Business data capitalization method based on end-to-end intelligent operation and maintenance all-in-one machine, storage medium, equipment and computer program product

Abstract

The invention discloses a business data capitalization method based on an end-to-end intelligent operation and maintenance all-in-one machine, a storage medium, equipment and a computer program product. The business data capitalization method comprises the following steps: deploying a computing power integration model on the end-to-end intelligent operation and maintenance all-in-one machine; calculating idle computing power required by the end-to-end intelligent operation and maintenance all-in-one machine for collecting a piece of service data in real time through a computing power modeling model, transmitting the service data by using a thread not smaller than the calculated idle computing power in the end-to-end intelligent operation and maintenance all-in-one machine, and storing the transmitted service data in a memory space of the end-to-end intelligent operation and maintenance all-in-one machine; and calculating the dispersion of each piece of business data stored in the memory space according to preset standard business data, and determining the asset value of one piece of business data based on an analytic hierarchy process. When the business data is transmitted to the end-to-end intelligent operation and maintenance all-in-one machine, the computing power space is preferentially considered, the accuracy of business data capitalization is improved, and the credibility and value of business data capitalization are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of data assetization, and specifically, relates to a business data assetization method, storage medium, device and computer program product based on an end-to-end intelligent operation and maintenance all-in-one machine. Background Art

[0002] The end-to-end intelligent operation and maintenance integrated machine improves operation and maintenance efficiency by real-time monitoring of massive business data in the enterprise operation process. In addition, these business data can guide the enterprise to provide more comprehensive, accurate and timely decision support. Business data assetization is the process of converting various business data into quantifiable and analyzable data, and further into assets with economic value, thereby helping enterprises better understand the market, understand user needs and optimize operation strategies.

[0003] However, since business data is generated in real time and the data volume is huge, when storing it in the end-to-end intelligent operation and maintenance integrated machine, only the memory size in the end-to-end intelligent operation and maintenance integrated machine is considered, while ignoring whether there is sufficient computing power space during the business data transmission process. As a result, when the computing power space is insufficient, the transmission of business data is delayed or interrupted, thereby reducing the accuracy of business data assetization; in addition, most of the existing business data assetization methods are formulated for individual enterprises separately, without forming a unified standard, and the credibility of their value is not high. Summary of the invention

[0004] In response to the problems existing in the prior art, the present invention provides a business data assetization method, storage medium, device and computer program product based on an end-to-end intelligent operation and maintenance all-in-one machine. When business data is transmitted to the end-to-end intelligent operation and maintenance all-in-one machine, computing power space is given priority to ensure real-time transmission of business data, improve the accuracy of business data assetization, and propose a generally applicable business data assetization method, which improves the credibility and value of business data assetization.

[0005] To achieve the above technical objectives, the present invention adopts the following technical solution: a business data assetization method based on an end-to-end intelligent operation and maintenance all-in-one machine, specifically comprising the following steps:

[0006] Step S1: deploy a computing power quantization model on an end-to-end intelligent operation and maintenance integrated machine;

[0007] Step S2: Calculate the idle computing power required by the end-to-end intelligent operation and maintenance integrated machine to collect a piece of business data in real time through a computing power quantification model, use a thread in the end-to-end intelligent operation and maintenance integrated machine that is not less than the calculated idle computing power to transmit the business data, and store the transmitted business data in the memory space of the end-to-end intelligent operation and maintenance integrated machine;

[0008] Step S3: Calculate the discreteness of each piece of business data stored in the memory space according to the preset standard business data, and determine the asset value of a piece of business data based on the hierarchical analysis method.

[0009] Furthermore, the computational power quantification model in step S1 is specifically:

[0010]

[0011] Among them, C br is the idle computing power, n is the number of logic operation chips, i is the index of n, f(a i ) is the mapping function of logical operation, α i is the mapping ratio coefficient of the i-th logic operation chip, q1 (TOPS) is the redundant computing power of the logic operation; m is the number of parallel computing chips, j is the index of m, f(b j ) is the mapping function for parallel computing, β j is the mapping ratio coefficient of the jth parallel computing chip, q2 (FLOPS) is the redundant computing power of parallel computing; p is the number of neural network acceleration chips, k is the index of p, and f(c k ) is the mapping function of neural network acceleration, γ k is the mapping ratio coefficient of the kth neural network acceleration chip, and q3 (FLOPS) is the redundant computing power of neural network acceleration.

[0012] Furthermore, step S3 includes the following sub-steps:

[0013] Step S3.1: Calculate the discreteness [a1, ..., a1] of each piece of business data stored in the memory space according to the preset standard business data. i , ..., a n ], as the secondary index of the hierarchical analysis method; where n represents the total number of business data types, a i Indicates the discreteness of the i-th type of business data;

[0014] Step S3.2: Determine the evaluation score [b1, ..., b2] of each piece of business data according to its discreteness. i , ..., b n ], as the first-level indicator of the hierarchical analysis method; among them, b i represents the evaluation score of the i-th type of business data;

[0015] Step S3.3: Multiply the secondary index and the primary index to obtain the value score of each business data [q1, ..., q i , ..., q n ]:

[0016] [q1, ..., q i , ..., q n ] T =[a1, ..., a i , ..., a n ] T [b1, ..., b i , ..., b n ]

[0017] Among them, q i Indicates the value score of the i-th type of business data;

[0018] Step S3.4: Weight the value score of each piece of business data to obtain the asset value score Q of each piece of business data:

[0019] Q=[w1,...,w i , ..., w n ][q1,...,q i , ..., q n ] T

[0020] Among them, w i represents the weight of the ith business type in the business type, w1+...+w i +...+w n =1;

[0021] Step S3.5, divide the asset value of the business data into four levels according to the asset value score Q: when 0≤Q<25, the asset value of the business data is the weakest; when 25≤Q<50, the asset value of the business data is relatively weak; when 50≤Q<75, the asset value of the business data is relatively strong; when 75≤Q<100, the asset value of the business data is the strongest.

[0022] Furthermore, the calculation process of the discreteness of each piece of business data in step S3.1 is:

[0023]

[0024] Among them, [x1,...,x i ,...,x n ] represents a piece of business data, x i represents the business data of the i-th type, Indicates preset standard business data. Represents the standard business data preset for the i-th type.

[0025] Furthermore, the process of determining the evaluation score of the business data in step S3.2 is as follows: when a i = 0, bi =100; when 0<a i When ≤0.2, b i =80; when 0.2<a i When ≤0.4, b i =60; when 0.4<a i When ≤0.6, b i =40; when 0.6<a i When ≤0.8, b i =20; when 0.8<a i When <1, b i =10; when a i =1, b i =0.

[0026] Furthermore, the present invention also provides a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the business data assetization method based on an end-to-end intelligent operation and maintenance integrated machine.

[0027] Furthermore, the present invention also provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the business data assetization method based on the end-to-end intelligent operation and maintenance integrated machine is implemented.

[0028] Furthermore, the present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements the business data assetization method based on an end-to-end intelligent operation and maintenance integrated machine.

[0029] Compared with the prior art, the present invention has the following beneficial effects: the business data assetization method based on the end-to-end intelligent operation and maintenance integrated machine of the present invention needs to calculate the idle computing power required for business data transmission when the business data is transmitted to the end-to-end intelligent operation and maintenance integrated machine, so as to select the appropriate thread for business data transmission, thereby improving the speed and efficiency of real-time transmission of business data, and will not terminate the data transmission due to insufficient computing power, so that all business data can be stored in the memory space of the end-to-end intelligent operation and maintenance integrated machine, thereby providing sufficient data support for subsequent business data assetization and improving the accuracy of business data assetization; at the same time, the present invention realizes business data assetization through the hierarchical analysis method, which is suitable for business data assetization in all walks of life, improves the credibility and value of business data assetization, and can also flexibly adjust and optimize the primary indicators and business weights according to actual conditions to adapt to different decision-making needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a flow chart of the business data assetization method based on the end-to-end intelligent operation and maintenance integrated machine of the present invention;

[0031] Figure 2 The present invention is a flowchart for determining the asset value of a piece of business data based on the hierarchical analysis method. DETAILED DESCRIPTION

[0032] The technical solution of the present invention is further explained below in conjunction with the accompanying drawings.

[0033] like Figure 1 The flowchart of the business data assetization method based on the end-to-end intelligent operation and maintenance integrated machine of the present invention is as follows:

[0034] Step S1: deploy a computing power quantization model on an end-to-end intelligent operation and maintenance integrated machine; the computing power quantization model in the present invention is specifically:

[0035]

[0036] Among them, C br is the idle computing power, n is the number of logic operation chips, i is the index of n, f(a i ) is the mapping function of logical operation, α i is the mapping ratio coefficient of the i-th logic operation chip, q1 (TOPS) is the redundant computing power of the logic operation; m is the number of parallel computing chips, j is the index of m, f(b j ) is the mapping function for parallel computing, β j is the mapping ratio coefficient of the jth parallel computing chip, q2 (FLOPS) is the redundant computing power of parallel computing; p is the number of neural network acceleration chips, k is the index of p, and f(c k ) is the mapping function of neural network acceleration, γ k is the mapping ratio coefficient of the kth neural network acceleration chip, and q3 (FLOPS) is the redundant computing power of neural network acceleration.

[0037] Step S2: Calculate the idle computing power required for the end-to-end intelligent operation and maintenance integrated machine to collect a piece of business data in real time through a computing power quantification model, use a thread in the end-to-end intelligent operation and maintenance integrated machine that is not less than the calculated idle computing power to transmit the business data, and store the transmitted business data in the memory space of the end-to-end intelligent operation and maintenance integrated machine. By fully considering the idle computing power required for business data transmission, the appropriate thread is selected for business data transmission, thereby improving the speed and efficiency of real-time transmission of business data, and will not terminate data transmission due to insufficient computing power, so that all business data can be stored in the memory space of the end-to-end intelligent operation and maintenance integrated machine, thereby providing sufficient data support for subsequent business data assetization and improving the accuracy of business data assetization.

[0038] Step S3, calculate the discreteness of each business data stored in the memory space according to the preset standard business data, and determine the asset value of a business data based on the hierarchical analysis method, and provide a universal method for business data assetization, which is applicable to business data assetization in all walks of life and improves the credibility and value of business data assetization; Figure 2 , specifically including the following sub-steps:

[0039] Step S3.1: Calculate the discreteness of each piece of business data stored in the memory space according to the preset standard business data. As the secondary indicator of the hierarchical analysis method; where n represents the total number of business data types, a i represents the discreteness of the business data of the i-th type, [x1,...,x i ,...,x n ] represents a piece of business data, x i represents the business data of the i-th type, Indicates preset standard business data. Represents the preset standard business data of the i-th type. The discreteness of business data can quantify the real distribution of business data, provide a reliable basis for data assetization, and use the discreteness of business data as a secondary evaluation indicator of the hierarchical analysis method to ensure the rationality and effectiveness of data assetization.

[0040] Step S3.2: Determine the evaluation score [b1,...,b i ,...,b n ], as a primary indicator of the hierarchical analysis method, the stability of business data can be evaluated through the dispersion of business data. Therefore, the reliability of the evaluation score of business data determined based on the dispersion of business data is higher, thereby ensuring the accuracy and credibility of business data assetization. Specifically, when a v = 0, b i =100; when 0 i When ≤0.2, b i =80; when 0.2 i When ≤0.4, b i =60; when 0.4 i When ≤0.6, b i =40; when 0.6 i When ≤0.8, b i =20; when 0.8 i When <1, b i =10; when a i =1, b i =0; where b i ​​​​​It represents the evaluation score of the i-th type of business data. When the discreteness of the business data is 0, the business data is consistent with the preset standard business data, indicating that the business data has a very high asset value and the set evaluation score is the highest. When the discreteness of the business data is 1, it means that there is a large gap between the business data and the preset standard business data, which may be abnormal business data and its asset value is not high, so its evaluation score is set to 0. Business data with a discreteness between 0-1 all have asset value, but as the discreteness value decreases, its asset value increases.

[0041] Step S3.3: Multiply the secondary index and the primary index to obtain the value score of each business data [q1, ..., q i , ..., q n ]:

[0042] [q1, ..., q i , ..., q n ] T =[a1, ..., a i , ..., a n ] T [b1, ..., b i , ..., b n ]

[0043] Among them, q i Represents the value score of the i-th type of business data.

[0044] Step S3.4: Weight the value score of each piece of business data to obtain the asset value score Q of each piece of business data:

[0045] Q=[w1,...,w i , ..., w n ][q1,...,q i , ..., q n ] T

[0046] Among them, w i represents the weight of the ith business type in the business type, W1+...+w i +...+w n = 1. The value score of business data is weighted according to the weight of the business type, so that the asset value of business data is closer to the actual business scenario, and the importance of key business type data can be highlighted, improving the accuracy of asset value assessment of business data.

[0047] Step S3.5, divide the asset value of the business data into four levels according to the asset value score Q: when 0≤Q<25, the asset value of the business data is the weakest; when 25≤Q<50, the asset value of the business data is relatively weak; when 50≤Q<75, the asset value of the business data is relatively strong; when 75≤Q<100, the asset value of the business data is the strongest.

[0048] In a technical solution of the present invention, the primary indicators and business weights in the hierarchical analysis method can be adjusted according to actual needs to adapt to different decision-making needs.

[0049] The business data assetization method based on the end-to-end intelligent operation and maintenance integrated machine of the present invention gives priority to computing power space when the business data is transmitted to the end-to-end intelligent operation and maintenance integrated machine, ensures real-time transmission of business data, improves the accuracy of business data assetization, and proposes a generally applicable business data assetization method, which improves the credibility and value of business data assetization.

[0050] In a technical solution of the present invention, a computer-readable storage medium is also provided, storing a computer program, wherein the computer program enables a computer to execute the business data assetization method based on an end-to-end intelligent operation and maintenance integrated machine.

[0051] In a technical solution of the present invention, an electronic device is also provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the business data assetization method based on the end-to-end intelligent operation and maintenance integrated machine is implemented.

[0052] In a technical solution of the present invention, a computer program product is also provided, including a computer program, which, when executed by a processor, implements the business data assetization method based on an end-to-end intelligent operation and maintenance integrated machine.

[0053] In the embodiments disclosed in the present application, the computer storage medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. The computer storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the above. More specific examples of computer storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above.

[0054] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0055] The above are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should be regarded as the protection scope of the present invention.

Claims

1. A business data assetization method based on an end-to-end intelligent operation and maintenance integrated machine, characterized in that: The specific steps include: Step S1: deploy a computing power quantization model on an end-to-end intelligent operation and maintenance integrated machine; Step S2: Calculate the idle computing power required by the end-to-end intelligent operation and maintenance integrated machine to collect a piece of business data in real time through a computing power quantification model, use a thread in the end-to-end intelligent operation and maintenance integrated machine that is not less than the calculated idle computing power to transmit the business data, and store the transmitted business data in the memory space of the end-to-end intelligent operation and maintenance integrated machine; Step S3: Calculate the discreteness of each piece of business data stored in the memory space according to the preset standard business data, and determine the asset value of a piece of business data based on the hierarchical analysis method.

2. According to claim 1, a method for assetizing business data based on an end-to-end intelligent operation and maintenance integrated machine is characterized in that: The specific calculation power quantification model in step S1 is: Among them, C br is the idle computing power, n is the number of logic operation chips, i is the index of n, f(a i ) is the mapping function of logical operation, α i is the mapping ratio coefficient of the i-th logic operation chip, q1 (TOPS) is the redundant computing power of the logic operation; m is the number of parallel computing chips, j is the index of m, f(b j ) is the mapping function for parallel computing, β j is the mapping ratio coefficient of the jth parallel computing chip, q2 (FLOPS) is the redundant computing power of parallel computing; p is the number of neural network acceleration chips, k is the index of p, and f(c k ) is the mapping function of neural network acceleration, γ k is the mapping ratio coefficient of the kth neural network acceleration chip, and q3 (FLOPS) is the redundant computing power of neural network acceleration.

3. The method for capitalizing business data based on an end-to-end intelligent operation and maintenance integrated machine according to claim 1, characterized in that: Step S3 includes the following sub-steps: Step S3.1: Calculate the discreteness [a1,...,a i ,...,a n ], as the secondary index of the hierarchical analysis method; where n represents the total number of business data types, a i Indicates the discreteness of the i-th type of business data; Step S3.2: Determine the evaluation score [b1,...,b i ,...,b n ], as the first-level indicator of the hierarchical analysis method; among them, b i represents the evaluation score of the i-th type of business data; Step S3.3: Multiply the secondary index and the primary index to obtain the value score of each business data [q1,...,q i ,...,q n ]: [q1,...,q i ,...,q n ] T =[a1,...,a i ,...,a n ] T [b1,...,b i ,...,b n ] Among them, q i Indicates the value score of the i-th type of business data; Step S3.4: Weight the value score of each piece of business data to obtain the asset value score Q of each piece of business data: Q=[w1,...,w i ,...,w n ][q1,...,q i ,...,q n ] T Among them, w i represents the weight of the ith business type in the business type, w1+...+w i +...+w n =1; Step S3.5, divide the asset value of the business data into four levels according to the asset value score Q: when 0≤Q<25, the asset value of the business data is the weakest; when 25≤Q<50, the asset value of the business data is relatively weak; when 50≤Q<75, the asset value of the business data is relatively strong; when 75≤Q<100, the asset value of the business data is the strongest.

4. A method for capitalizing business data based on an end-to-end intelligent operation and maintenance integrated machine according to claim 3, characterized in that: The calculation process of the discreteness of each piece of business data in step S3.1 is: Among them, [x1,...,x i ,...,x n ] represents a piece of business data, x i Represents the i-th type of business data, Indicates preset standard business data. Represents the standard business data preset for the i-th type.

5. The method for capitalizing business data based on an end-to-end intelligent operation and maintenance integrated machine according to claim 3 is characterized in that: The process of determining the evaluation score of the business data in step S3.2 is as follows: i = 0, b i =100; when 0<a i When ≤0.2, b i =80; when 0.2<a i When ≤0.4, b i =60; when 0.4<a i When ≤0.6, b i =40; When 0.6<a i When ≤0.8, b i =20; when 0.8<a i When <1, b i =10; when a i =1, b i =0.

6. A computer-readable storage medium storing a computer program, characterized in that: The computer program enables the computer to execute the business data assetization method based on the end-to-end intelligent operation and maintenance integrated machine as described in any one of claims 1-5.

7. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for assetizing business data based on an end-to-end intelligent operation and maintenance integrated machine as described in any one of claims 1 to 5 is implemented.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for capitalizing business data based on an end-to-end intelligent operation and maintenance integrated machine as described in any one of claims 1 to 5 is implemented.

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