the i-th to-be-verified quality index; The feeding amount to be calculated for the jth sample raw material; The inventory amount of the jth sample raw material; The deviation of the feeding amount of the jth sample raw material from the feeding average; and n is the number of sample raw materials.

[0139] Optionally, the processing module 402 is further configured to determine the mixed batch index value corresponding to the quality index and the quality index curve according to the feeding amount of the sample raw material.

[0140] Please refer to Figure 9 The electronic device 500 provided by the embodiment of the present application is a block schematic diagram of the electronic device 500. The electronic device 500 includes, but is not limited to, a personal computer (PC), a Personal Digital Assistant (PDA), a notebook computer, a tablet computer, a server, etc. The electronic device 500 includes a memory 510, a processor 520, and a communication module 530. The memory 510, the processor 520, and the communication module 530 are directly or indirectly electrically connected to each other to realize data transmission or interaction. For example, these elements can be electrically connected to each other through one or more communication buses or signal lines.

[0141] The memory 510 is configured to store programs or data. The memory 510 can be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), an Electric Erasable Programmable Read-Only Memory (EEPROM), etc.

[0142] The processor 520 is configured to read / write the data or programs stored in the memory 510 and perform corresponding functions. For example, when the computer program stored in the memory 510 is executed by the processor 520, the raw material mixed batch feeding method disclosed in the above embodiments can be realized.

[0143] The communication module 530 is configured to establish a communication connection between the electronic device 500 and other communication terminals through a network, and to receive / transmit data through the network.

[0144] It should be understood that Figure 9 The structure shown is only a structural schematic diagram of the electronic device 500, and the electronic device 500 can further include more elements thanFigure 9 more or less components than those shown, or configurations of components having different configurations and / or Figure 9 configurations than those shown. Figure 9 The components shown in the various embodiments can be implemented in hardware, software or a combination thereof.

[0145] The embodiment of the present application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by the processor 520 to realize the raw material batching feeding method disclosed by the above-mentioned embodiments.

[0146] The embodiment of the present application further provides a program product, and the program product is executed by the processor 520 to realize the raw material batching feeding method disclosed by the above-mentioned embodiments.

[0147] In several embodiments provided in the present application, it should be understood that the disclosed apparatus and method can also be implemented by other manners. The apparatus embodiments described above are only schematic, for example, the flow charts and block diagrams in the drawings show the possible implementation architectures, functions and operations of the apparatus, method and computer program product according to the embodiments of the present application. In this regard, each block in the flow charts or block diagrams can represent a module, a program segment or a part of code, which contains one or more executable instructions for implementing the specified logic function. It should also be noted that, in some alternative implementation manners, the functions noted in the blocks can occur in different orders from those noted in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and sometimes they can be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flow charts, and the combination of blocks in the block diagrams and / or flow charts, can be implemented by a dedicated hardware-based system for implementing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0148] In addition, each functional module in the various embodiments of the present application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0149] If the functions are implemented in the form of software function modules and sold or used as independent products, the functions can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or parts of the present application that essentially contribute to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0150] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A mixed batch feeding method for raw materials, characterized in that: The method comprises: Respond to the user's material feeding calculation operation and obtain the sample raw materials and constraint information configured by the user; Determining a target constraint function and target attribute information corresponding to the sample raw material according to the constraint information; The target constraint function is optimally solved based on the target attribute information and the constraint information to obtain the feeding amount of the sample raw material.

2. The raw material mixed batch feeding method according to claim 1, characterized in that, The constraint information includes the total feed amount; and determining the target constraint function and the target attribute information corresponding to the sample raw material according to the constraint information includes: If the user configures a total material input constraint, the inventory corresponding to the sample raw material is determined as the target attribute information corresponding to the sample raw material; Determine the constraint function corresponding to the total feed amount, the constraint function corresponding to the inventory amount, and the mean regularization function as the target constraint function; Among them, the constraint function corresponding to the total feed amount is: The constraint function corresponding to the inventory is: The mean regularization function is: is the total feed amount; The amount of raw material to be fed for the jth sample; is the inventory of raw materials for the jth sample; is the deviation between the feeding amount of the jth sample raw material and the feeding mean; n is the number of sample raw materials.

3. The raw material mixed batch feeding method according to claim 1, characterized in that, The constraint information includes the total feed amount, the minimum value of the quality index to be verified, and the maximum value of the quality index to be verified; and determining the target constraint function and the target attribute information corresponding to the sample raw material according to the constraint information includes: If the user configures a quality index constraint, the inventory quantity corresponding to the sample raw material and the standard index value of the quality index to be verified are determined as the target attribute information corresponding to the sample raw material; Determine the constraint function corresponding to the quality indicator to be verified, the constraint function corresponding to the inventory quantity, and the mean regularization function as the target constraint function; The constraint function corresponding to the quality indicator to be verified is: The constraint function corresponding to the inventory is: The mean regularization function is: is the total feed amount; is the standard index value of the i-th quality index to be verified corresponding to the j-th sample raw material; is the minimum index value of the i-th quality index to be verified; is the maximum value of the i-th quality indicator to be verified; The amount of raw material to be fed for the jth sample; is the inventory of raw materials for the jth sample; is the deviation between the feeding amount of the jth sample raw material and the feeding mean; n is the number of sample raw materials.

4. The raw material mixed batch feeding method according to claim 1, characterized in that, The constraint information includes the total feed amount, the minimum value of the quality index to be verified, and the maximum value of the quality index to be verified; and determining the target constraint function and the target attribute information corresponding to the sample raw material according to the constraint information includes: If the user configures the total material input constraint and the quality index constraint, the inventory quantity corresponding to the sample raw material and the standard index value of the quality index to be verified are determined as the target attribute information corresponding to the sample raw material; Determine the constraint function corresponding to the total feeding amount, the constraint function corresponding to the quality index to be verified, the constraint function corresponding to the inventory amount, and the mean regularization function as the target constraint function; Among them, the constraint function corresponding to the total feed amount is: The constraint function corresponding to the inventory is: The mean regularization function is: The constraint function corresponding to the quality index to be verified is: is the total feed amount; is the standard index value of the i-th quality index to be verified corresponding to the j-th sample raw material; is the minimum index value of the i-th quality index to be verified; is the maximum value of the i-th quality indicator to be verified; The amount of raw material to be fed for the jth sample; is the inventory of raw materials for the jth sample; is the deviation between the feeding amount of the jth sample raw material and the feeding mean; n is the number of sample raw materials.

5. The raw material mixed batch feeding method according to claim 1, characterized in that, After optimally solving the target constraint function based on the target attribute information and the constraint information to obtain the feeding amount of the sample raw material, the method further includes: The mixed batch index value and the quality index curve corresponding to the quality index are determined according to the feeding amount of the sample raw materials.

6. The raw material mixed batch feeding method according to claim 5, characterized in that: The method further comprises: In response to the user's feed amount update operation, the latest total feed amount, the latest feed amount corresponding to the sample raw material, and the standard index values ​​of all quality indicators corresponding to the sample raw material are obtained; Determine the latest mixed batch index value corresponding to each quality index according to the latest total feed amount, the latest feed amount and the standard index value of each quality index corresponding to the sample raw material; The latest quality index curve is drawn according to the latest mixed batch index value.

7. The mixed batch feeding method of raw materials according to claim 1 or 5, characterized in that: The method further comprises: In response to the user's automatic allocation operation, obtaining the user-configured unfixed sample raw materials, fixed sample raw materials, feeding amounts of the fixed sample raw materials, and constraint information; Determining a target constraint function, target attribute information corresponding to the unfixed sample raw material, and target attribute information corresponding to the fixed sample raw material according to the constraint information and the feeding amount of the fixed sample raw material; performing an optimal solution to the target constraint function based on the target attribute information corresponding to the unfixed sample raw material, the target attribute information corresponding to the fixed sample raw material, and the constraint information to obtain the feeding amount of the unfixed sample raw material; The mixed batch index value and the quality index curve corresponding to the quality index are determined according to the feeding amount of the unfixed sample raw material and the feeding amount of the fixed sample raw material.

8. A raw material mixed batch feeding device, characterized in that: The device comprises: The response module is used to respond to the user's material feeding calculation operation and obtain the sample raw materials and constraint information configured by the user; A processing module is used to determine the target constraint function and the target attribute information corresponding to the sample raw material according to the constraint information; and optimally solve the target constraint function based on the target attribute information and the constraint information to obtain the feeding amount of the sample raw material.

9. An electronic device, characterized in that: It comprises a processor and a memory, wherein the memory stores a computer program that can be executed by the processor, and the processor can execute the computer program to implement the raw material mixed batch feeding method described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the raw material mixed batch feeding method according to any one of claims 1 to 7 is implemented.

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