Industrial data management control system and method for furniture customization

Abstract

The invention relates to the technical field of data management, in particular to an industrial data management control system and method for furniture customization, and the system comprises a pre-selected scheme preliminary making module, a scheme multi-source data reasonability judgment module, a scheme evaluation and screening module, and an early warning signal generation and processing module. The scheme multi-source data rationality judgment module is used for performing periodic processing test on different product prediction assembly schemes in sequence, analyzing product qualification conditions by combining corresponding periodic processing test reports, and calculating comprehensive benefit conditions of corresponding products according to analysis results; according to the method, the benefit condition generated by the combination scheme of different parts in the product production in the to-be-monitored area is analyzed and obtained, the optimal process manufacturing scheme is generated in combination with the benefit condition, and the optimal process manufacturing scheme is calibrated in real time in combination with the corresponding product demand list, so that the production efficiency is improved, the product production flexibility is improved, and the product quality is improved. And maximization of the production benefit of the product is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to an industrial data management control system and method for furniture customization. Background Art

[0002] Home customization is a service model that provides exclusive, integrated furniture products to meet users' individual needs through professional design, custom manufacturing, and precise installation. Currently, the production process for customized furniture involves assembling furniture parts according to pre-set instructions, achieving automated production. This process fails to optimize production plans based on user needs, hindering further product efficiency. Therefore, an industrial data management and control system and method for customized furniture are needed. Summary of the Invention

[0003] The purpose of the present invention is to provide an industrial data management control system and method for furniture customization to solve the problems raised in the above background technology. The present invention provides the following technical solutions: An industrial data management and control method for furniture customization, the method comprising the following steps: S1. Obtain the production process steps of products in the area to be monitored, extract the corresponding spare parts procurement list in the production process steps, and generate a product prediction assembly plan based on the production process steps of products in the area to be monitored; S2. Conduct periodic processing tests on the predicted assembly plans of different products in turn, analyze the product qualification status based on the corresponding periodic processing test reports, and calculate the comprehensive benefits of the corresponding products based on the analysis results; S3. Generate a prediction assembly solution evaluation model based on the comprehensive product benefit analysis results, set a priority sequence for the corresponding product process manufacturing solutions in the monitored area using the generated prediction assembly solution evaluation model, and generate an optimal process manufacturing solution based on the priority sequence; S4. Analyze the rationality of the optimal process manufacturing plan based on the corresponding product demand list of the monitored area, generate early warning signals based on the analysis results, and take corresponding measures to eliminate the alarm according to the corresponding early warning signals.

[0004] Furthermore, the method in S1 includes the following steps: Step 1001: Obtain the production process steps of the products in the monitored area, extract the corresponding spare parts purchase list in the production process steps, and record it as set A. , in represents the supplier set of the nth part in the corresponding spare parts purchase list, and n represents the total number of parts types in the corresponding spare parts purchase list in the production process. Among the subsets in set A , Indicates the spare parts provided by the mth supplier in the supplier set of the nth part in the corresponding spare parts procurement list, where m represents the total number of suppliers of the corresponding spare parts; Step 1002: extract any elements from each subset of set A in sequence, bundle them together, and generate a product prediction assembly plan based on the production process of the products in the monitored area, which is recorded as set B. , in It represents the predicted assembly plan of the i-th product, i represents the total number of predicted assembly plans for the product, the combination plans between various parts, and each subset element only confirms the parts provided by one supplier.

[0005] The present invention obtains the production process steps of products in the monitored area, extracts the purchase list of corresponding spare parts in the production process steps, and combines the spare parts provided by different suppliers, thereby providing data reference for subsequent analysis of the qualification and efficiency of manufactured products under different combination schemes.

[0006] Furthermore, the method in S2 includes the following steps: Step 2001: Extract the predicted assembly plan for the i-th product in step 1002, and perform a periodic processing test based on the product predicted assembly plan. Analyze the product qualification rate under the i-th product predicted assembly plan based on the periodic processing test report, and record it as , , in It represents the analysis value of the ath product in the jth quality inspection link in the cycle processing test report, and u represents the total number of products in the cycle processing test report. Indicates the total number of quality inspection links in the cycle processing test report. It represents the sum of the reference values ​​in each quality inspection link in the periodic processing test report. The reference value is a preset value in the database, where the corresponding quality inspection data of the corresponding product in the periodic processing test report is obtained through the corresponding tests in the quality inspection link; Step 2002: Based on the analysis results of step 2001, calculate the comprehensive benefits of the product under the predicted assembly plan of the i-th product, which is recorded as , , in 、 as well as Both represent proportional coefficients, which are preset values ​​in the database. represents the cost price of the vth component purchased in the predicted assembly plan for the i-th product, and w represents the total number of parts purchased in the predicted assembly plan for the i-th product. represents the predicted assembly solution test completion time for product i; Step 2003: Loop step 2001 to step 2002 to obtain the comprehensive benefits of different product prediction assembly plans, and record the analysis results in table M.

[0007] The present invention analyzes the qualified rates of different product prediction assembly plans and the comprehensive benefits of the corresponding product prediction assembly plans, thereby providing data reference for subsequent evaluation of different product prediction assembly plans and generating the best process manufacturing plan based on the evaluation results.

[0008] Furthermore, the method in S3 includes the following steps: Step 3001: Extract the data information in Table M, generate a forecast assembly plan evaluation model based on the comprehensive benefit analysis results of the product, and record the comprehensive benefit evaluation of the product under the forecast assembly plan of the i-th product as , , like When , it means that the predicted assembly plan for product i is reasonable. like When , it means that the predicted assembly plan of product i is unreasonable, where Indicates the database default value; Step 3002: loop step 3001 to obtain comprehensive benefit evaluation of products under various product forecast assembly plans, eliminate unreasonable product forecast assembly plans in table M, calibrate the data information in table M, and generate a calibrated table M, which is recorded as table ; Step 3003: Extract table Chinese data information, formulate a comprehensive benefit evaluation mechanism, With point o as the origin, the product forecast assembly plan serial number as the x-axis, and the comprehensive benefit situation as the y-axis, a first plane rectangular coordinate system is constructed. In the first plane rectangular coordinate system, the coordinate points corresponding to the comprehensive benefit situation of different product forecast assembly plans are marked. In the first plane rectangular coordinate system, mark the line where y=0 is located, take the origin o as the starting point, translate the line upward along the y axis, mark the lines corresponding to the intersections between the line and the marked points, and generate a sequence F based on the marking order. , in Indicates that the line and the marked point intersect. straight line, Indicates the number of marked straight lines; Step 3004: extract the elements in sequence F in step 3003 where the number of intersections between the straight line and the marked point is greater than 1, and combine the product forecast assembly plans corresponding to the corresponding marked points, and mark the product forecast assembly plans in ascending order of completion time; Step 3005: Combine sequence F and the analysis result of step 3004 to generate a priority sequence, recorded as sequence D, wherein the corresponding product prediction assembly solutions are sorted according to the sequence F. When the number of intersections between the straight lines and the marked points in sequence F is greater than 1, the product prediction assembly solutions corresponding to the judgment results are further sorted; Step 3006: Obtain sequence D in step 3005, and use the first element in sequence D as the optimal process manufacturing solution for the area to be monitored.

[0009] The present invention screens out suitable assembly plans through the comprehensive benefit calculation results of different product predicted assembly plans, evaluates the screening results, and generates a priority sequence of corresponding product process manufacturing plans in the monitored area based on the evaluation results, thereby providing data reference for subsequent determination of the rationality of the priority sequence based on order demand conditions.

[0010] Furthermore, the method in S4 includes the following steps: Step 4001: Obtain order demand information for corresponding products in the monitored area, extract a set of characteristic data information in the order demand information, and record it as set E. The characteristic data information is a preset value in the database. , in represents the vth feature data information in the order demand information, v represents the total number of feature data information in the order demand information, and different elements in the set E correspond to different feature data information; Step 4002: Extract historical order demand information of user S in the monitored area, and set the optimal judgment condition value based on the historical order demand information, which is recorded as , judge the rationality of the optimal process manufacturing solution in step 3006, and generate an early warning signal based on the judgment result, and record the judgment result of the rationality of user S matching the optimal process manufacturing solution as , , Where H() represents the judgment function, Indicates the optimal process manufacturing solution based on the conditions The corresponding value, Indicates that user S is based on the condition The corresponding value, , G() represents the mode statistical function, where Equivalent to the characteristic data information in the historical order information of user S The total number of like ,but , , like ,but , ; Step 4003: Based on the analysis in step 4002, When , it indicates that the current optimal process manufacturing plan has an abnormality, and an early warning signal is issued. The second element in sequence D is used as the new optimal process manufacturing plan, and the rationality is judged in step 4002. when When , it indicates that there is no abnormality in the current optimal process manufacturing plan, no warning signal is issued, and the production task is executed according to the optimal process manufacturing plan.

[0011] The present invention analyzes the rationality of the optimal process manufacturing plan by combining the corresponding product demand list of the monitored area, generates early warning signals based on the analysis results, and takes corresponding measures according to different early warning signals, thereby maximizing product benefits.

[0012] An industrial data management and control system for furniture customization, the system comprising the following modules: Preliminary formulation module for preselected plans: The module is used to obtain the production process steps of products in the area to be monitored, extract the corresponding spare parts procurement list in the production process steps, and generate a product prediction assembly plan based on the production process steps of products in the area to be monitored; Solution multi-source data rationality judgment module: The solution multi-source data rationality judgment module sequentially conducts periodic processing tests on different product predicted assembly solutions, analyzes product qualification status based on corresponding periodic processing test reports, and calculates the comprehensive benefits of corresponding products based on the analysis results; Scheme evaluation and screening module: The scheme evaluation and screening module is used to generate a forecast assembly scheme evaluation model based on the comprehensive benefit analysis results of the product, set the priority sequence of the corresponding product process manufacturing schemes in the monitored area based on the generated forecast assembly scheme evaluation model, and generate the optimal process manufacturing scheme based on the priority sequence; Early warning signal generation and processing module: The early warning signal generation and processing module is used to analyze the rationality of the optimal process manufacturing plan in combination with the corresponding product demand list of the monitored area, generate early warning signals based on the analysis results, and take corresponding measures to execute alarm elimination processing based on the corresponding early warning signals.

[0013] Furthermore, the pre-selection scheme preliminary formulation module includes a data acquisition unit and a data combination unit: The data acquisition unit is used to obtain the production process steps of products in the monitored area and extract the corresponding spare parts purchase list in the production process steps; The data combination unit is used to combine the analysis results of the data acquisition unit with the production process steps of the products in the monitored area to generate a product prediction assembly plan.

[0014] Furthermore, the scheme multi-source data rationality judgment module includes a combination scheme analysis unit, a scheme benefit calculation unit, and a calculation result recording unit: The combination scheme analysis unit is used to calculate the product qualification rate of the corresponding combination scheme based on the analysis result of the data combination unit; The scheme benefit calculation unit is used to calculate the comprehensive product benefit of the corresponding combination scheme in combination with the analysis results of the combination scheme analysis unit; The operation result recording unit is used to record the analysis results of the solution benefit calculation unit in real time.

[0015] Furthermore, the program evaluation and screening module includes a comprehensive benefit evaluation unit, a data calibration unit, an evaluation mechanism formulation unit, and a priority sequence generation unit: The comprehensive benefit evaluation unit is used to evaluate the analysis results of the operation result recording unit; The data calibration unit is used to calibrate the rationality of the combination scheme in combination with the analysis results of the comprehensive benefit evaluation unit; The evaluation mechanism formulation unit is used to formulate a comprehensive benefit evaluation mechanism based on the analysis results of the data calibration unit; The priority sequence generating unit is used to generate a priority sequence in combination with the analysis result of the evaluation mechanism formulating unit.

[0016] Furthermore, the warning signal generation and processing module includes a demand acquisition unit, a rationality determination unit, and a warning signal generation and cancellation unit: The demand acquisition unit is used to obtain order demand information of corresponding products in the monitored area and extract characteristic data information in the order demand information; The rationality determination unit is used to determine the rationality of the priority sequence generation unit in combination with the analysis result of the demand acquisition unit; The warning signal generation and alarm elimination unit is used to generate a warning signal in combination with the analysis result of the rationality judgment unit, and take corresponding measures to eliminate the warning signal in combination with different warning signals.

[0017] The present invention analyzes and obtains the benefits generated by the combination schemes of different spare parts in the production of products in the monitored area, generates the best process manufacturing plan based on the benefits, and analyzes the rationality of the best process manufacturing plan in combination with the corresponding product demand list. The best process manufacturing plan is adjusted in real time according to the analysis results, thereby not only maximizing the product production benefits, but also matching the best plan to different needs, thereby improving production efficiency and increasing the flexibility of product production. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flow chart of an industrial data management and control method for furniture customization according to the present invention. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0020] Example 1: Please refer to Figure 1 , in this embodiment: An industrial data management and control method for furniture customization, the method comprising the following steps: S1. Obtain the production process steps of products in the area to be monitored, extract the corresponding spare parts procurement list in the production process steps, and generate a product prediction assembly plan based on the production process steps of products in the area to be monitored; The method in S1 comprises the following steps: Step 1001: Obtain the production process steps of the products in the monitored area, extract the corresponding spare parts purchase list in the production process steps, and record it as set A. , in represents the supplier set of the nth part in the corresponding spare parts purchase list, and n represents the total number of parts types in the corresponding spare parts purchase list in the production process. Among the subsets in set A , Indicates the spare parts provided by the mth supplier in the supplier set of the nth part in the corresponding spare parts procurement list, where m represents the total number of suppliers of the corresponding spare parts; Step 1002: extract any elements from each subset of set A in sequence, bundle them together, and generate a product prediction assembly plan based on the production process of the products in the monitored area, which is recorded as set B. , in represents the predicted assembly plan for the i-th product, and i represents the total number of predicted assembly plans for the product.

[0021] S2. Conduct periodic processing tests on the predicted assembly plans of different products in turn, analyze the product qualification status based on the corresponding periodic processing test reports, and calculate the comprehensive benefits of the corresponding products based on the analysis results; The method in S2 comprises the following steps: Step 2001: Extract the predicted assembly plan for the i-th product in step 1002, and perform a periodic processing test based on the product predicted assembly plan. Analyze the product qualification rate under the i-th product predicted assembly plan based on the periodic processing test report, and record it as , , in It represents the analysis value of the ath product in the jth quality inspection link in the cycle processing test report, and u represents the total number of products in the cycle processing test report. Indicates the total number of quality inspection links in the cycle processing test report. It represents the sum of the reference values ​​in each quality inspection link in the periodic processing test report. The reference value is a preset value in the database, where the corresponding quality inspection data of the corresponding product in the periodic processing test report is obtained through the corresponding tests in the quality inspection link; Step 2002: Based on the analysis results of step 2001, calculate the comprehensive benefits of the product under the predicted assembly plan of the i-th product, which is recorded as , , in 、 as well as Both represent proportional coefficients, which are preset values ​​in the database. represents the cost price of the vth component purchased in the predicted assembly plan for the i-th product, and w represents the total number of parts purchased in the predicted assembly plan for the i-th product. represents the predicted assembly solution test completion time for product i; Step 2003: Loop step 2001 to step 2002 to obtain the comprehensive benefits of different product prediction assembly plans, and record the analysis results in table M.

[0022] S3. Generate a prediction assembly solution evaluation model based on the comprehensive product benefit analysis results, set a priority sequence for the corresponding product process manufacturing solutions in the monitored area using the generated prediction assembly solution evaluation model, and generate an optimal process manufacturing solution based on the priority sequence; The method in S3 comprises the following steps: Step 3001: Extract the data information in Table M, generate a forecast assembly plan evaluation model based on the comprehensive benefit analysis results of the product, and record the comprehensive benefit evaluation of the product under the forecast assembly plan of the i-th product as , , like When , it means that the predicted assembly plan for product i is reasonable. like When , it means that the predicted assembly plan of product i is unreasonable, where Indicates the database default value; Step 3002: loop step 3001 to obtain comprehensive benefit evaluation of products under various product forecast assembly plans, eliminate unreasonable product forecast assembly plans in table M, calibrate the data information in table M, and generate a calibrated table M, which is recorded as table ; Step 3003: Extract table Chinese data information, formulate a comprehensive benefit evaluation mechanism, With point o as the origin, the product forecast assembly plan serial number as the x-axis, and the comprehensive benefit situation as the y-axis, a first plane rectangular coordinate system is constructed. In the first plane rectangular coordinate system, the coordinate points corresponding to the comprehensive benefit situation of different product forecast assembly plans are marked. In the first plane rectangular coordinate system, mark the line where y=0 is located, take the origin o as the starting point, translate the line upward along the y axis, mark the lines corresponding to the intersections between the line and the marked points, and generate a sequence F based on the marking order. , in Indicates that the line and the marked point intersect. straight line, Indicates the number of marked straight lines; Step 3004: extract the elements in sequence F in step 3003 where the number of intersections between the straight line and the marked point is greater than 1, and combine the product forecast assembly plans corresponding to the corresponding marked points, and mark the product forecast assembly plans in ascending order of completion time; Step 3005: Combine sequence F and the analysis result of step 3004 to generate a priority sequence, which is recorded as sequence D; Step 3006: Obtain sequence D in step 3005, and use the first element in sequence D as the optimal process manufacturing solution for the area to be monitored.

[0023] S4. Analyze the rationality of the optimal process manufacturing plan based on the corresponding product demand list of the monitored area, generate early warning signals based on the analysis results, and take corresponding measures to eliminate the alarm according to the corresponding early warning signals.

[0024] The method in S4 comprises the following steps: Step 4001: Obtain order demand information for corresponding products in the monitored area, extract a set of characteristic data information in the order demand information, and record it as set E. The characteristic data information is a preset value in the database. , in represents the vth feature data information in the order demand information, v represents the total number of feature data information in the order demand information, and different elements in the set E correspond to different feature data information; Step 4002: Extract historical order demand information of user S in the monitored area, and set the optimal judgment condition value based on the historical order demand information, which is recorded as , judge the rationality of the optimal process manufacturing solution in step 3006, and generate an early warning signal based on the judgment result, and record the judgment result of the rationality of user S matching the optimal process manufacturing solution as , , Where H() represents the judgment function, Indicates the optimal process manufacturing solution based on the conditions The corresponding value, Indicates that user S is based on the condition The corresponding value, , G() represents the mode statistical function, like ,but , , like ,but , ; Step 4003: Based on the analysis in step 4002, When , it indicates that the current optimal process manufacturing plan has an abnormality, and an early warning signal is issued. The second element in sequence D is used as the new optimal process manufacturing plan, and the rationality is judged in step 4002. when When , it indicates that there is no abnormality in the current optimal process manufacturing plan, no warning signal is issued, and the production task is executed according to the optimal process manufacturing plan.

[0025] In this embodiment: an industrial data management and control system for furniture customization is disclosed, and the system is used to implement the specific solution content of the method.

[0026] Example 2: Assume that there are four predicted assembly plans for a product, which are respectively recorded as Plan A, Plan B, Plan C, and Plan D. The comprehensive benefit evaluation of the four plans is as follows: Plan A > Plan B = Plan C > Plan D. The predicted completion time of the product assembly plan in Plan B is longer than that in Plan B. Therefore, the priority sequence of the four plans is [Plan A, Plan C, Plan B, Plan D]. Plan A is selected as the best process manufacturing plan. Extract the historical order demand information of user S in the monitored area. By extracting the mode of each feature data in the historical order demand information of user S, it is known that user S most hopes that the product production cycle can be delivered within 7 days. By querying the predicted assembly plan of the product, it is known that the production cycle of plan A is 8 days, the production cycle of plan B is 9 days, the production cycle of plan C is 6 days, and the production cycle of plan D is 8 days. Based on the best judgment condition of user S, it is known that plan A is unreasonable, and an early warning signal is issued, and plan C is used as the best process manufacturing plan for user S. After cyclic judgment, it is found that plan C is reasonable, and the system executes the production operation.

[0027] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

[0028] It should be noted that, in this document, relational terms such as first and second, etc., 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 that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0029] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. An industrial data management and control method for furniture customization, characterized in that: The method comprises the following steps: S1. Obtain the production process steps of products in the area to be monitored, extract the corresponding spare parts procurement list in the production process steps, and generate a product prediction assembly plan based on the production process steps of products in the area to be monitored; S2. Conduct periodic processing tests on the predicted assembly plans of different products in turn, analyze the product qualification status based on the corresponding periodic processing test reports, and calculate the comprehensive benefits of the corresponding products based on the analysis results; S3. Generate a prediction assembly solution evaluation model based on the comprehensive product benefit analysis results, set a priority sequence for the corresponding product process manufacturing solutions in the monitored area using the generated prediction assembly solution evaluation model, and generate an optimal process manufacturing solution based on the priority sequence; S4. Analyze the rationality of the optimal process manufacturing plan based on the corresponding product demand list of the monitored area, generate early warning signals based on the analysis results, and take corresponding measures to eliminate the alarm according to the corresponding early warning signals.

2. The industrial data management and control method for furniture customization according to claim 1, characterized in that: The method in S1 comprises the following steps: Step 1001: Obtain the production process steps of the products in the monitored area, extract the corresponding spare parts purchase list in the production process steps, and record it as set A. ， in represents the supplier set of the nth part in the corresponding spare parts purchase list, and n represents the total number of parts types in the corresponding spare parts purchase list in the production process. Among the subsets in set A , Indicates the spare parts provided by the mth supplier in the supplier set of the nth part in the corresponding spare parts procurement list, where m represents the total number of suppliers of the corresponding spare parts; Step 1002: extract any elements from each subset of set A in sequence, bundle them together, and generate a product prediction assembly plan based on the production process of the products in the monitored area, which is recorded as set B. ， in represents the predicted assembly plan for the i-th product, and i represents the total number of predicted assembly plans for the product.

3. The industrial data management and control method for furniture customization according to claim 2, characterized in that: The method in S2 comprises the following steps: Step 2001: Extract the predicted assembly plan for the i-th product in step 1002, and perform a periodic processing test based on the product predicted assembly plan. Analyze the product qualification rate under the i-th product predicted assembly plan based on the periodic processing test report, and record it as ; Step 2002: Based on the analysis results of step 2001, calculate the comprehensive benefits of the product under the predicted assembly plan of the i-th product, which is recorded as ; Step 2003: Loop step 2001 to step 2002 to obtain the comprehensive benefits of different product prediction assembly plans, and record the analysis results in table M.

4. The industrial data management and control method for furniture customization according to claim 3, characterized in that: The method in S3 comprises the following steps: Step 3001: Extract the data information in Table M, generate a forecast assembly plan evaluation model based on the comprehensive benefit analysis results of the product, and record the comprehensive benefit evaluation of the product under the forecast assembly plan of the i-th product as , ， like When , it means that the predicted assembly plan for product i is reasonable. like When , it means that the predicted assembly plan of product i is unreasonable, where Indicates the database default value; Step 3002: loop step 3001 to obtain comprehensive benefit evaluation of products under various product forecast assembly plans, eliminate unreasonable product forecast assembly plans in table M, calibrate the data information in table M, and generate a calibrated table M, which is recorded as table ; Step 3003: Extract table Chinese data information, formulate a comprehensive benefit evaluation mechanism, With point o as the origin, the product forecast assembly plan serial number as the x-axis, and the comprehensive benefit situation as the y-axis, a first plane rectangular coordinate system is constructed. In the first plane rectangular coordinate system, the coordinate points corresponding to the comprehensive benefit situation of different product forecast assembly plans are marked. In the first plane rectangular coordinate system, mark the line where y=0 is located, take the origin o as the starting point, translate the line upward along the y axis, mark the lines corresponding to the intersections between the line and the marked points, and generate a sequence F based on the marking order. ， in Indicates that the line and the marked point intersect. straight line, Indicates the number of marked straight lines; Step 3004: extract the elements in sequence F in step 3003 where the number of intersections between the straight line and the marked point is greater than 1, and combine the product forecast assembly plans corresponding to the corresponding marked points, and mark the product forecast assembly plans in ascending order of completion time; Step 3005: Combine sequence F and the analysis result of step 3004 to generate a priority sequence, which is recorded as sequence D; Step 3006: Obtain sequence D in step 3005, and use the first element in sequence D as the optimal process manufacturing solution for the area to be monitored.

5. The industrial data management and control method for furniture customization according to claim 4, characterized in that: The method in S4 comprises the following steps: Step 4001: Obtain order demand information for corresponding products in the monitored area, extract a set of characteristic data information in the order demand information, and record it as set E. The characteristic data information is a preset value in the database. ， in represents the vth feature data information in the order demand information, v represents the total number of feature data information in the order demand information, and different elements in the set E correspond to different feature data information; Step 4002: Extract historical order demand information of user S in the monitored area, and set the optimal judgment condition value based on the historical order demand information, which is recorded as , judge the rationality of the optimal process manufacturing solution in step 3006, and generate an early warning signal based on the judgment result, and record the judgment result of the rationality of user S matching the optimal process manufacturing solution as , ， Where H() represents the judgment function, Indicates the optimal process manufacturing solution based on the conditions The corresponding value, Indicates that user S is based on the condition The corresponding value, , G() represents the mode statistical function, like ,but , , like ,but , ; Step 4003: Based on the analysis in step 4002, When , it indicates that the current optimal process manufacturing plan has an abnormality, and an early warning signal is issued. The second element in sequence D is used as the new optimal process manufacturing plan, and the rationality is judged in step 4002. when When , it indicates that there is no abnormality in the current optimal process manufacturing plan, no warning signal is issued, and the production task is executed according to the optimal process manufacturing plan.

6. An industrial data management and control system for furniture customization, characterized in that: The system includes the following modules: Preliminary formulation module for preselected plans: The module is used to obtain the production process steps of products in the area to be monitored, extract the corresponding spare parts procurement list in the production process steps, and generate a product prediction assembly plan based on the production process steps of products in the area to be monitored; Solution multi-source data rationality judgment module: The solution multi-source data rationality judgment module sequentially conducts periodic processing tests on different product predicted assembly solutions, analyzes product qualification status based on corresponding periodic processing test reports, and calculates the comprehensive benefits of corresponding products based on the analysis results; Scheme evaluation and screening module: The scheme evaluation and screening module is used to generate a forecast assembly scheme evaluation model based on the comprehensive benefit analysis results of the product, set the priority sequence of the corresponding product process manufacturing schemes in the monitored area based on the generated forecast assembly scheme evaluation model, and generate the optimal process manufacturing scheme based on the priority sequence; Early warning signal generation and processing module: The early warning signal generation and processing module is used to analyze the rationality of the optimal process manufacturing plan in combination with the corresponding product demand list of the monitored area, generate early warning signals based on the analysis results, and take corresponding measures to execute alarm elimination processing based on the corresponding early warning signals.

7. The industrial data management and control system for furniture customization according to claim 6, characterized in that: The pre-selection scheme preliminary formulation module includes a data acquisition unit and a data combination unit: The data acquisition unit is used to obtain the production process steps of products in the monitored area and extract the corresponding spare parts purchase list in the production process steps; The data combination unit is used to combine the analysis results of the data acquisition unit with the production process steps of the products in the monitored area to generate a product prediction assembly plan.

8. The industrial data management and control system for furniture customization according to claim 7, characterized in that: The scheme multi-source data rationality judgment module includes a combination scheme analysis unit, a scheme benefit calculation unit, and a calculation result recording unit: The combination scheme analysis unit is used to calculate the product qualification rate of the corresponding combination scheme based on the analysis result of the data combination unit; The scheme benefit calculation unit is used to calculate the comprehensive product benefit of the corresponding combination scheme in combination with the analysis results of the combination scheme analysis unit; The operation result recording unit is used to record the analysis results of the solution benefit calculation unit in real time.

9. The industrial data management and control system for furniture customization according to claim 8, characterized in that: The program evaluation and screening module includes a comprehensive benefit evaluation unit, a data calibration unit, an evaluation mechanism formulation unit, and a priority sequence generation unit: The comprehensive benefit evaluation unit is used to evaluate the analysis results of the operation result recording unit; The data calibration unit is used to calibrate the rationality of the combination scheme in combination with the analysis results of the comprehensive benefit evaluation unit; The evaluation mechanism formulation unit is used to formulate a comprehensive benefit evaluation mechanism based on the analysis results of the data calibration unit; The priority sequence generating unit is used to generate a priority sequence in combination with the analysis result of the evaluation mechanism formulating unit.

10. The industrial data management and control system for furniture customization according to claim 9, characterized in that: The warning signal generation and processing module includes a demand acquisition unit, a rationality determination unit, and a warning signal generation and cancellation unit: The demand acquisition unit is used to obtain order demand information of corresponding products in the monitored area and extract characteristic data information in the order demand information; The rationality determination unit is used to determine the rationality of the priority sequence generation unit in combination with the analysis result of the demand acquisition unit; The warning signal generation and alarm elimination unit is used to generate a warning signal in combination with the analysis result of the rationality judgment unit, and take corresponding measures to eliminate the warning signal in combination with different warning signals.

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