An intelligent method, device, equipment and medium for production and delivery of prefabricated components

Abstract

The present invention relates to an intelligent method, device, equipment and medium for the production and delivery of prefabricated components, including: obtaining the target production index value of the component to be produced and generating a delivery order; screening multiple collaborative factories from the factory resource database and determining their actual production index values ​​for producing the component; using a fuzzy multi-criteria decision method to calculate the comprehensive evaluation value of the collaborative factory; determining an order allocation scheme based on the evaluation value, optimizing the allocation and production priority of the order through a genetic algorithm, and ensuring efficient production and on-time delivery. The method realizes intelligent scheduling and factory collaboration of prefabricated component production, and improves production efficiency and customer satisfaction.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an intelligent method, device, equipment and medium for producing and delivering prefabricated components. Background Art

[0002] In traditional construction and prefabricated component manufacturing, the coordinated management of production and distribution is a key challenge. Traditional construction usually adopts on-site casting or installation methods, relying on large-scale manual operations and time scheduling. However, as the construction industry's requirements for efficiency, cost and quality continue to increase, traditional construction methods have gradually exposed multiple problems, such as construction delays, resource waste, cost overruns, etc. Especially in the scenario of prefabricated component manufacturing, the production and distribution of each component need to be highly coordinated, otherwise it is easy to lead to uncertainty in project schedules and inefficient use of production resources.

[0003] In the production of traditional prefabricated components, different factories may undertake the production of multiple components for the same project, and these components usually need to be strictly matched in terms of quality standards, specifications and delivery time. However, the traditional production scheduling model usually relies on experience judgment or a single cost-driven allocation model, lacks a systematic method for quantifying production and delivery indicators, and leads to low scheduling efficiency. In addition, due to differences in equipment, technical level, raw materials and human resources among various factories, it is difficult to ensure the best combination of production and delivery based on experience alone, and it is easy to have problems such as insufficient capacity utilization of some factories or excessive distribution costs, which affects the progress and economic benefits of the entire project.

[0004] Therefore, traditional prefabricated component production and distribution management requires a more scientific, data-driven scheduling and evaluation method, which can optimize the allocation of production tasks and distribution arrangements for different factories through quantitative evaluation of various production and delivery indicators. Summary of the invention

[0005] The main purpose of the present invention is to provide an intelligent method and device for the production and delivery of prefabricated components, so as to solve the technical problems of low factory resource utilization efficiency, unintelligent production scheduling and unreasonable order allocation in the production process of prefabricated components.

[0006] To achieve the above-mentioned objectives, the present invention provides an intelligent method for the production and delivery of prefabricated components, comprising the following steps: obtaining the target production index value of the component to be produced, and generating a to-be-delivered order based on the target production index value of the component to be produced; determining multiple collaborative factories from a factory resource database based on the to-be-delivered order, and determining the actual production index value corresponding to the production of the component to be produced by each collaborative factory based on the factory resource database; calculating the index weight value of each of the target production index values ​​respectively, and combining the index weight value and the actual production index value, using a preset analysis method to calculate the comprehensive evaluation value of the production of the component to be produced by each of the collaborative factories; based on the comprehensive evaluation value, allocating the to-be-delivered order to the multiple collaborative factories, so that the collaborative factories can produce and deliver the components according to the allocated to-be-delivered orders.

[0007] The present invention also provides an intelligent device for the production and delivery of prefabricated components, including: an acquisition unit, used to acquire the target production index value of the component to be produced, and generate a to-be-delivered order based on the target production index value of the component to be produced; a determination unit, used to determine multiple collaborative factories from a factory resource database based on the to-be-delivered order, and determine the actual production index value corresponding to the production of the component to be produced by each collaborative factory based on the factory resource database; a calculation unit, used to respectively calculate the index weight value of each of the target production index values, and combine the index weight value and the actual production index value to calculate the comprehensive evaluation value of the production of the component to be produced by each collaborative factory using a preset analysis method; an allocation unit, used to allocate the to-be-delivered order to the multiple collaborative factories based on the comprehensive evaluation value, so that the collaborative factories can perform component production and delivery processing according to the allocated to-be-delivered orders.

[0008] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0009] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0010] The present invention provides an intelligent method and device for the production and delivery of prefabricated components. Through an order allocation strategy based on a comprehensive evaluation value, it achieves optimal production resource utilization of collaborative factories, intelligently allocates and produces orders, significantly improves the resource utilization rate of the production process, shortens the component production and delivery cycle, and improves the overall intelligence level of production scheduling and customer satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a schematic diagram of the steps of an intelligent method for producing and delivering prefabricated components in one embodiment of the present invention;

[0012] Figure 2 It is a structural block diagram of an intelligent device for production and delivery of prefabricated components in one embodiment of the present invention;

[0013] Figure 3 It is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0014] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0016] Reference Figure 1 The embodiment of the present invention provides an intelligent method for producing and delivering prefabricated components, comprising the following steps:

[0017] S1, obtaining a target production index value of a component to be produced, and generating a to-be-delivered order based on the target production index value of the component to be produced;

[0018] S2, determining a plurality of collaborative factories from a factory resource database based on the to-be-delivered order, and determining actual production index values ​​corresponding to the production of the to-be-produced components by each collaborative factory based on the factory resource database;

[0019] S3, respectively calculating the index weight value of each target production index value, and combining the index weight value and the actual production index value, using a preset analysis method to calculate the comprehensive evaluation value of each collaborative factory producing the component to be produced;

[0020] S4, allocating the to-be-delivered orders to the multiple collaborative factories based on the comprehensive evaluation values, so that the collaborative factories can perform component production and delivery processing according to the allocated to-be-delivered orders.

[0021] First, in step S1, obtaining the target production index value of the component to be produced is an important first step in implementing intelligent production scheduling. The target production index value contains the quality parameters of the component, such as strength, surface roughness, dimensional accuracy and material uniformity, which directly affect the quality and production process requirements of the component. For example, the strength requirement of a component to be produced is 50 MPa, the surface roughness requirement is 0.8 μm, and the dimensional accuracy requirement is ±0.1 mm. After obtaining these target production index values, orders to be delivered are generated according to these specific quality requirements, and target production indicators are attached to each order. These indicators will provide clear standards and basis for selecting appropriate factories and conducting production scheduling in subsequent steps.

[0022] Next, in step S2, based on the generated orders to be delivered, it is necessary to determine multiple collaborative factories from the factory resource database. The factory resource database contains all factory information that can participate in production, including the product types, production capacity data, production equipment status, human resource status, etc. produced by each factory. By extracting these factory resource data from the database, factories with production capacity can be preliminarily screened out. Specifically, based on the product type, production quantity, delivery cycle and other information in the orders to be delivered, the product type and production capacity of the factory are matched. In order to improve the accuracy of the matching, the present invention uses fuzzy set theory to fuzzify the matching degree. For example, the matching degree between the factory's product type and the order demand is divided into three levels: "unmatched", "partially matched" and "fully matched", and the fuzzy membership of each factory is calculated to describe the matching degree between the factory and the order.

[0023] At the same time, based on the data in the factory resource database, the actual production index values ​​corresponding to the components to be produced by each collaborative factory are determined. Here, the actual production index values ​​refer to the values ​​of various quality parameters of each factory when producing a specified component. For example, when a factory produced similar components in the past, its strength may have reached 48 MPa, the surface roughness was 0.9 μm, and the dimensional accuracy was ±0.2 mm. By obtaining these historical data and fuzzy processing them, it can provide important input data for the comprehensive evaluation of the production capacity of each factory in the subsequent steps.

[0024] In step S3, the weight of each target production index value is calculated, and combined with these index weight values ​​and the actual production index values ​​of each collaborative factory, a preset analysis method is used to calculate the comprehensive evaluation value of each factory's production of the component to be produced. Here, the weight value is calculated using the entropy method to reflect the importance of each production index in the overall production. For example, for the strength requirement in the target production index, its weight will be higher than the surface roughness requirement because it is crucial to the final quality and safety of the component. And through a preset analysis method, such as the weighted average method, the actual production index of each factory is compared with the target production index, and the weight of each index is considered to calculate the comprehensive evaluation value of each factory. For example, if the strength parameter of a factory is close to the target value and the weight of the parameter is large, then the comprehensive evaluation value of the factory will be higher, indicating that the factory is more suitable for producing the component.

[0025] Finally, in step S4, based on the comprehensive evaluation value calculated in the previous step, the orders to be delivered are allocated to multiple collaborative factories so that these factories can produce and deliver components according to the allocated orders. In the order allocation process, factories with higher comprehensive evaluation values ​​are given priority to ensure the quality and production efficiency of the components. In addition, the present invention can also be combined with genetic algorithms to further optimize order allocation. For example, assuming there are three orders to be delivered and five factories, the fitness of each factory is calculated through comprehensive evaluation, and then the genetic algorithm is used for iterative optimization to find the optimal order allocation plan to ensure the reasonable allocation of production tasks of each factory, thereby avoiding overload or waste of resources in some factories.

[0026] The present invention generates orders by obtaining target production index values, uses the factory resource database to screen collaborative factories, combines the entropy method and the fuzzy comprehensive evaluation model to evaluate the factories, and finally optimizes production scheduling through the order allocation algorithm to achieve intelligent management of prefabricated component production. In practical applications, for example, for a component production task with high strength and high precision requirements, the above method can select those factories with stable production quality and high matching degree in history, and allocate orders to these factories to ensure production reliability and efficiency, thereby solving the problems of arbitrary factory selection and uneven production resource utilization in the prior art, and improving the intelligence of the entire production process and customer satisfaction.

[0027] In one example, multiple collaborative factories are determined from a factory resource database based on the pending orders, including: obtaining all factory resource data sets in the factory resource database, wherein the factory resource data sets include: product types produced by the factory and capacity data of each product type; performing order demand analysis on the pending orders to obtain an order demand data set, wherein the order demand data set includes: product type, product quantity, delivery cycle and production specifications; fuzzifying the factory product type data according to the degree of matching with the order demand data set to obtain a first fuzzy membership of the product type matching degree, wherein the matching degree is divided into mismatch, partial match and complete match; fuzzifying the capacity data of each product type of the factory according to the degree of matching with the order demand data set to obtain a second fuzzy membership of the capacity data matching degree of each product type, wherein the matching degree is divided into low capacity, medium capacity and high capacity; combining the first fuzzy membership and the second fuzzy membership to obtain a fuzzy resource data set, performing criterion classification processing to obtain multiple criterion sets, A fuzzy multi-criteria decision model is established based on the multiple criterion sets; each criterion in the fuzzy multi-criteria decision model is compared and calculated pairwise to obtain a fuzzy contrast matrix, and the fuzzy consistency of the fuzzy contrast matrix is ​​calculated to obtain a fuzzy weight value of each criterion in the fuzzy multi-criteria decision model; the fuzzy multi-criteria decision model is used to perform a fuzzy comprehensive evaluation on the resource data of each factory in the factory resource database to obtain a fuzzy comprehensive evaluation result of each factory, and a fuzzy membership function is used to characterize the comprehensive evaluation result to obtain a fuzzy comprehensive membership value of each factory; based on the fuzzy comprehensive membership value of each factory, a fuzzy membership vector of each factory is calculated, and the fuzzy distance between each fuzzy membership vector and a preset positive ideal solution and a negative ideal solution is calculated respectively to obtain a positive ideal solution distance and a negative ideal solution distance; according to the positive ideal solution distance and the negative ideal solution distance of each factory, the relative proximity of each factory is calculated, and the factories are sorted from high to low according to their relative proximity, and a preset number of factories with the highest proximity are selected as collaborative factories.

[0028] In an embodiment of the present invention, determining multiple collaborative factories is a key step. By parsing the orders to be delivered, the factories that can best meet the order requirements are screened out from the factory resource database. In order to achieve this, it is first necessary to obtain all factory resource data sets from the factory resource database. The data sets include the types of products produced by each factory and their corresponding production capacity data. These data are essential for understanding the production capacity, capacity utilization, and whether they are suitable for the current production tasks of each factory. For example, suppose that five factories are recorded in the database, of which factory A produces modular wall components with an annual production capacity of 1,000 units, and factory B produces roof beams with an annual production capacity of 800 units. These data will be used in subsequent steps to determine the degree of match between the factory and the order requirements.

[0029] Next, it is necessary to parse the requirements of the pending orders and generate an order requirement dataset. The order requirement dataset includes information such as product type, product quantity, delivery cycle, and production specifications. For example, a pending order requires the production of 500 modular wall components with a delivery cycle of three months and clear standards for strength and surface finish. With such an order requirement dataset, production goals can be clearly defined to guide the subsequent factory screening process.

[0030] When determining the matching of a factory, the present invention adopts a fuzzy method to process the matching relationship between factory resource data and order demand data. First, the product type data of the factory is matched with the product type in the order demand to obtain the first fuzzy membership of the product type matching degree. Here, the matching degree is divided into three levels: "mismatch", "partial match" and "complete match". For example, if the product type required in the order demand is "modular wall components", and the production type of a factory is "roof beams and columns", its matching degree may be "mismatch"; if a factory can produce modular wall components, but its expertise is other types of components, its matching degree may be "partial match". Through this fuzzy processing, the first fuzzy membership is obtained, which can effectively describe the degree of compliance of each factory with the order demand in terms of product type.

[0031] In addition to product type matching, the factory's capacity data also needs to be matched and analyzed. Compare the capacity data of each product type in the factory with the production quantity in the order demand to obtain the second fuzzy membership of the matching degree of the capacity data of each product type. The matching degree is divided into three levels: "low capacity", "medium capacity" and "high capacity". For example, if the annual production capacity of a factory is 1,000 units and the order demand is 500 units, then its capacity matching degree can be considered to be "medium capacity"; if the order quantity is 300 units, then its capacity matching degree is "high capacity". Through the calculation of fuzzy membership, the degree of compliance of each factory with the order demand in terms of capacity can be evaluated more flexibly to ensure the reasonable allocation of production tasks.

[0032] After obtaining the first fuzzy membership and the second fuzzy membership, they need to be processed as a set to obtain a fuzzy resource data set. The fuzzy resource data set is classified by criteria to form multiple criterion sets, and a fuzzy multi-criteria decision model is established based on these criteria. In the fuzzy multi-criteria decision model, multiple evaluation criteria are included, such as the matching degree of product type and the matching degree of production capacity. By comparing these criteria pairwise, a fuzzy comparison matrix is ​​constructed. For example, for the matching degree of product type and production capacity, the fuzzy comparison matrix can be used to calculate their relative importance in the overall decision and determine the fuzzy weight value of each criterion.

[0033] The determination of fuzzy weight values ​​is crucial for the subsequent comprehensive evaluation. The fuzzy consistency calculation of the fuzzy comparison matrix ensures the consistency between the various criteria, thereby obtaining the fuzzy weight value of each criterion. For example, if the matching degree of product type is determined to be critical to the successful execution of an order, then its weight will be higher, while the weight of capacity matching may be relatively low. These weight values ​​will be used to weight the comprehensive evaluation of each factory to ensure that the impact of important criteria in the comprehensive evaluation is fully reflected.

[0034] Next, the fuzzy multi-criteria decision-making model is used to perform a fuzzy comprehensive evaluation on the resource data of each factory in the factory resource database to obtain the fuzzy comprehensive evaluation results of each factory. For example, if the product type matching degree of factory A is "complete matching" and the capacity matching degree is "medium capacity", its comprehensive evaluation may be high. Although the capacity matching degree of factory B is "high capacity", the product type is "partial matching", so the comprehensive evaluation may be reduced. The comprehensive evaluation results are characterized by the fuzzy membership function to obtain the fuzzy comprehensive membership value of each factory, thereby forming a global understanding of the production capacity of each factory.

[0035] After obtaining the fuzzy comprehensive membership value of each factory, the fuzzy membership vector of each factory is further calculated, and the fuzzy distance between each fuzzy membership vector and the preset positive ideal solution and negative ideal solution is calculated respectively. The positive ideal solution represents the optimal state of all evaluation criteria, while the negative ideal solution represents the worst state. For example, the positive ideal solution may correspond to "perfect match" and "high capacity", while the negative ideal solution corresponds to "mismatch" and "low capacity". By calculating the distance between each factory and the positive ideal solution and the negative ideal solution, the positive ideal solution distance and the negative ideal solution distance of each factory are obtained.

[0036] Finally, the relative proximity of each factory is calculated based on the positive ideal solution distance and the negative ideal solution distance. The higher the proximity, the closer the production capacity of the factory is to the ideal state. Therefore, the factories can be sorted from high to low according to their relative proximity, and the preset number of factories with the highest proximity can be selected as collaborative factories. For example, if two factories need to be selected for production cooperation, the two factories with the highest proximity can be selected. These collaborative factories will participate in the production and delivery of orders to ensure that the production tasks can be completed efficiently and with high quality.

[0037] In one example, the actual production index values ​​corresponding to the components to be produced by each collaborative factory are determined based on a factory resource database, including: based on the factory resource database, determining the quality parameter data set of the components of the component type to be produced by each collaborative factory, wherein the quality parameter data set includes: component strength, surface roughness, dimensional accuracy and material uniformity; for each quality parameter, according to its numerical range in different components, it is divided into multiple fuzzy sets according to the first fuzzy level, the second fuzzy level and the third fuzzy level, and a fuzzy membership matrix is ​​constructed based on the fuzzy sets, wherein the columns of the fuzzy membership matrix represent quality parameters, the rows represent construction types, and different collaborative factories correspond to different fuzzy membership matrices; using the first formula, calculating the uncertainty evaluation score of each quality parameter in the fuzzy membership matrix, wherein the first formula is: , is the uncertainty assessment score of the u-th parameter, is the initial weight value of the vth component at the uth parameter; according to the uncertainty evaluation score of each quality parameter, the parameter weight value of each quality parameter is calculated using the second formula, wherein the second formula is: , represents the weight of the jth quality parameter, and m represents the total number of parameters; the fuzzy membership matrix is ​​fuzzy weighted averaged using the third formula to obtain the actual production index value of each component produced by each system factory, wherein the third formula is: , is the actual production index value of the i-th component, Represents the fuzzy membership value of the i-th component on the j-th quality parameter.

[0038] In an embodiment of the present invention, determining the actual production index values ​​corresponding to the components to be produced by each collaborative factory based on the factory resource database is an important step in realizing intelligent scheduling and factory selection. First, it is necessary to extract the quality parameter data set of the components to be produced by each collaborative factory based on the factory resource database. The data set includes the strength, surface roughness, dimensional accuracy, and material uniformity of the components. These quality parameters are key indicators for measuring the final quality of the components. For example, a collaborative factory recorded in previous production that the strength of modular wall components was 48 MPa, the surface roughness was 1.0 μm, the dimensional accuracy was ±0.2 mm, and the material uniformity was 0.8. These data are stored and extracted through the factory resource database, which is an important reference for evaluating the actual production capacity of the factory in this production plan.

[0039] Next, the data of each quality parameter needs to be fuzzified in order to make a more flexible assessment of the production levels of different factories. Specifically, for each quality parameter, according to its numerical range in different components, it is divided into the first fuzzy level, the second fuzzy level and the third fuzzy level to form multiple fuzzy sets. For example, the strength of a component can be divided into three levels: low strength (40-45 MPa), medium strength (45-50 MPa) and high strength (50 MPa and above). Similarly, the surface roughness can be divided into three fuzzy levels: "rough", "medium" and "fine". In this way, continuous numerical parameters can be graded so that the fuzzy characteristics of these parameters can be described by the subsequent calculation of fuzzy membership.

[0040] After completing the division of fuzzy levels, it is necessary to construct a fuzzy membership matrix to represent the performance of each collaborative factory on different component types and quality parameters. Specifically, the columns of the fuzzy membership matrix represent quality parameters, such as strength, surface roughness, dimensional accuracy, etc., while the rows represent component types. Different collaborative factories have their own independent fuzzy membership matrices. For example, in the fuzzy membership matrix of factory A, the fuzzy membership of a certain component type in "medium strength" is 0.7, and the fuzzy membership of "high strength" is 0.3, which means that the components produced by the factory have a 70% chance of reaching a medium level in terms of strength parameters and a 30% chance of reaching a high strength level. In this way, the fuzzy membership matrix can clearly express the fuzzy performance of the factory on each quality parameter.

[0041] In order to further evaluate the uncertainty of each quality parameter in different factories, it is necessary to use the first formula to calculate the uncertainty assessment score of each quality parameter in the fuzzy membership matrix. Through this formula, the uncertainty of each quality parameter can be calculated. For example, for the strength parameter of factory A, its fuzzy membership is low, medium, and high. The uncertainty assessment score can be obtained through the above formula. If the membership distribution of a parameter is relatively uniform, then its uncertainty will be higher, indicating that the performance of the factory on this parameter fluctuates greatly. If the membership is concentrated at a certain level, then the uncertainty is lower, indicating that the factory's production performance on this parameter is relatively stable.

[0042] Based on the uncertainty assessment score of each quality parameter, the second formula is then used to calculate the weight value of each quality parameter. The calculation of the weight value reflects the importance of each parameter in the overall evaluation. Generally speaking, parameters with smaller uncertainties will have higher weights in the overall evaluation because they are more representative of the factory's production stability and quality assurance. For example, if the uncertainty of the strength parameter is small, while the uncertainty of the surface roughness is large, then the weight of the strength will be higher, which means that the strength parameter has a greater impact when evaluating the factory's production capacity.

[0043] After obtaining the weight value of each quality parameter, it is necessary to perform fuzzy weighted average calculation on the fuzzy membership matrix to obtain the actual production index value of each component produced by each collaborative factory. Through fuzzy weighted average calculation, the performance of each factory on each quality parameter can be comprehensively analyzed, and combined with the weights of these parameters, the comprehensive quality index of each component can be obtained. For example, a modular wall component produced by factory A has a membership of 0.7 for its strength parameter and 0.6 for its surface roughness. By performing weighted average calculation on these memberships and the corresponding weights, the comprehensive production index of the component can be obtained. These indicators can fully reflect the overall quality level of the factory when producing the component.

[0044] Through the above steps, the present invention realizes the process of determining the actual production index values ​​of the components to be produced by each collaborative factory based on the factory resource database. First, each quality parameter is divided into different levels through fuzzy processing, and then a fuzzy membership matrix is ​​constructed, the uncertainty assessment score is calculated, the parameter weight is determined, and finally the comprehensive production index is obtained through fuzzy weighted average. For example, assuming that the strength requirements of a component to be produced are very high, while the surface roughness requirements are relatively low, the above method can ensure that when selecting collaborative factories, those factories that perform well in strength parameters are given priority consideration. This method not only takes into account the multiple dimensions of the factory's production capacity, but also can find the factory that best suits the order requirements in uncertainty, thereby achieving optimal production scheduling and resource allocation.

[0045] In one example, respectively calculating the indicator weight value of each target production indicator value includes: normalizing the target production indicator values ​​and integrating them into a target production indicator matrix, wherein the target production indicator matrix is , i represents the target production index number, j represents the serial number of the component to be produced; the fourth formula is used to calculate the entropy value H of each row of the target production index matrix, wherein the fourth formula is: Said is the entropy value of the jth row, is the proportion of the i-th target production index in the j-th component to be produced; the fifth formula is used to calculate the weight value W of each row of the target production index matrix based on the entropy value H of each row of the target production index matrix, wherein the fifth formula is: , n represents the target production index quantity, and m represents the number of components to be produced.

[0046] Furthermore, combining the index weight value and the actual production index value, a preset analysis method is used to calculate the comprehensive evaluation value of each collaborative factory producing the component to be produced, including: normalizing the actual production index value and integrating it into an actual production index matrix, wherein the actual production index matrix is ​​Y=[ ], p represents the actual production index number, q represents the collaborative factory number;

[0047] The sixth formula is used to calculate the comprehensive evaluation value of each row of the actual production index matrix in combination with the weight value of each row of the target production index matrix, so as to obtain the comprehensive evaluation value corresponding to each collaborative factory, wherein the sixth formula is: , i represents the collaborative factory, j represents the production index number, n represents the total number of production indicators, Represents the comprehensive evaluation value of the i-th collaborative factory.

[0048] In the embodiment of the present invention, calculating the index weight value of each target production index value is an important step in realizing intelligent production scheduling. The goal of this process is to accurately calculate the weight of each quality index, determine the importance of each quality parameter in the comprehensive evaluation, and calculate the comprehensive evaluation value of each collaborative factory in combination with the actual production index, so as to select the most suitable factory to complete the production task.

[0049] First, the target production index values ​​need to be normalized to obtain the target production index matrix. The elements in the target production index matrix represent the relative performance of each production index in each component to be produced. Assuming that the target production index includes the strength, surface roughness, dimensional accuracy, etc. of the component, the value of each index for different components to be produced may be different. The normalization process normalizes the value of each production index to the same dimension so that a unified weight calculation and comparison can be performed later. For example, for the target production index "strength", the strength value of each component is converted into a relative proportional value between 0 and 1 through the normalization formula, so that the strength of different components can be compared and evaluated on the same scale.

[0050] After normalization, a target production index matrix is ​​formed, in which each row in the matrix represents a target production index, and each column represents a component to be produced. Next, the fourth formula is used to calculate the entropy value of each row of the target production index matrix. By calculating the entropy value, the performance stability of each production index in different components can be evaluated. If the numerical difference of a production index in each component is small, then its entropy value will be low, indicating that the index is relatively stable. Conversely, if the numerical fluctuation of a production index is large, then its entropy value will be high, indicating that there is a large uncertainty in the index. For example, assuming that the entropy value of the strength parameter in different components is low, it means that the strength changes little in all components and maintains good consistency in production.

[0051] After obtaining the entropy value of each target production indicator, the weight value of each indicator needs to be calculated. The smaller the entropy value of the indicator, the higher its weight, which indicates that the indicator is more important when evaluating the production capacity of the factory. For example, if the entropy value of the strength parameter of a component to be produced is very low, while the entropy value of the surface roughness is relatively high, then the weight value of strength will be larger because it is more consistent in different components, indicating that its impact on component quality is more important. These weight values ​​will play a vital role in the subsequent comprehensive evaluation of the factory.

[0052] After obtaining the weight value of each target production indicator, these weight values ​​are further combined with the actual production indicator values ​​of each collaborative factory to calculate the comprehensive evaluation value. First, the actual production indicator values ​​need to be normalized and integrated into an actual production indicator matrix. In the actual production indicator matrix, p represents the production indicator number, and q represents the collaborative factory number. For example, the actual production indicators include the factory's strength parameters, surface roughness parameters, etc. The performance of each factory for different production indicators is standardized through normalization, so that a unified comparison can be made. Assume that the strength parameter of factory A is 0.8 after normalization, and the surface roughness is 0.6. These values ​​reflect the relative performance of the factory in various indicators.

[0053] Next, combined with the weight value of the target production index, the weighted sum of each row in the actual production index matrix is ​​performed to obtain the comprehensive evaluation value of each collaborative factory. For example, for factory A, its strength index is normalized to 0.8, and the surface roughness is 0.6, while the weight of the strength index is 0.5 and the weight of the surface roughness is 0.3. The comprehensive evaluation value of factory A is: C = 0.5 × 0.8 + 0.3 × 0.6 = 0.4 + 0.18 = 0.58.

[0054] Through this weighted calculation, we can get a comprehensive evaluation value for each factory. The factory with a higher comprehensive evaluation value is more suitable for performing production tasks because it performs better in multiple production indicators.

[0055] Finally, through the comprehensive evaluation value, the components to be produced can be assigned to the factory with the highest comprehensive evaluation value, thereby ensuring the efficiency of the production process and the reliability of product quality. This method not only takes into account the importance differences between different production indicators, but also comprehensively considers the production capacity and actual performance of each factory, thereby realizing intelligent production order scheduling and ensuring that each component to be produced can find the most suitable factory for production to achieve optimal resource utilization and the highest production efficiency. For example, in a production environment with multiple orders and multiple collaborative factories, through such weight calculation and comprehensive evaluation, some components with extremely high strength requirements can be preferentially assigned to factories with excellent performance in strength indicators, while other components can be assigned to factories with better performance in surface quality. In this way, the advantages of each factory can be fully utilized to maximize the production requirements of the order.

[0056] In one example, the to-be-delivered orders are allocated to the multiple collaborative factories based on the comprehensive evaluation values ​​so that the collaborative factories can produce and deliver components according to the allocated to-be-delivered orders, including: performing data extraction processing on the to-be-delivered orders and the comprehensive evaluation values ​​of the multiple collaborative factories to obtain a production order data set and a collaborative factory comprehensive evaluation data set; performing state coding processing on the production order data set to obtain an initial state code, wherein the initial state code includes preliminary collaborative factory allocation information for each production order, and the initial state code uses a chromosome form to represent the order state; performing genetic operations on the initial state code for iterative optimization until it converges to an optimal state or reaches a limit on the number of iterations, so as to obtain to an optimized status code; based on the optimized status code, the urgency and criticality of the production orders are judged to obtain the urgency level and criticality level of each production order; the resource availability data of each production order in its target collaborative factory is obtained, and according to the urgency level and criticality level of each production order and the resource availability data of the production order in its target collaborative factory, the priority and delivery time of each production order are calculated to obtain the priority and delivery time requirements; according to the optimized status code and the determined priority, each production order is processed by collaborative factory allocation to obtain a final order allocation plan, wherein each production order corresponds to a target collaborative factory, as well as the corresponding production start time and completion time.

[0057] In an embodiment of the present invention, the orders to be delivered are allocated to multiple collaborative factories based on the comprehensive evaluation value to achieve an efficient production and delivery process. The implementation steps of this process involve multiple key links, including data extraction, state coding, genetic algorithm optimization, priority calculation and final order allocation. First, it is necessary to perform data extraction processing on the comprehensive evaluation values ​​of the orders to be delivered and the collaborative factories to obtain two main data sets: a production order data set and a collaborative factory comprehensive evaluation data set. The production order data set contains the order demand information of each component to be produced, such as the type, quantity and delivery deadline of the component, while the collaborative factory comprehensive evaluation data set includes the production capacity evaluation results of each factory, such as comprehensive evaluation values ​​such as quality parameters and capacity utilization.

[0058] After data extraction, the production order data set is state-encoded to form an initial state code. Here, the role of state coding is to convert the information of production order and factory allocation into a coding form that is convenient for optimization calculation. Specifically, the preliminary collaborative factory allocation information of each production order is encoded into a chromosome form. For example, the order number and the corresponding factory number are encoded together to form a chromosome, so that a chromosome can represent the initial allocation state of a production order in a specific factory. For example, order 1 is assigned to factory A, and order 2 is assigned to factory B, which can be encoded as chromosome [1A, 2B]. Through this encoding method, the preliminary allocation relationship between multiple orders and factories can be clearly represented, which is convenient for subsequent optimization calculations.

[0059] Next, the initial state encoding needs to be optimized to improve the rationality of order allocation and the efficiency of overall production. In this embodiment, the initial state encoding is iteratively optimized using a genetic algorithm. A genetic algorithm is an optimization algorithm that simulates the natural selection process, and the solution is gradually improved mainly through operations such as selection, crossover and mutation. First, the selection operation is used to select individuals with better performance from the current chromosome population, which will serve as the basis for the next generation, such as giving priority to those factory order combinations that make the comprehensive evaluation value the highest. Then, the crossover operation is used to combine the selected chromosomes, and the allocation information of different orders and different factories is exchanged to explore new combinations. For example, order 1 is transferred from factory A to factory B, and order 2 is transferred from factory B to factory A at the same time, so that a new chromosome combination is formed. Finally, the mutation operation is used to randomly adjust some chromosomes to increase the diversity of the search space and prevent the algorithm from falling into a local optimal solution. By continuously iterating the above operations, the genetic algorithm gradually approaches the optimal solution and finally obtains the optimized state encoding.

[0060] After obtaining the optimized status codes, the urgency and criticality of each production order are judged based on these codes to determine its priority and delivery time requirements. Urgency refers to how quickly an order must be completed, which may depend on customer requirements and the urgency of the production plan, while criticality refers to the importance of the order to the overall production task, such as whether it is an order on the critical path. Through these judgments, an urgency level and a criticality level can be assigned to each order. For example, the delivery date of Order 3 is very tight, and the components of this order will play a key role in the subsequent assembly process, so the urgency level and criticality level of this order are both high. Based on these levels, the priority of the order can be calculated, and orders with higher urgency and criticality levels will be prioritized for production.

[0061] In addition, it is also necessary to obtain the resource availability data of each production order in its target collaborative factory, including the factory's equipment utilization, production capacity, and material supply. Resource availability data directly affects the production schedule of orders and the capacity allocation of factories. For example, if the equipment utilization of a factory is close to saturation, assigning a new order to the factory may cause production delays. Therefore, when calculating the priority and delivery time of each order, it is necessary to comprehensively consider the resource availability data of the order in its target collaborative factory. By combining the urgency level, criticality level, and resource availability data, the production priority and delivery time requirements for each order can be determined more accurately. For example, although order 4 has a higher urgency level, the target factory's capacity is close to saturation, so it may need to be reallocated to other factories or its production plan adjusted.

[0062] Finally, according to the optimized status code and the priority of each order, the production orders are finally allocated to the collaborative factories to form the final order allocation plan. In this plan, each production order corresponds to a target collaborative factory, as well as a specific production start time and completion time. For example, the final plan may assign order 5 to factory C, with a production start time of next Monday and an estimated completion time of two weeks later. In this way, all orders can be efficiently arranged for production while ensuring quality, shortening the production cycle as much as possible and improving the timeliness of delivery. The final order allocation plan, after genetic algorithm optimization and multi-dimensional priority calculation, can make the best use of production resources, reasonably schedule orders, ensure high-quality production and on-time delivery of components, and maximize overall production efficiency and customer satisfaction.

[0063] Through these steps, the present invention not only optimizes the allocation plan of orders and factories through genetic algorithms during the order allocation process, but also conducts a multi-dimensional comprehensive evaluation of the urgency and criticality of the orders and the availability of resources in the factories to determine the optimal production priority and delivery time. The final order allocation plan ensures that each order can be allocated to the most suitable factory and that production is completed at the appropriate time. This intelligent allocation process greatly improves the efficiency and flexibility of production scheduling, avoids production delays and quality problems caused by insufficient factory resources or unreasonable order allocation, and ensures the efficient operation of the entire production system.

[0064] like Figure 2 As shown, an embodiment of the present invention further provides an intelligent device for producing and delivering prefabricated components, including:

[0065] An acquisition unit 1 is used to acquire a target production index value of a component to be produced, and generate a to-be-delivered order based on the target production index value of the component to be produced;

[0066] Determining unit 2, used to determine multiple collaborative factories from a factory resource database based on the to-be-delivered order, and to determine actual production index values ​​corresponding to the production of the to-be-produced components by each collaborative factory based on the factory resource database;

[0067] A calculation unit 3 is used to calculate the index weight value of each target production index value respectively, and combine the index weight value and the actual production index value to calculate the comprehensive evaluation value of each collaborative factory producing the component to be produced by a preset analysis method;

[0068] The allocation unit 4 is used to allocate the to-be-delivered orders to the multiple collaborative factories based on the comprehensive evaluation value, so that the collaborative factories can produce and deliver components according to the allocated to-be-delivered orders.

[0069] In this embodiment, for the specific implementation of each unit in the above device embodiment, please refer to the above method embodiment, which will not be repeated here.

[0070] Reference Figure 3 In an embodiment of the present invention, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0071] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0072] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0073] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided by the present invention and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM.

[0074] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0075] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. An intelligent method for production and delivery of prefabricated components, characterized in that: The following steps are involved: Obtaining a target production index value of a component to be produced, and generating a to-be-delivered order based on the target production index value of the component to be produced; Determine a plurality of collaborative factories from a factory resource database based on the to-be-delivered order, and determine actual production index values ​​corresponding to the production of the to-be-produced components by each collaborative factory based on the factory resource database; Calculate the index weight value of each target production index value respectively, and combine the index weight value and the actual production index value to calculate the comprehensive evaluation value of each collaborative factory producing the component to be produced by using a preset analysis method; Combining the index weight value and the actual production index value, a preset analysis method is used to calculate the comprehensive evaluation value of each collaborative factory producing the component to be produced, including: normalizing the actual production index value and integrating it into an actual production index matrix, wherein the actual production index matrix is ​​Y=[ ], p represents the actual production index number, q represents the collaborative factory number, The actual production index matrix is ​​the matrix element with the actual production index number p and the collaborative factory number q; the sixth formula is used to calculate the comprehensive evaluation value of each row of the actual production index matrix in combination with the weight value of each row of the target production index matrix to obtain the comprehensive evaluation value corresponding to each collaborative factory, wherein the sixth formula is: , i represents the collaborative factory, j represents the production index number, n represents the total number of production indicators, represents the comprehensive evaluation value of the i-th collaborative factory, is the matrix element of the actual production index matrix with the actual production index number i and the collaborative factory number j, is the weight value of each row of the target production index matrix, wherein the target production index matrix is ​​obtained by normalizing and integrating the target production index values; Based on the comprehensive evaluation value, the orders to be delivered are allocated to the multiple collaborative factories, so that the collaborative factories produce components and deliver them according to the allocated orders to be delivered; based on the comprehensive evaluation value, the orders to be delivered are allocated to the multiple collaborative factories, so that the collaborative factories produce components and deliver them according to the allocated orders to be delivered, including: performing data extraction processing on the comprehensive evaluation values ​​of the orders to be delivered and the multiple collaborative factories to obtain a production order data set and a collaborative factory comprehensive evaluation data set; performing state coding processing on the production order data set to obtain an initial state code, wherein the initial state code includes preliminary collaborative factory allocation information for each production order, and the initial state code uses a chromosome form to represent the order state; performing genetic operations on the initial state code Iterative optimization is performed until it converges to the optimal state or reaches the limit of the number of iterations to obtain an optimized state code; based on the optimized state code, the urgency and criticality of the production order are judged to obtain the urgency level and criticality level of each production order; the resource availability data of each production order in its target collaborative factory is obtained, and according to the urgency level and criticality level of each production order and the resource availability data of the production order in its target collaborative factory, the priority and delivery time of each production order are calculated to obtain the priority and delivery time requirements; according to the optimized state code and the determined priority, collaborative factory allocation processing is performed on each production order to obtain the final order allocation plan, wherein each production order corresponds to a target collaborative factory, and the corresponding production start time and completion time.

2. The intelligent method according to claim 1, characterized in that: Determining a plurality of collaborative factories from a factory resource database based on the to-be-delivered order includes: Acquire all factory resource data sets in the factory resource database, wherein the factory resource data sets include: product types produced by the factory and production capacity data of each product type; Performing order demand analysis on the to-be-delivered orders to obtain an order demand data set, wherein the order demand data set includes: product type, product quantity, delivery cycle, and production specifications; Fuzzifying the factory product type data according to the matching degree with the order demand data set to obtain a first fuzzy membership of the product type matching degree, wherein the matching degree is divided into mismatch, partial match and complete match; Fuzzifying the capacity data of each product type of the factory according to the matching degree with the order demand data set, and obtaining a second fuzzy membership of the matching degree of the capacity data of each product type, wherein the matching degree is divided into low capacity, medium capacity and high capacity; The first fuzzy membership degree and the second fuzzy membership degree are combined to obtain a fuzzy resource data set, and a criterion classification process is performed to obtain a plurality of criterion sets, and a fuzzy multi-criteria decision model is established based on the plurality of criterion sets; Performing pairwise comparison calculations on the criteria in the fuzzy multi-criteria decision-making model to obtain a fuzzy contrast matrix, and calculating the fuzzy consistency of the fuzzy contrast matrix to obtain a fuzzy weight value of each criterion in the fuzzy multi-criteria decision-making model; The fuzzy multi-criteria decision-making model is used to perform fuzzy comprehensive evaluation on the resource data of each factory in the factory resource database to obtain the fuzzy comprehensive evaluation result of each factory, and the fuzzy membership function is used to characterize the comprehensive evaluation result to obtain the fuzzy comprehensive membership value of each factory; Based on the fuzzy comprehensive membership value of each factory, the fuzzy membership vector of each factory is calculated, and the fuzzy distance between each fuzzy membership vector and the preset positive ideal solution and negative ideal solution is calculated respectively to obtain the positive ideal solution distance and the negative ideal solution distance; According to the positive ideal solution distance and negative ideal solution distance of each factory, the relative proximity of each factory is calculated, and the factories are sorted from high to low according to their relative proximity, and a preset number of factories with the highest proximity are selected as collaborative factories.

3. The intelligent method according to claim 1, characterized in that: Based on the factory resource database, the actual production index values ​​corresponding to the components to be produced by each collaborative factory are determined, including: Based on the factory resource database, determine the quality parameter data set of components of the component type produced by each collaborative factory, wherein the quality parameter data set includes: component strength, surface roughness, dimensional accuracy and material uniformity; For each quality parameter, according to its value range in different components, it is divided into multiple fuzzy sets according to the first fuzzy level, the second fuzzy level and the third fuzzy level, and a fuzzy membership matrix is ​​constructed based on the fuzzy sets, wherein the columns of the fuzzy membership matrix represent the quality parameters, the rows represent the construction types, and different collaborative factories correspond to different fuzzy membership matrices; The uncertainty evaluation score of each quality parameter in the fuzzy membership matrix is ​​calculated using the first formula, wherein the first formula is: , is the uncertainty assessment score of the u-th quality parameter, is the initial weight value of the vth component at the uth quality parameter; According to the uncertainty evaluation score of each quality parameter, a second formula is used to calculate the parameter weight value of each quality parameter, wherein the second formula is: , represents the weight of the jth quality parameter, m represents the total number of parameters, For the Uncertainty assessment scores for each quality parameter; The fuzzy membership matrix is ​​fuzzy weighted averaged using the third formula to obtain the actual production index value of each component produced by each system factory, wherein the third formula is: , is the actual production index value of the i-th component, Represents the fuzzy membership value of the i-th component on the j-th quality parameter.

4. The intelligent method according to claim 3, characterized in that: Calculating the index weight value of each target production index value respectively, including: The target production index values ​​are normalized and integrated into a target production index matrix, where the target production index matrix is , i represents the target production index number, j represents the component number to be produced, is the matrix element with target production index number i and component to be produced number j in the target production index matrix; The fourth formula is used to calculate the entropy value H of each row of the target production index matrix, wherein the fourth formula is: , is the entropy value of the jth row, is the proportion of the i-th target production index in the j-th component to be produced; Using the fifth formula, based on the entropy value of each row of the target production indicator matrix , calculate the weight value of each row of the target production index matrix , wherein the fifth formula is: , n represents the target production index quantity, and m represents the number of components to be produced.

5. An intelligent device for production and delivery of prefabricated components, characterized in that: include: An acquisition unit, configured to acquire a target production index value of a component to be produced, and generate a to-be-delivered order based on the target production index value of the component to be produced; A determination unit, configured to determine a plurality of collaborative factories from a factory resource database based on the to-be-delivered order, and to determine actual production index values ​​corresponding to the production of the to-be-produced components by each collaborative factory based on the factory resource database; A calculation unit, used to calculate the index weight value of each target production index value respectively, and combine the index weight value and the actual production index value to calculate the comprehensive evaluation value of each collaborative factory producing the component to be produced by a preset analysis method; Combining the index weight value and the actual production index value, a preset analysis method is used to calculate the comprehensive evaluation value of each collaborative factory producing the component to be produced, including: normalizing the actual production index value and integrating it into an actual production index matrix, wherein the actual production index matrix is ​​Y=[ ], p represents the actual production index number, q represents the collaborative factory number, The actual production index matrix is ​​the matrix element with the actual production index number p and the collaborative factory number q; the sixth formula is used to calculate the comprehensive evaluation value of each row of the actual production index matrix in combination with the weight value of each row of the target production index matrix to obtain the comprehensive evaluation value corresponding to each collaborative factory, wherein the sixth formula is: , i represents the collaborative factory, j represents the production index number, n represents the total number of production indicators, represents the comprehensive evaluation value of the i-th collaborative factory, is the matrix element of the actual production index matrix with the actual production index number i and the collaborative factory number j, is the weight value of each row of the target production index matrix, wherein the target production index matrix is ​​obtained by normalizing and integrating the target production index values; An allocation unit is used to allocate the pending orders to the multiple collaborative factories based on the comprehensive evaluation values, so that the collaborative factories produce components and deliver them according to the allocated pending orders; allocating the pending orders to the multiple collaborative factories based on the comprehensive evaluation values, so that the collaborative factories produce components and deliver them according to the allocated pending orders, including: performing data extraction processing on the pending orders and the comprehensive evaluation values ​​of the multiple collaborative factories to obtain a production order data set and a collaborative factory comprehensive evaluation data set; performing state coding processing on the production order data set to obtain an initial state code, wherein the initial state code includes preliminary collaborative factory allocation information for each production order, and the initial state code uses a chromosome form to represent the order state; performing genetic coding on the initial state code The operation is iteratively optimized until it converges to the optimal state or reaches the limit of the number of iterations to obtain an optimized state code; based on the optimized state code, the urgency and criticality of the production order are judged to obtain the urgency level and criticality level of each production order; the resource availability data of each production order in its target collaborative factory is obtained, and according to the urgency level and criticality level of each production order and the resource availability data of the production order in its target collaborative factory, the priority and delivery time of each production order are calculated to obtain the priority and delivery time requirements; according to the optimized state code and the determined priority, each production order is processed by collaborative factory allocation to obtain the final order allocation plan, wherein each production order corresponds to a target collaborative factory, and the corresponding production start time and completion time.

6. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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