Business data processing integration system and method based on big data platform

Abstract

The invention relates to the technical field of data processing, in particular to a business data processing integration system and method based on a big data platform, and the system comprises a first data receiving module which is used for receiving external data and transmitting the received external data to a selection module; the second data receiving module is used for receiving enterprise internal data, sending the received production data to the optimization module and sending the received financial data to the judgment module; the selection module is used for selecting a production plan according to the relationship between the industry data and the external public data and sending the production plan to the optimization module; the optimization module is used for optimizing the production plan according to the production data to obtain an optimization scheme and sending the optimization scheme to the judgment module; the method relates to data and decision-making processes of a plurality of departments such as production, finance and the like, facilitates promotion of information sharing and cooperation among the departments, and avoids the situation that production and sales of enterprises cannot be effectively carried out due to information islands.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a business data processing integration system and method based on a big data platform. Background Art

[0002] The rapid development of new-generation information technologies such as cloud computing, big data, the Internet of Things, mobile Internet, and artificial intelligence is accelerating the deepening of global industrial division of labor and economic structural adjustment, while profoundly reshaping the competitiveness of enterprises.

[0003] When enterprises specify production plans, they often need to conduct statistical analysis on various business data to select the best production plan. In the existing data processing method, data managers directly store data, resulting in scattered data distribution and information islands. Ultimately, the production plan of the enterprise is not supported by data, resulting in production losses. Summary of the invention

[0004] In view of the above-mentioned defects, the purpose of the present invention is to propose a business data processing integration system and method based on a big data platform to solve the problem of information islands and production plans being unable to obtain data support.

[0005] To achieve this purpose, the present invention adopts the following technical solution: a business data processing and integration system based on a big data platform, comprising:

[0006] A first data receiving module is used to receive external data, wherein the external data includes external industry data and external public data, and send the received external data to the decision module;

[0007] A second data receiving module is used to receive internal enterprise data, wherein the internal enterprise data includes production data and financial data, and send the received production data to the optimization module, and send the received financial data to the judgment module;

[0008] The decision module is used to select the production plan based on the relationship between industry data and external public data, and send the production plan to the optimization module;

[0009] The optimization module is used to optimize the production plan according to the production data, obtain the optimization plan, and send the optimization plan to the judgment module;

[0010] A judgment module is used to judge whether the optimization scheme is feasible based on the economic benefits and financial data of the optimization scheme. If it is feasible, it is sent to the decision module. If it is not feasible, the production plan is reselected or the external data is replaced;

[0011] The decision module is used to present the optimization plan to the user in a visual manner.

[0012] Preferably, it further comprises a data cleaning module, and before executing the optimization module, the production data is sent to the data cleaning module, and the data cleaning module is used to parse the missing parts in the production data and supplement the missing parts.

[0013] Preferably, the decision module includes a determination submodule, a comparison submodule, a direction determination submodule and a planning submodule;

[0014] The determination submodule is used to determine the items that the enterprise can produce based on external industry data and the production and business scope of the enterprise, and send the items to the comparison submodule;

[0015] The comparison submodule is used to obtain the relationship ratio between the project and the external public data, and determine whether the relationship ratio of the project is greater than the ratio threshold. If it is greater, the project is retained; if it is less, the project is removed to obtain the collection of remaining projects, and the collection of projects is sent to the direction determination submodule;

[0016] The direction determination submodule is used to sort the collection of projects in order according to the relationship weight, and select the top three projects as the production direction of the enterprise;

[0017] The planning submodule is used to make corresponding production plans according to the production direction.

[0018] Preferably, the optimization module includes a model building submodule and an analysis and reconstruction submodule;

[0019] The model building submodule is used to analyze the production data to obtain the number of processes, the number of machines in the process, and the processing time of the process in the corresponding production plan; and to build a mathematical model of the minimum completion time based on the production data;

[0020] The analytical reconstruction submodule is used to reconstruct the order of production processes, obtain the result value of the mathematical model after the reconstructed production process, and determine whether the result value is less than the result value of the original process. If it is less, the reconstructed process is used as the optimization plan. If it is greater, the reconstruction operation is re-executed.

[0021] A business data processing and integration method based on a big data platform is applied to a business data processing and integration system based on a big data platform, and is characterized by comprising the following steps:

[0022] Receiving external data and internal enterprise data, wherein the external data includes external industry data and external public data, and the internal enterprise data includes production data and financial data;

[0023] Select production plans based on the relationship between industry data and external public data;

[0024] Optimize the production plan according to the production data to obtain the optimization plan;

[0025] Determine whether the optimization plan is feasible based on the economic benefits and financial data of the optimization plan. If feasible, present the optimization plan to the user in a visual manner;

[0026] If this is not feasible, reselect the production plan or replace the external data.

[0027] Preferably, before obtaining the optimization solution, the production data needs to be cleaned, wherein data cleaning is to analyze the missing parts in the production data and supplement the missing parts.

[0028] Preferably, the specific steps of selecting a production plan are as follows:

[0029] Determine the items that the enterprise can produce based on external industry data and the enterprise's production and business scope;

[0030] Obtain the relationship ratio between the project and external public data, and determine whether the relationship ratio of the project is greater than the ratio threshold. If so, retain the project; if less, remove the project to obtain a collection of remaining projects.

[0031] Arrange the collection of projects in order according to the relationship ratio, and select the top three projects as the production direction of the enterprise;

[0032] Make corresponding production plans according to the production direction.

[0033] Preferably, the specific steps of obtaining the optimization solution are as follows: parsing the production data to obtain the number of processes, the number of machines in the process, and the processing time of the process in the corresponding production plan; and constructing a mathematical model of the minimum completion time based on the production data;

[0034] Reconstruct the order of production processes, obtain the result value of the mathematical model after the reconstruction of the production process, and determine whether the result value is less than the result value of the original process. If it is less, the reconstructed process is used as the optimization plan. If it is greater, re-execute the reconstruction operation.

[0035] Preferably, the specific steps of reconstructing the order of the production process are as follows:

[0036] Step A: Sort all processes from largest to smallest processing time;

[0037] Step B: Construct an empty processing sequence, randomly insert the processes into the processing sequence according to the order of size, and obtain the initial sequence;

[0038] Step C: Input the initial sequence into the mathematical model to obtain the corresponding result value, and determine whether the result value is less than the result value of the original production process sequence. If so, retain the initial sequence; if so, re-execute step B.

[0039] Step D: Eliminate the processes with a random position and a number of processes x in the initial sequence, where x is greater than 1 and less than n, and n is the total number of processes, and keep the eliminated processes in the removal sequence;

[0040] Step E: The process of removing the sequence is inserted into the damaged initial sequence in the order of elimination;

[0041] When the process sequence of the removed sequence is inserted, the process of the removed sequence is inserted into all insertable positions of the damaged initial sequence, and all new subsequences of the damaged initial sequence are obtained, and the new subsequences are sorted into the mathematical model to obtain the optimal result value, so as to obtain an updated sequence;

[0042] Step F: Determine whether the result value of the update sequence is less than the result value of the previous update sequence or the initial sequence. If so, retain the current update sequence; if so, retain the previous update sequence or the initial sequence.

[0043] Step G: Repeat steps D to F until the number of iterations is greater than the threshold, stop the operation, and use the last retained update sequence or initial sequence as the optimization solution.

[0044] Preferably, before executing step F, the following steps need to be executed:

[0045] Identify the update sequence or the initial sequence, obtain the process that can reduce the maximum completion time, and mark it as the first process;

[0046] Determine whether there are other processes before and after the first process. If so, mark the processes before and after as the second process, and swap the first process with the second process to obtain a modified sequence or a collection of sequences to be modified.

[0047] Determine whether the result value of the modified sequence or the set of sequences to be modified in the mathematical model is greater than the update sequence or the initial sequence. If so, update the modified sequence to the update sequence, or update the update sequence with the smallest result value in the set of modified sequences to the update sequence. If so, retain the original update sequence.

[0048] One of the above technical solutions has the following advantages or beneficial effects: The present invention obtains different data through a big data platform, and the diversified data sources help enterprises to more comprehensively understand market trends, industry dynamics and their own operating conditions, and provide more comprehensive and accurate data support for decision-making. At the same time, the present invention involves the data and decision-making process of multiple departments such as production and finance, which helps to promote information sharing and coordination between departments, avoid the emergence of information islands, and prevent the effective production and sales of enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 The present invention is a structural diagram of an embodiment of a business data processing and integration system based on a big data platform.

[0050] Figure 2 The invention is a flowchart of a business data processing and integration method based on a big data platform. DETAILED DESCRIPTION

[0051] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals represent the same or similar elements or elements having the same or similar functions from beginning to end. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0052] In the description of the embodiments of the present invention, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0053] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, the meaning of "plurality" is two or more. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0054] like Figures 1-2 As shown, a business data processing and integration system based on a big data platform includes:

[0055] A first data receiving module is used to receive external data, wherein the external data includes external industry data and external public data, and send the received external data to the decision module;

[0056] A second data receiving module is used to receive internal enterprise data, wherein the internal enterprise data includes production data and financial data, and send the received production data to the optimization module, and send the received financial data to the judgment module;

[0057] The decision module is used to select the production plan based on the relationship between industry data and external public data, and send the production plan to the optimization module;

[0058] The optimization module is used to optimize the production plan according to the production data, obtain the optimization plan, and send the optimization plan to the judgment module;

[0059] A judgment module is used to judge whether the optimization scheme is feasible based on the economic benefits and financial data of the optimization scheme. If it is feasible, it is sent to the decision module. If it is not feasible, the production plan is reselected or the external data is replaced;

[0060] The decision module is used to present the optimization plan to the user in a visual manner.

[0061] The external industry data includes the sales data, profit data, time data, etc. of the industry. The external public data can be the data that affects the sales data, profit data, etc. of the industry. For example, in a clothing company, users can receive external industry data about clothing and external public data about weather. If in a candy company, users can choose to receive external industry data about candy, and the external public data can be the user's taste data, holiday sales data, etc., which will not be explained in detail here.

[0062] The production data in the internal data of an enterprise may be the data on processes, machinery, etc. that the enterprise needs to use when producing and selling a product, while the financial data may be the funds or liquid funds that the enterprise currently has available for production.

[0063] After obtaining the above external data and internal enterprise data, the decision module will select a production plan based on the relationship between industry data and external public data. For example, in a candy company, in the external public data, the proportion of the impact of festival data on candy sales at the current time point accounts for 70%, and the proportion of the impact of taste data on candy sales accounts for 20%. At this time, a production plan related to festival candy will be selected.

[0064] However, at this time, the production plan is only a simple decision, and is not combined with the factors of the production machinery and process of the enterprise. Therefore, an optimization module is also provided in the present invention, and the production plan can be converted into a specific production plan (optimization plan) through the optimization module. Then, the user of the enterprise can input the expenditure data such as the production cost and advertising expenses of the commodity and the income data of the sales amount into the judgment module to obtain the economic benefit according to the optimization plan, and then judge the size of the economic benefit and the financial data. If the economic benefit is greater than the financial data, it means that the enterprise can make money within a certain period of time, and if it is less than, it means that the current finance cannot support the enterprise to generate and sell the commodity. Finally, the decision module will present the optimization plan to the user in a visual way, and the user of the enterprise can choose the production plan according to their own situation.

[0065] The present invention obtains different data through a big data platform. The diversified data sources help enterprises to have a more comprehensive understanding of market trends, industry dynamics and their own operating conditions, and provide more comprehensive and accurate data support for decision-making. At the same time, the present invention involves the data and decision-making process of multiple departments such as production and finance, which helps to promote information sharing and coordination between departments, avoid the emergence of information islands, and prevent the effective production and sales of enterprises.

[0066] Preferably, it further comprises a data cleaning module, and before executing the optimization module, the production data is sent to the data cleaning module, and the data cleaning module is used to parse the missing parts in the production data and supplement the missing parts.

[0067] Because during optimization, production data will affect the generation of optimization solutions. When part of the production data is missing, it may cause a process in the optimization solution to be missing, which will make the optimization solution impossible to implement. Therefore, it is necessary to supplement the missing part of the production data before executing the optimization solution to ensure the integrity of the production data.

[0068] Preferably, the decision module includes a determination submodule, a comparison submodule, a direction determination submodule and a planning submodule;

[0069] The determination submodule is used to determine the items that the enterprise can produce based on external industry data and the production and business scope of the enterprise, and send the items to the comparison submodule;

[0070] Taking candy production as an example, the production and business scope of the enterprise is food processing related to candy, and the external industry data obtained include external industry data of candy processing, external industry data of egg processing, and external industry data of sugar substitutes. At this time, the external industry data of candy processing is the most consistent with the production and business scope of the enterprise, so it is confirmed that the enterprise produces candy and the project of producing candy is obtained.

[0071] The comparison submodule is used to obtain the relationship ratio between the project and the external public data, and determine whether the relationship ratio of the project is greater than the ratio threshold. If it is greater, the project is retained; if it is less, the project is removed to obtain the collection of remaining projects, and the collection of projects is sent to the direction determination submodule;

[0072] The direction determination submodule is used to sort the collection of projects in order according to the relationship weight, and select the top three projects as the production direction of the enterprise;

[0073] The planning submodule is used to make corresponding production plans according to the production direction.

[0074] If the external public data for candy includes taste data, festival time point data, etc., in the current time period, the proportion of the impact of festival data on candy sales at the current time point accounts for 70%, and the proportion of the impact of taste data on candy sales accounts for 20%, if the proportion threshold is set to 30%, only the festival data is more likely to be retained.

[0075] At this time, the direction determination submodule will determine the production direction of the enterprise to be the production of holiday candies, and finally the planning submodule will determine the production plan.

[0076] The present invention uses external industry data and the production and business scope of the enterprise itself to determine production projects. This decision-making method based on big data can more accurately reflect market trends and consumer demand, reduce errors in subjective judgment, and improve the scientificity and accuracy of decision-making.

[0077] At the same time, by comparing the sub-modules to evaluate the relationship between the project and external public data, it can effectively screen out projects that are closely related to market demand and have development potential. This screening mechanism ensures that corporate resources can be concentrated on the most valuable projects and avoid waste of resources.

[0078] Preferably, the optimization module includes a model building submodule and an analysis and reconstruction submodule;

[0079] The model building submodule is used to analyze the production data to obtain the number of processes, the number of machines in the process, and the processing time of the process in the corresponding production plan; and to build a mathematical model of the minimum completion time based on the production data;

[0080] The analytical reconstruction submodule is used to reconstruct the order of production processes, obtain the result value of the mathematical model after the reconstructed production process, and determine whether the result value is less than the result value of the original process. If it is less, the reconstructed process is used as the optimization plan. If it is greater, the reconstruction operation is re-executed.

[0081] When the production plan is determined, the optimization module will convert the production plan into a specific production plan. At this time, a mathematical model with the minimum completion time as the goal will be constructed. Since the processes, time, and machinery used in different production plans are different, we will not go into too much detail here. However, there are still certain limitations on the data model, and the limitations are as follows:

[0082]

[0083] C ij ≥C i-1,j +P ij --Formula (2);

[0084] C ij ≥C il +P ij -M*(3-X ijl -Y ikj -Y ikl )--Formula (3);

[0085] C il ≥C ij +P il -M*X ijl -*(2-Y ikj -Y ikl )--Formula (4);

[0086] C max ≥C mj --Formula (5);

[0087] C ij ≥0--Formula (6);

[0088] X ijl ∈{0,1}--Formula (7);

[0089] Y ikj ∈{0,1}--Formula (8);

[0090] Among them, C max is a mathematical model, l is an arbitrary integer, n is the number of products, j is the product index, j∈{1,..l,..,n}, m is the number of stages, i is the stage index, i∈{1,..l,..,m}, m i is the number of machines in stage i, k is the machine index, k∈{1,..l,..,m i}, P ij represents the processing time of product j in stage i, C ij is the completion time of the process of commodity j at stage i, Y ikj It means that if commodity j is processed on machine k at stage i, then Y ikj=1, otherwise Y ikj j=0,X ijl It means that if commodity j is processed after commodity l at stage i, then Y ikj =1, otherwise Y ikj = 0. Wherein formula (1) is used to stipulate that each commodity can only be processed once on any machine in each stage, formula (3), formula (4) and formula (2) are used to calculate the completion time of the operation, formula (5) is used for the line mathematical model, formula (7), formula (8) and formula (6) are used to limit the feasible domain of variables, and M is a maximum value.

[0091] Generally, the order of production processes for a certain product is set manually. However, there is still room for optimization in the order of production processes. After optimization, the completion time of a product can be shortened, thereby reducing the time cost of production for the enterprise and producing more products in the same time. For this purpose, a parsing and reconstruction submodule is set up to reconstruct the order of the original production process through the parsing and reconstruction submodule, so as to obtain an optimization plan and improve the production efficiency of the enterprise.

[0092] A business data processing and integration method based on a big data platform, applied to a business data processing and integration system based on a big data platform, comprises the following steps:

[0093] Receiving external data and internal enterprise data, wherein the external data includes external industry data and external public data, and the internal enterprise data includes production data and financial data;

[0094] Select production plans based on the relationship between industry data and external public data;

[0095] Optimize the production plan according to the production data to obtain the optimization plan;

[0096] Determine whether the optimization plan is feasible based on the economic benefits and financial data of the optimization plan. If feasible, present the optimization plan to the user in a visual manner;

[0097] If this is not feasible, reselect the production plan or replace the external data.

[0098] Preferably, before obtaining the optimization solution, the production data needs to be cleaned, wherein data cleaning is to analyze the missing parts in the production data and supplement the missing parts.

[0099] Preferably, the specific steps of selecting a production plan are as follows:

[0100] Determine the items that the enterprise can produce based on external industry data and the enterprise's production and business scope;

[0101] Obtain the relationship ratio between the project and external public data, and determine whether the relationship ratio of the project is greater than the ratio threshold. If so, retain the project; if less, remove the project to obtain a collection of remaining projects.

[0102] Arrange the collection of projects in order according to the relationship ratio, and select the top three projects as the production direction of the enterprise;

[0103] Make corresponding production plans according to the production direction.

[0104] Preferably, the specific steps of obtaining the optimization solution are as follows: parsing the production data to obtain the number of processes, the number of machines in the process, and the processing time of the process in the corresponding production plan; and constructing a mathematical model of the minimum completion time based on the production data;

[0105] Reconstruct the order of production processes, obtain the result value of the mathematical model after the reconstruction of the production process, and determine whether the result value is less than the result value of the original process. If it is less, the reconstructed process is used as the optimization plan. If it is greater, re-execute the reconstruction operation.

[0106] Preferably, the specific steps of reconstructing the order of the production process are as follows:

[0107] Step A: Sort all processes from largest to smallest processing time;

[0108] Step B: Construct an empty processing sequence, randomly insert the processes into the processing sequence according to the order of size, and obtain the initial sequence;

[0109] Step C: Input the initial sequence into the mathematical model to obtain the corresponding result value, and determine whether the result value is less than the result value of the original production process sequence. If so, retain the initial sequence; if so, re-execute step B.

[0110] Step D: Eliminate the processes with a random position and a number of processes x in the initial sequence, where x is greater than 1 and less than n, and n is the total number of processes, and keep the eliminated processes in the removal sequence;

[0111] Step E: The process of removing the sequence is inserted into the damaged initial sequence in the order of elimination;

[0112] When the process sequence of the removed sequence is inserted, the process of the removed sequence is inserted into all insertable positions of the damaged initial sequence, and all new subsequences of the damaged initial sequence are obtained, and the new subsequences are sorted into the mathematical model to obtain the optimal result value, so as to obtain an updated sequence;

[0113] Step F: Determine whether the result value of the update sequence is less than the result value of the previous update sequence or the initial sequence. If so, retain the current update sequence; if so, retain the previous update sequence or the initial sequence.

[0114] Step G: Repeat steps D to F until the number of iterations is greater than the threshold, stop the operation, and use the last retained update sequence or initial sequence as the optimization solution.

[0115] Assume that the initial sequence is A→B→C→D→E→F, which meets the constraints of the mathematical model. At this time, the number of processes is selected to be 3 for process elimination. After elimination, the damaged initial sequence is A→B→F, and the C, D, and E processes are retained in the removed sequence. Then, the C, D, and E processes are sequentially inserted into A→B→F, and four new subsequences of C→D→E→A→B→F, A→C→D→E→B→F, A→B→C→D→E→F, and A→B→F→C→D→E are obtained. If the result value of C→D→E→A→B→F in the mathematical model is the smallest, then C→D→E→A→B→F is used as the update sequence; at this time, since the result value of C→D→E→A→B→F is less than that of A→B→C→D→E→F, C→D→E→A→B→F is retained. Continue to repeat steps D to F until the number of iterations is greater than the threshold. The last update sequence is still C→D→E→A→B→F, so C→D→E→A→B→F is used as the optimization solution.

[0116] The present invention explores the neighborhood space of the initial sequence. In step E, the removed process is attempted to be inserted into all possible insertable positions, and all possible new subsequences are generated for evaluation. This comprehensive search significantly increases the possibility of finding a global optimal solution. This provides an efficient and adaptable solution to the production process sequencing problem.

[0117] Preferably, before executing step F, the following steps need to be executed:

[0118] Identify the update sequence or the initial sequence, obtain the process that can reduce the maximum completion time, and mark it as the first process;

[0119] Determine whether there are other processes before and after the first process. If so, mark the processes before and after as the second process, and swap the first process with the second process to obtain a modified sequence or a collection of sequences to be modified.

[0120] Determine whether the result value of the modified sequence or the set of sequences to be modified in the mathematical model is greater than the update sequence or the initial sequence. If so, update the modified sequence to the update sequence, or update the update sequence with the smallest result value in the set of modified sequences to the update sequence. If so, retain the original update sequence.

[0121] In order to further improve the update sequence for searching the global optimal solution, a local change is added in one embodiment of the present invention, in which the first process is used for local change. Since the first process is the process for reducing the maximum completion time, the bottleneck or key link in the production process is directly optimized. This targeted optimization strategy can more effectively improve the overall production efficiency. After the exchange, the optimization effect that may be brought about by adjusting the position of the first process can be explored. At the same time, because only the first process and the second process need to be exchanged, the other processes remain unchanged. When calculating the result value of the mathematical model, only the section after the first process and the second process are exchanged can be calculated. To reduce unnecessary calculations.

[0122] At the same time, each iteration is improved based on the previous result. This iterative improvement mechanism helps to gradually approach the global optimal solution or satisfactory solution.

[0123] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0124] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

Claims

1. A business data processing and integration system based on a big data platform, characterized in that: include: A first data receiving module is used to receive external data, wherein the external data includes external industry data and external public data, and send the received external data to the decision module; A second data receiving module is used to receive internal enterprise data, wherein the internal enterprise data includes production data and financial data, and send the received production data to the optimization module, and send the received financial data to the judgment module; The decision module is used to select the production plan based on the relationship between industry data and external public data, and send the production plan to the optimization module; The optimization module is used to optimize the production plan according to the production data, obtain the optimization plan, and send the optimization plan to the judgment module; A judgment module is used to judge whether the optimization scheme is feasible based on the economic benefits and financial data of the optimization scheme. If it is feasible, it is sent to the decision module. If it is not feasible, the production plan is reselected or the external data is replaced; The decision module is used to present the optimization plan to the user in a visual manner.

2. According to claim 1, a business data processing and integration system based on a big data platform is characterized in that: It also includes a data cleaning module. Before executing the optimization module, the production data is sent to the data cleaning module. The data cleaning module is used to parse the missing parts in the production data and supplement the missing parts.

3. The business data processing and integration system based on a big data platform according to claim 1 is characterized in that: The decision module includes a determination submodule, a comparison submodule, a direction determination submodule, and a planning submodule; The determination submodule is used to determine the items that the enterprise can produce based on external industry data and the production and business scope of the enterprise, and send the items to the comparison submodule; The comparison submodule is used to obtain the relationship ratio between the project and the external public data, and determine whether the relationship ratio of the project is greater than the ratio threshold. If it is greater, the project is retained; if it is less, the project is removed to obtain the collection of remaining projects, and the collection of projects is sent to the direction determination submodule; The direction determination submodule is used to sort the collection of projects in order according to the relationship weight, and select the top three projects as the production direction of the enterprise; The planning submodule is used to make corresponding production plans according to the production direction.

4. The business data processing and integration system based on a big data platform according to claim 1 is characterized in that: The optimization module includes a model building submodule and an analysis and reconstruction submodule; The model building submodule is used to analyze the production data to obtain the number of processes, the number of machines in the process, and the processing time of the process in the corresponding production plan; and to build a mathematical model of the minimum completion time based on the production data; The analytical reconstruction submodule is used to reconstruct the order of production processes, obtain the result value of the mathematical model after the reconstructed production process, and determine whether the result value is less than the result value of the original process. If it is less, the reconstructed process is used as the optimization plan. If it is greater, the reconstruction operation is re-executed.

5. A business data processing and integration method based on a big data platform, applied to a business data processing and integration system based on a big data platform as claimed in any one of claims 1 to 4, characterized in that: The steps include: Receiving external data and internal enterprise data, wherein the external data includes external industry data and external public data, and the internal enterprise data includes production data and financial data; Select production plans based on the relationship between industry data and external public data; Optimize the production plan according to the production data to obtain the optimization plan; Determine whether the optimization plan is feasible based on the economic benefits and financial data of the optimization plan. If feasible, present the optimization plan to the user in a visual manner; If this is not feasible, reselect the production plan or replace the external data.

6. A business data processing and integration method based on a big data platform according to claim 5, characterized in that: Before obtaining the optimization plan, the production data needs to be cleaned, where data cleaning is to analyze the missing parts in the production data and supplement the missing parts.

7. The business data processing and integration method based on a big data platform according to claim 5 is characterized in that: The specific steps for selecting a production plan are as follows: Determine the items that the enterprise can produce based on external industry data and the enterprise's production and business scope; Obtain the relationship ratio between the project and external public data, and determine whether the relationship ratio of the project is greater than the ratio threshold. If so, retain the project; if less, remove the project to obtain a collection of remaining projects. Arrange the collection of projects in order according to the relationship ratio, and select the top three projects as the production direction of the enterprise; Make corresponding production plans according to the production direction.

8. The business data processing and integration method based on a big data platform according to claim 5 is characterized in that: The specific steps of obtaining the optimization solution are as follows: parsing the production data to obtain the number of processes, the number of machines in the process, and the processing time of the process in the corresponding production plan; and constructing a mathematical model of the minimum completion time based on the production data; Reconstruct the order of production processes, obtain the result value of the mathematical model after the reconstruction of the production process, and determine whether the result value is less than the result value of the original process. If it is less, the reconstructed process is used as the optimization plan. If it is greater, re-execute the reconstruction operation.

9. The business data processing and integration method based on a big data platform according to claim 8 is characterized in that: The specific steps to reconstruct the order of production processes are as follows: Step A: Sort all processes from largest to smallest processing time; Step B: Construct an empty processing sequence, randomly insert the processes into the processing sequence according to the order of size, and obtain the initial sequence; Step C: Input the initial sequence into the mathematical model to obtain the corresponding result value, and determine whether the result value is less than the result value of the original production process sequence. If so, retain the initial sequence; if so, re-execute step B. Step D: Eliminate the processes with a random position and a number of processes x in the initial sequence, where x is greater than 1 and less than n, and n is the total number of processes, and keep the eliminated processes in the removal sequence; Step E: The process of removing the sequence is inserted into the damaged initial sequence in the order of elimination; When the process sequence of the removed sequence is inserted, the process of the removed sequence is inserted into all insertable positions of the damaged initial sequence, and all new subsequences of the damaged initial sequence are obtained, and the new subsequences are sorted into the mathematical model to obtain the optimal result value, so as to obtain an updated sequence; Step F: Determine whether the result value of the update sequence is less than the result value of the previous update sequence or the initial sequence. If so, retain the current update sequence; if so, retain the previous update sequence or the initial sequence. Step G: Repeat steps D to F until the number of iterations is greater than the threshold, stop the operation, and use the last retained update sequence or initial sequence as the optimization solution.

10. A business data processing and integration method based on a big data platform according to claim 9, characterized in that: Before executing step F, you also need to perform the following steps: Identify the update sequence or the initial sequence, obtain the process that can reduce the maximum completion time, and mark it as the first process; Determine whether there are other processes before and after the first process. If so, mark the processes before and after as the second process, and swap the first process with the second process to obtain a modified sequence or a collection of sequences to be modified. Determine whether the result value of the modified sequence or the set of sequences to be modified in the mathematical model is greater than the update sequence or the initial sequence. If so, update the modified sequence to the update sequence, or update the update sequence with the smallest result value in the set of modified sequences to the update sequence. If so, retain the original update sequence.

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