Data ladder operation method and terminal

Abstract

According to the data ladder operation method and the terminal provided by the invention, the transaction pool is established to perform hierarchical disassembly on the operation data distributed to the distribution server by the user and the operation formula thereof according to the operation sequence, namely, the whole complex operation process is decomposed into multiple layers of operation steps; different computers are matched for the operation steps of each hierarchy for sequential calculation, that is, the complex operation process is dispersed to different computers for hierarchical processing, so that the data calculation efficiency is improved, the CPU load of each computer is reduced, and meanwhile, the utilization rate of the computing power of idle computers is improved; and meanwhile, the hierarchical operation classification can perform hierarchical pushing on formulas and data required by the data, and each hierarchy is responsible for one piece of data, so that the data privacy is ensured, and the problem of data isolation is solved at the same time.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a ladder operation method and terminal for data. Background Art

[0002] Current data calculations, such as cloud computing and Feishu's online spreadsheet calculations, usually use the server's computing power to help calculate user data. This requires uploading user spreadsheets to the server, which can easily increase the risk to user privacy data. At the same time, it is necessary to rent the server's idle computing power to perform data calculations, which requires a large amount of calculations and leads to low computing efficiency.

[0003] Therefore, how to effectively ensure data privacy while effectively improving the computing power utilization of idle computers to improve computing efficiency has gradually become a technical problem that needs to be solved urgently in the field of data computing. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a data ladder operation method and terminal, which effectively solves the data isolation problem and improves the CPU computing power utilization of idle computers.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] A ladder operation method for data comprises the steps of:

[0007] S1. Establish a transaction pool and obtain the calculation data and calculation formulas sent by the user to the distribution server;

[0008] S2, performing hierarchical decomposition on the operation data and operation formula according to the operation order to obtain hierarchical data and hierarchical formula;

[0009] S3, allocating each of the hierarchical data and the corresponding hierarchical formula to different computers and performing calculations in sequence according to the operation order;

[0010] S4. After the calculation is completed, the transaction callback mechanism is called to output the calculation result and return it to the corresponding user.

[0011] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0012] A ladder operation terminal for data includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0013] S1. Establish a transaction pool and obtain the calculation data and calculation formulas sent by the user to the distribution server;

[0014] S2, performing hierarchical decomposition on the operation data and operation formula according to the operation order to obtain hierarchical data and hierarchical formula;

[0015] S3, allocating each of the hierarchical data and the corresponding hierarchical formula to different computers and performing calculations in sequence according to the operation order;

[0016] S4. After the calculation is completed, the transaction callback mechanism is called to output the calculation result and return it to the corresponding user.

[0017] The beneficial effects of the present invention are: providing a ladder operation method and terminal for data, by establishing a transaction pool, the operation data and its operation formulas distributed by the user to the distribution server are hierarchically disassembled according to the operation order, that is, the entire complex operation process is decomposed into multiple layers of operation steps, and different computers are matched for the operation steps of each layer to perform sequential calculations, that is, the complex operation process is dispersed to different computers for hierarchical processing, which is beneficial to improving data calculation efficiency, reducing the CPU load of each computer while improving the utilization rate of idle computer computing power; at the same time, the hierarchical operation classification can push the formulas and data required for the data in a hierarchical manner, each layer is responsible for one data, and also ensures data privacy while solving the data isolation problem. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is an overall flow chart of a ladder operation method for data according to an embodiment of the present invention;

[0019] Figure 2 A schematic diagram of the structure of a ladder operation terminal for data according to an embodiment of the present invention.

[0020] Description of labels:

[0021] 1. A data ladder operation terminal; 2. Memory; 3. Processor. DETAILED DESCRIPTION

[0022] In order to explain the technical content, achieved objectives and effects of the present invention in detail, the following is an explanation in conjunction with the implementation modes and the accompanying drawings.

[0023] Prior to this, the English terms that appear in this article are explained as follows:

[0024] FLOPS: Floating-point Operations Per Second, floating-point operations per second, can be used to evaluate computer performance, especially in the field of scientific computing that uses a large number of floating-point operations.

[0025] Please refer to Figure 1 , a ladder operation method for data, comprising the steps of:

[0026] S1. Establish a transaction pool and obtain the calculation data and calculation formulas sent by the user to the distribution server;

[0027] S2, performing hierarchical decomposition on the operation data and operation formula according to the operation order to obtain hierarchical data and hierarchical formula;

[0028] S3, allocating each of the hierarchical data and the corresponding hierarchical formula to different computers and performing calculations in sequence according to the operation order;

[0029] S4. After the calculation is completed, the transaction callback mechanism is called to output the calculation result and return it to the corresponding user.

[0030] From the above description, it can be seen that the beneficial effects of the present invention are: providing a step-by-step calculation method for data, by establishing a transaction pool, the calculation data and its calculation formulas distributed by the user to the distribution server are hierarchically decomposed according to the calculation order, that is, the entire complex calculation process is decomposed into multiple layers of calculation steps, and different computers are matched for the calculation steps of each level to perform calculations in sequence, that is, the complex calculation process is dispersed to different computers for hierarchical processing, which is conducive to improving data calculation efficiency, reducing the CPU load of each computer while improving the utilization rate of idle computer computing power; at the same time, the hierarchical calculation classification can push the formulas and data required for the data in a hierarchical manner, and each level is responsible for one data, which also ensures data privacy while solving the data isolation problem.

[0031] Furthermore, the step S2 is specifically as follows:

[0032] S21, the operation formula is decomposed hierarchically according to the operation order to obtain multiple hierarchical formulas, and stored in an array form S{S1, S2, S3, ..., S n};

[0033] S22, the operation data is also decomposed into mechanical energy levels according to the operation order to obtain multiple levels of data, and stored in an array form D{D1, D2, D3, ..., D n};

[0034] S23, combining the hierarchical formulas and hierarchical data one by one according to the operation order, and storing them in a combined array form F{F1, F2, F3, ..., F n},in:

[0035] F1 = S1 (D1);

[0036] F2 = S2 (F1D2);

[0037] F3 = S3 (F2D3);

[0038] …

[0039] F n =S n (F n-1 D n );

[0040] S24. Mark the complexity level of each element in the combination array from low to high according to the operation order, recorded as {NP1, NP2, NP3, ..., NPn}.

[0041] From the above description, it can be seen that the operation data and the required operation formulas are hierarchically decomposed according to the operation order and nested in the form of arrays, which is fast and meets the requirements of subsequent sequential operations.

[0042] Furthermore, the step S3 is specifically as follows:

[0043] S31, obtaining the operating status of each computer in the current network, including idle status and floating point operations per second FLOPS;

[0044] S32, screening to obtain idle computers, and matching computers with corresponding FLOPS for each element in the combination array in descending order of the complexity level according to the amount of the hierarchical data corresponding to each element in the combination array and the complexity of the hierarchical formula;

[0045] S33, performing step operations on corresponding elements in the matched computers in order from low to high complexity levels, and outputting the final calculation results in the computer matched with the highest complexity level.

[0046] From the above description, it can be seen that for each layered data and the calculation formula it needs to use, an idle computer with corresponding computing power is matched according to its calculation complexity, and the matching is carried out in sequence according to the order of calculation to ensure that the computer matched to each layer of calculation can fully utilize the idle computing power utilization rate; at the same time, when matching the computer, the number of floating-point operations per second (FLOPS) of the computer is matched to ensure that the matched computer will not be overloaded in the subsequent data calculation process.

[0047] Furthermore, the step S32 also includes:

[0048] The matching of a computer with corresponding FLOPS for each element in the combination array according to the amount of the hierarchical data corresponding to each element in the combination array and the complexity of the hierarchical formula is specifically:

[0049] First, a computer with corresponding FLOPS is matched according to the amount of the hierarchical data corresponding to each element in the combination array. If multiple computers are matched, a computer with corresponding FLOPS is matched again from the matched multiple computers according to the complexity of the hierarchical formula as the computer for performing hierarchical calculations on each element in the combination array.

[0050] From the above description, it can be seen that the computer with corresponding computing power is matched first by the computing volume of the hierarchical data, and then the computer with corresponding computing power is further screened by the complexity of the hierarchical formula used, which can effectively ensure the data calculation with optimal throughput.

[0051] Furthermore, in step S31, the number of floating point operations per second (FLOPS) of each computer in the current network is obtained, specifically:

[0052] The clock cycle and clock frequency of each computer are obtained in advance, the number of floating-point operations that can be performed by each computer in each clock cycle is calculated, and the number of floating-point operations per second (FLOPS) of each computer is obtained by multiplying the number by the clock frequency of the corresponding computer.

[0053] From the above description, we can see that the number of floating-point operations per second (FLOPS) is used to measure the speed at which the CPU performs floating-point operations. It is a key indicator in modern CPU design. FLOPS is calculated by obtaining the computer's clock cycle and clock frequency to ensure that the matched computer will not be overloaded during subsequent data calculations.

[0054] Please refer to Figure 2 , a ladder operation terminal for data, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0055] S1. Establish a transaction pool and obtain the calculation data and calculation formulas sent by the user to the distribution server;

[0056] S2, performing hierarchical decomposition on the operation data and operation formula according to the operation order to obtain hierarchical data and hierarchical formula;

[0057] S3, allocating each of the hierarchical data and the corresponding hierarchical formula to different computers and performing calculations in sequence according to the operation order;

[0058] S4. After the calculation is completed, the transaction callback mechanism is called to output the calculation result and return it to the corresponding user.

[0059] From the above description, it can be seen that the beneficial effects of the present invention are: based on the same technical concept, in conjunction with the above-mentioned ladder operation method of data, a ladder operation terminal for data is provided, and by establishing a transaction pool, the operation data and its operation formulas distributed by the user to the distribution server are hierarchically decomposed according to the operation order, that is, the entire complex operation process is decomposed into multiple layers of operation steps, and different computers are matched for the operation steps of each level to perform calculations in sequence, that is, the complex operation process is dispersed to different computers for hierarchical processing, which is conducive to improving data calculation efficiency, reducing the CPU load of each computer while improving the utilization rate of idle computer computing power; at the same time, the hierarchical operation classification can push the formulas and data required for the data in a hierarchical manner, and each level is responsible for one data, which also ensures data privacy while solving the data isolation problem.

[0060] Furthermore, the step S2 is specifically as follows:

[0061] S21, the operation formula is decomposed hierarchically according to the operation order to obtain multiple hierarchical formulas, and stored in an array form S{S1, S2, S3, ..., S n};

[0062] S22, the operation data is also decomposed into mechanical energy levels according to the operation order to obtain multiple levels of data, and stored in an array form D{D1, D2, D3, ..., D n};

[0063] S23, combining the hierarchical formulas and hierarchical data one by one according to the operation order, and storing them in a combined array form F{F1, F2, F3, ..., F n},in:

[0064] F1 = S1 (D1);

[0065] F2 = S2 (F1D2);

[0066] F3 = S3 (F2D3);

[0067] …

[0068] F n =S n (F n-1 D n );

[0069] S24. Mark the complexity level of each element in the combination array from low to high according to the operation order, recorded as {NP1, NP2, NP3, ..., NPn}.

[0070] From the above description, it can be seen that the operation data and the required operation formulas are hierarchically decomposed according to the operation order and nested in the form of arrays, which is fast and meets the requirements of subsequent sequential operations.

[0071] Furthermore, the step S3 is specifically as follows:

[0072] S31, obtaining the operating status of each computer in the current network, including idle status and floating point operations per second FLOPS;

[0073] S32, screening to obtain idle computers, and matching computers with corresponding FLOPS for each element in the combination array in descending order of the complexity level according to the amount of the hierarchical data corresponding to each element in the combination array and the complexity of the hierarchical formula;

[0074] S33, performing step operations on corresponding elements in the matched computers in order from low to high complexity levels, and outputting the final calculation results in the computer matched with the highest complexity level.

[0075] From the above description, it can be seen that for each layered data and the calculation formula it needs to use, an idle computer with corresponding computing power is matched according to its calculation complexity, and the matching is carried out in sequence according to the order of calculation to ensure that the computer matched to each layer of calculation can fully utilize the idle computing power utilization rate; at the same time, when matching the computer, the number of floating-point operations per second (FLOPS) of the computer is matched to ensure that the matched computer will not be overloaded in the subsequent data calculation process.

[0076] Furthermore, the step S32 also includes:

[0077] The matching of a computer with corresponding FLOPS for each element in the combination array according to the amount of the hierarchical data corresponding to each element in the combination array and the complexity of the hierarchical formula is specifically:

[0078] First, a computer with corresponding FLOPS is matched according to the amount of the hierarchical data corresponding to each element in the combination array. If multiple computers are matched, a computer with corresponding FLOPS is matched again from the matched multiple computers according to the complexity of the hierarchical formula as the computer for performing hierarchical calculations on each element in the combination array.

[0079] From the above description, it can be seen that the computer with corresponding computing power is matched first by the computing volume of the hierarchical data, and then the computer with corresponding computing power is further screened by the complexity of the hierarchical formula used, which can effectively ensure the data calculation with optimal throughput.

[0080] Furthermore, the number of floating point operations per second (FLOPS) of each computer in the current network is obtained in step S31, specifically:

[0081] The clock cycle and clock frequency of each computer are obtained in advance, the number of floating-point operations that can be performed by each computer in each clock cycle is calculated, and the number of floating-point operations per second (FLOPS) of each computer is obtained by multiplying the number by the clock frequency of the corresponding computer.

[0082] The present invention provides a data ladder operation method and terminal, which are mainly used in a scenario where complex operations are performed on a large amount of user data in a distributed network, and are specifically described below in conjunction with specific embodiments:

[0083] Please refer to Figure 1 , Embodiment 1 of the present invention is:

[0084] A ladder operation method for data, such as Figure 1 As shown, the steps include:

[0085] S1. Establish a transaction pool and obtain the calculation data and calculation formulas sent by the user to the distribution server.

[0086] S2. Decompose the operation data and operation formula hierarchically according to the operation order to obtain hierarchical data and hierarchical formulas.

[0087] S3. Allocate each level of data and its corresponding level formula to different computers and calculate them in sequence according to the order of operation.

[0088] S4. After the calculation is completed, the transaction callback mechanism is called to output the calculation result and return it to the corresponding user.

[0089] The transaction callback in this embodiment refers to creating a transaction callback for the data and sending the data to the server while the user waits for the data to be processed. Other processes are processed under the hierarchical operation of the transaction pool. The transaction callback satisfies the formula sequential execution, that is, it is executed in an orderly manner until all the data sent by the user are calculated and the final result is output, and then the result is returned to the user. That is, the user only submits the data, and the data processing process is handled by the transaction pool and transaction callback, which further ensures data privacy and security and solves the data isolation problem.

[0090] That is, in this embodiment, by establishing a transaction pool, the calculation data and its calculation formulas sent by the user to the distribution server are hierarchically decomposed according to the order of calculation, that is, the entire complex calculation process is decomposed into multiple layers of calculation steps, and different computers are matched for the calculation steps of each layer to perform calculations in sequence, that is, the complex calculation process is dispersed to different computers for hierarchical processing, which is beneficial to improve data calculation efficiency, reduce the CPU load of each computer and improve the utilization rate of idle computer computing power; at the same time, hierarchical calculation grading can push the formulas and data required for the data in a hierarchical manner, and each layer is responsible for one data, which also ensures data privacy while solving the data isolation problem.

[0091] Embodiment 2 of the present invention is:

[0092] A ladder operation method for data, based on the above embodiment 1, in this embodiment, the step S2 is specifically:

[0093] S21, the operation formula is decomposed hierarchically according to the operation order to obtain multiple hierarchical formulas, and stored in an array form S{S1, S2, S3, ..., S n}.

[0094] S22, the operation data is also decomposed into mechanical energy levels according to the operation order to obtain multiple levels of data, and stored in an array form D{D1, D2, D3, ..., D n}.

[0095] S23, combining the hierarchical formulas and hierarchical data one by one according to the operation order, and storing them in a combined array form F{F1, F2, F3, ..., F n},in:

[0096] F1 = S1 (D1);

[0097] F2 = S2 (F1D2);

[0098] F3 = S3 (F2D3);

[0099] …

[0100] F n =S n (F n-1 D n ).

[0101] S24, marking the complexity level of each element in the combination array from low to high according to the operation order, recorded as {NP1, NP2, NP3, ..., NP n}.

[0102] That is, the operation data and the required operation formulas are decomposed hierarchically according to the order of operations, and nested in the form of arrays, which is fast and meets the requirements of subsequent sequential operations.

[0103] Meanwhile, in this embodiment, the step S3 is specifically as follows:

[0104] S31, obtaining the operating status of each computer in the current network, including the idle status and the number of floating-point operations per second FLOPS, wherein the number of floating-point operations per second FLOPS is obtained as follows:

[0105] The clock cycle and clock frequency of each computer are obtained in advance, the number of floating-point operations that can be performed by each computer in each clock cycle is calculated, and the number of floating-point operations per second (FLOPS) of each computer is obtained by multiplying the number by the clock frequency of the corresponding computer.

[0106] That is, the number of floating-point operations per second FLOPS is used to measure the speed at which the CPU performs floating-point operations and is a key indicator in modern CPU design:

[0107] One MFLOPS (megaFLOPS) is equal to one million (=10^6) floating point operations per second;

[0108] One GFLOPS (gigaFLOPS) is equal to one billion (=10^9) floating point operations per second;

[0109] One TFLOPS (teraFLOPS) is equal to one trillion (=10^12) floating point operations per second;

[0110] One PFLOPS (petaFLOPS) is equal to one quadrillion (=10^15) floating-point operations per second.

[0111] At the same time, FLOPS can also be replaced by OPS (Tera Operations Per Second, the number of operations that the processor can perform per second):

[0112] One MOPS (Million Operation Per Second) means that the processor can perform one million (10^6) operations per second;

[0113] One GOPS (Giga Operations Per Second) represents one billion (10^9) operations that the processor can perform per second;

[0114] One TOPS (Tera Operations Per Second) means that the processor can perform one trillion (10^12) operations per second.

[0115] FLOPS is calculated by obtaining the computer's clock cycle and clock frequency to ensure that the matched computer will not be overloaded during subsequent data calculations.

[0116] S32. Filter and obtain idle computers, and match computers with corresponding FLOPS for each element in the combination array in order of the complexity level from low to high according to the amount of the hierarchical data corresponding to each element in the combination array and the complexity of the hierarchical formula.

[0117] S33, performing step operations on corresponding elements in the matched computers in order from low to high complexity levels, and outputting the final calculation results in the computer matched with the highest complexity level.

[0118] That is, each layered data and the calculation formula it needs are matched with idle computers with corresponding computing power according to their calculation complexity, and are matched in sequence according to the order of calculation to ensure that the computers matched to each layer of calculation can fully utilize the idle computing power. At the same time, when matching computers, the number of floating-point operations per second (FLOPS) of the computers is matched to ensure that the matched computers will not be overloaded during subsequent data calculations.

[0119] In this embodiment, step S32 further includes:

[0120] The method of matching a computer with corresponding FLOPS for each element in the combination array according to the amount of the hierarchical data corresponding to each element in the combination array and the complexity of the hierarchical formula is specifically as follows.

[0121] First, a computer with corresponding FLOPS is matched according to the amount of the hierarchical data corresponding to each element in the combination array. If multiple computers are matched, a computer with corresponding FLOPS is matched again from the matched multiple computers according to the complexity of the hierarchical formula as the computer for performing hierarchical calculations on each element in the combination array.

[0122] That is, the computing volume of the hierarchical data is prioritized to match the computer with the corresponding computing power, and then the complexity of the hierarchical formula used is used to further screen the computer with the corresponding computing power. Because when the data volume is large but the formula is simple, not much computing power is required, so the computing volume of the data is prioritized to match. At the same time, when the data volume is large and the formula is complex, some formulas design data arithmetic, variance, analogy calculation, etc., multiple calculations require computing power. Therefore, on the basis of considering the data volume, we must further know how many levels of formulas are nested in the data, that is, the complexity of the formula, to further screen and match the best computer, which can effectively ensure the data calculation with the optimal throughput.

[0123] Please refer to Figure 2 , Embodiment 3 of the present invention is:

[0124] A ladder operation terminal 1 for data includes a memory 2, a processor 3, and a computer program stored in the memory 2 and executable on the processor 3. When the processor 3 executes the computer program, the steps in a ladder operation method for data in the above-mentioned embodiment 1 or embodiment 2 are completed.

[0125] In summary, the present invention provides a data ladder operation method and terminal, which establishes a transaction pool to hierarchically decompose the operation data and its operation formulas distributed by users to the distribution server according to the operation order, that is, to decompose the entire complex operation process into multiple layers of operation steps, and match the operation steps of each layer with computers of corresponding computing power according to the operating status of each computer in the current network to perform calculations in sequence, that is, to disperse the complex operation process to different computers for hierarchical processing, which is beneficial to improving data calculation efficiency, reducing the CPU load of each computer while improving the utilization rate of idle computer computing power; at the same time, the hierarchical operation classification can push the formulas and data required for the data in a hierarchical manner, and each layer is responsible for one data, which also ensures data privacy while solving the data isolation problem.

[0126] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's specification and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A ladder operation method for data, characterized in that: Includes steps: S1. Establish a transaction pool and obtain the calculation data and calculation formulas sent by the user to the distribution server; S2, performing hierarchical decomposition on the operation data and operation formula according to the operation order to obtain hierarchical data and hierarchical formula; S3, allocating each of the hierarchical data and the corresponding hierarchical formula to different computers and performing calculations in sequence according to the operation order; S4. After the calculation is completed, the transaction callback mechanism is called to output the calculation result and return it to the corresponding user.

2. The ladder operation method of data according to claim 1, characterized in that: The step S2 is specifically as follows: S21, the operation formula is decomposed hierarchically according to the operation order to obtain multiple hierarchical formulas, and stored in an array form S{S1, S2, S3, ..., S n }; S22, the operation data is also decomposed into mechanical energy levels according to the operation order to obtain multiple levels of data, and stored in an array form D{D1, D2, D3, ..., D n }; S23, combining the hierarchical formulas and hierarchical data one by one according to the operation order, and storing them in a combined array form F{F1, F2, F3, ..., F n },in: F1 = S1 (D1); F2 = S2 (F1D2); F3 = S3 (F2D3); …… F n =S n (F n-1 D n ); S24. Mark the complexity level of each element in the combination array from low to high according to the operation order, recorded as {NP1, NP2, NP3, ..., NPn}.

3. The ladder operation method of data according to claim 2, characterized in that: The step S3 is specifically as follows: S31, obtaining the operating status of each computer in the current network, including idle status and floating point operations per second FLOPS; S32, screening to obtain idle computers, and matching computers with corresponding FLOPS for each element in the combination array in descending order of the complexity level according to the amount of the hierarchical data corresponding to each element in the combination array and the complexity of the hierarchical formula; S33, performing step operations on corresponding elements in the matched computers in order from low to high complexity levels, and outputting the final calculation results in the computer matched with the highest complexity level.

4. The ladder operation method of data according to claim 3, characterized in that: The step S32 also includes: The matching of a computer with corresponding FLOPS for each element in the combination array according to the amount of the hierarchical data corresponding to each element in the combination array and the complexity of the hierarchical formula is specifically: First, a computer with corresponding FLOPS is matched according to the amount of the hierarchical data corresponding to each element in the combination array. If multiple computers are matched, a computer with corresponding FLOPS is matched again from the matched multiple computers according to the complexity of the hierarchical formula as the computer for performing hierarchical calculations on each element in the combination array.

5. The ladder operation method of data according to claim 3, characterized in that: In step S31, the number of floating point operations per second (FLOPS) of each computer in the current network is obtained, specifically: The clock cycle and clock frequency of each computer are obtained in advance, the number of floating-point operations that can be performed by each computer in each clock cycle is calculated, and the number of floating-point operations per second (FLOPS) of each computer is obtained by multiplying the number by the clock frequency of the corresponding computer.

6. A ladder operation terminal for data, characterized in that: The invention comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the following steps are implemented: S1. Establish a transaction pool and obtain the calculation data and calculation formulas sent by the user to the distribution server; S2, performing hierarchical decomposition on the operation data and operation formula according to the operation order to obtain hierarchical data and hierarchical formula; S3, allocating each of the hierarchical data and the corresponding hierarchical formula to different computers and performing calculations in sequence according to the operation order; S4. After the calculation is completed, the transaction callback mechanism is called to output the calculation result and return it to the corresponding user.

7. A data ladder operation terminal according to claim 6, characterized in that: The step S2 is specifically as follows: S21, the operation formula is decomposed hierarchically according to the operation order to obtain multiple hierarchical formulas, and stored in an array form S{S1, S2, S3, ..., S n }; S22, the operation data is also decomposed into mechanical energy levels according to the operation order to obtain multiple levels of data, and stored in an array form D{D1, D2, D3, ..., D n }; S23, combining the hierarchical formulas and hierarchical data one by one according to the operation order, and storing them in a combined array form F{F1, F2, F3, ..., F n },in: F1 = S1 (D1); F2 = S2 (F1D2); F3 = S3 (F2D3); …… F n =S n (F n-1 D n ); S24. Mark the complexity level of each element in the combination array from low to high according to the operation order, recorded as {NP1, NP2, NP3, ..., NPn}.

8. A data ladder operation terminal according to claim 7, characterized in that: The step S3 is specifically as follows: S31, obtaining the operating status of each computer in the current network, including idle status and floating point operations per second FLOPS; S32, screening to obtain idle computers, and matching computers with corresponding FLOPS for each element in the combination array in descending order of the complexity level according to the amount of the hierarchical data corresponding to each element in the combination array and the complexity of the hierarchical formula; S33, performing step operations on corresponding elements in the matched computers in order from low to high complexity levels, and outputting the final calculation results in the computer matched with the highest complexity level.

9. A data ladder operation terminal according to claim 8, characterized in that: The step S32 also includes: The matching of a computer with corresponding FLOPS for each element in the combination array according to the amount of the hierarchical data corresponding to each element in the combination array and the complexity of the hierarchical formula is specifically: First, a computer with corresponding FLOPS is matched according to the amount of the hierarchical data corresponding to each element in the combination array. If multiple computers are matched, a computer with corresponding FLOPS is matched again from the matched multiple computers according to the complexity of the hierarchical formula as the computer for performing hierarchical calculations on each element in the combination array.

10. A data ladder operation terminal according to claim 8, characterized in that: In step S31, the number of floating point operations per second (FLOPS) of each computer in the current network is obtained, specifically: The clock cycle and clock frequency of each computer are obtained in advance, the number of floating-point operations that can be performed by each computer in each clock cycle is calculated, and the number of floating-point operations per second (FLOPS) of each computer is obtained by multiplying the number by the clock frequency of the corresponding computer.

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