It is the perfect theory if a theory is based on mathematics and meets the five basic principles of "Simplicity, Unity, Order, Symmetry and Definiteness" in science, industry and aesthetics.
Traditional IT theory (OOP, FP and hardware architecture, etc.) are pseudoscience. they belong to what physicist Wolfgang Pauli said "Not Even Wrong".
Keep it Simple and Unified.
Computer science is essentially a management science, and vice versa.
Software and hardware are factories that manufacture data, so they have the same "warehouse/workshop model" and management methods as the manufacturing industry.
 Lin Pengcheng
Traditional IT theory (OOP, FP and hardware architecture, etc.) VS. Warehouse/Workshop Model
1. Traditional IT theory (OOP, FP and hardware architecture, etc.)
1.1. Mathematical support
Lack of mathematical support, in any discipline, the part that has mathematical support can be called science.
1.2. Common sense in scientific research: repeatability and verifiability
There is no definite, operational theoretical basis for design and evaluation, design is very arbitrary, and good examples of OO & FP & hardware architectures are rare.
Because the same case, different people design OOP & FP & hardware architecture varies greatly, and it is difficult to evaluate the advantages and disadvantages. So they belong to what Pauli
said "Not Even Wrong"
In short, they are not repeatable, not verifiable, and not scientific at all. They are pseudoscience.
Reference:
"Not even wrong" is a phrase often used to describe pseudoscience or bad science. It describes an argument or explanation that purports to be scientific but uses faulty reasoning or speculative premises, which can be neither affirmed nor denied and thus cannot be discussed rigorously and scientifically.
For a meaningful discussion on whether a certain statement is true or false, the statement must satisfy the criterion of falsifiability, the inherent possibility for the statement to be tested and found false. In this sense, the phrase "not even wrong" is synonymous with "unfalsifiable".
History of the expression
The phrase is generally attributed to the theoretical physicist Wolfgang Pauli, who was known for his colorful objections to incorrect or careless thinking. Rudolf Peierls documents an instance in which "a friend showed Pauli the paper of a young physicist which he suspected was not of great value but on which he wanted Pauli's views. Pauli remarked sadly, 'It is not even wrong'." This is also often quoted as "That is not only not right; it is not even wrong", or in Pauli's native German, "Das ist nicht nur nicht richtig; es ist nicht einmal falsch!" Peierls remarks that quite a few apocryphal stories of this kind have been circulated and mentions that he listed only the ones personally vouched for by him.
He also quotes another example when Pauli replied to Lev Landau, "What you said was so confused that one could not tell whether it was nonsense or not."Columbia physicist Peter Woit used the phrase in the title of his book Not Even Wrong: The Failure of String Theory and the Search For Unity in Physical Law. Woit also writes a blog of that name.
Physicist Wolfgang Pauli was once asked to review a technical paper and assess its accuracy. The content was so garbled, however, that Pauli is said to have remarked that not only was the paper not right, it was “not even wrong.” He meant the paper was so poorly written, so muddled in its reasoning, that it was impossible to evaluate in any fashion. It was even worse than wrong—it was incoherent.
The author would have to substantially improve the paper in order for it to even be assessed as wrong.Science is largely a literary endeavor. It advances only when scientists are able to communicate their discoveries to other scientists for independent evaluation and confirmation. A hypothesis that is not clearly stated cannot be tested.
Only when experimental methods are carefully articulated can they be critiqued or validated.
Therefore, scientists must be able to cogently articulate their hypotheses, observations, and methods. They must carefully define important terms and use them in a consistent way. Anything less is confusing at best and “not even wrong” at worst.
1.3. Realworld reference models
FP & hardware system architectures do not have realworld reference models, and theories without reference systems are difficult to explain and understand, and most likely to make mistakes.
OOP is only a superficial simulation of the real world, there is no unified guidelines, principles and models. Both the system architecture and the process are very complex and confusing.
2. Warehouse/Workshop Model
2.1. Mathematical support
 Based on mathematical models, operations research, management science
 The architecture can be visualized with a simple star chart and Gantt chart
2.2. Common sense in scientific research: repeatability and verifiability
With 10 principles, 5 aesthetics, and 5 basic components

In the same case, the architectures designed by different people will be consistent and clear, and the superiority of different people's design solutions can be accurately judged by mathematical models and operations research, which is in line with the scientific principle of repeatable and verifiable certainty, so we call it science. The best people can effectively prove the superiority of their solutions by mathematical methods, so this method is the choice of the best people.
 In my mathematical and principlebased "warehouse/shop floor model" theory, uncertainty exists in a deterministic principlebased framework and mathematical model. Uncertainty is controllable, and when an uncertain event occurs, the scheduler can immediately optimize it by dynamic planning and incorporate it into the deterministic system.
 Uncertainty is the monkey on the palm of Buddha. The monkey may make trouble, but it cannot turn over the palm of Buddha's hand, and Buddha (scheduler) sentences the monkey to 500 years of imprisonment and then exiles it to a hundred thousand miles, and finally incorporates it into the deterministic Buddhist system.
 Just like after Enron, the principlebased international accounting standards turned around and defeated the proud and conceited rulebased US accounting standards, effectively dealing with uncertainty and avoiding being exploited. Most importantly, they are simple, effective and reliable.
2.3. Realworld reference models
The theory of having a realworld mainstream system as a reference model is easy to understand, easy to comprehend, and if there is controversy or doubt, the realworld reference model can be analyzed and the answer found.

This has been the best practice of the manufacturing industry for centuries, and there are countless research materials and book boxes available on the subject.
 We should be based on a centrally managed large data model standard system, not many simple types (or objects) that manage confusion,
 Validate centrally instead of everywhere.
 The large data model standard system is managed by the warehouse.
 The input data of each workshop (pure function, pipeline) is given by the warehouse scheduler to the data that has been checked to meet the standard.
 The output data of each workshop (pure function, pipeline) is checked by the warehouse and saved after meeting the standard.
 The workshops are independent, short, logically simple, clear, linearly scalable, they do not interact with each other, only with the warehouse, which guarantees simplicity, correctness and high efficiency.
 Replace "the complex and chaotic m*n object interactive network" with "1:n One scheduler for global scheduling and optimization of all workshops(pure function, pipeline).".
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