CN111694844A - Enterprise operation data analysis method and device based on configuration algorithm and electronic equipment - Google Patents

Enterprise operation data analysis method and device based on configuration algorithm and electronic equipment Download PDF

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CN111694844A
CN111694844A CN202010471293.7A CN202010471293A CN111694844A CN 111694844 A CN111694844 A CN 111694844A CN 202010471293 A CN202010471293 A CN 202010471293A CN 111694844 A CN111694844 A CN 111694844A
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CN111694844B (en
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陈喜涛
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Ping An Technology Shenzhen Co Ltd
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Abstract

The invention relates to a big data technology, and discloses an enterprise operation data analysis method based on a configuration algorithm, which comprises the following steps: acquiring an algorithm configuration main table, an algorithm configuration parameter table and an algorithm parameter value table; selecting one algorithm in the algorithm configuration main table, and calculating the selected algorithm to obtain an output variable set; evaluating and calculating the output variable set to obtain a core correlation value; calculating an error value of the core correlation value, and configuring the selected algorithm by using the parameters and the parameter values when the error value is less than or equal to a preset error threshold value; and acquiring enterprise data, and performing data analysis on the enterprise data by using a configured algorithm to obtain the operation condition of the enterprise. The invention also relates to a blockchain technology, wherein the algorithm configuration main table, the algorithm configuration parameter table and the algorithm parameter value table are stored in the blockchain. The invention can solve the problems of high cost and low efficiency of enterprise operation data analysis.

Description

Enterprise operation data analysis method and device based on configuration algorithm and electronic equipment
Technical Field
The invention relates to the field of big data, in particular to an enterprise operation data analysis method and device based on a configuration algorithm and electronic equipment.
Background
With the rise of big data, data analysis by using a large amount of data becomes an increasingly important means, such as analyzing the operation status of an enterprise by using enterprise data, but in the process of processing data by using big data technology, a plurality of algorithms are needed, and the used algorithms are complex and diverse, so that how to realize efficient and low-cost configuration algorithms to analyze the operation status of the enterprise is more and more emphasized by people.
At present, most of algorithms are configured manually, for algorithm configuration service personnel, after a configuration method of a solution configuration table, code languages need to be learned, and code compiling capacity needs to be provided, so that human resources are consumed in the algorithm configuration process, and the problems of high cost and low efficiency of enterprise operation data analysis are caused.
Disclosure of Invention
The invention provides a method and a device for analyzing enterprise operation data based on a configuration algorithm, electronic equipment and a computer readable storage medium, and mainly aims to solve the problems of high cost and low efficiency of enterprise operation data analysis.
In order to achieve the above object, the present invention provides an enterprise operation data analysis method based on a configuration algorithm, which includes:
acquiring an algorithm configuration main table, an algorithm configuration parameter table and an algorithm parameter value table, wherein the algorithm configuration main table comprises an algorithm to be configured, the algorithm configuration parameter table comprises parameters of the algorithm in the algorithm configuration main table, and the algorithm parameter value table comprises values of all the parameters in the algorithm configuration parameter table;
sequentially selecting one algorithm in the algorithm configuration main table, selecting parameters from the algorithm configuration parameter table according to the selected algorithm, selecting parameter values from the algorithm parameter value table, and calculating the selected algorithm according to the selected parameters and the parameter values to obtain an output variable set;
evaluating and calculating the output variable set to obtain a core correlation value;
performing error calculation on the core correlation value to obtain an error value, and configuring the selected algorithm by using the selected parameter and the parameter value when the error value is less than or equal to a preset error threshold value to obtain a configured algorithm;
and acquiring enterprise data, and performing data analysis on the enterprise data by using the configured algorithm to obtain the operation condition of the enterprise.
Optionally, the sequentially selecting one of the algorithms in the algorithm configuration main table, selecting a parameter from the algorithm configuration parameter table according to the selected algorithm, selecting a parameter value from the algorithm parameter value table, and calculating the selected algorithm according to the selected parameter and the parameter value to obtain an output variable set, includes:
selecting algorithm codes according to the sequence of the algorithms of the algorithm configuration main table;
acquiring an algorithm execution scheme corresponding to the algorithm code in the algorithm configuration main table according to the algorithm code;
acquiring parameters in the algorithm configuration parameter table and parameter sequence numbers corresponding to the parameters according to the algorithm execution scheme, and acquiring algorithm calculation numbers according to the parameters and the parameter sequence numbers;
acquiring parameter values in the algorithm parameter value table according to the algorithm calculation numbers;
inputting the parameter values into the algorithm execution scheme for calculation to obtain output variables;
and repeating the operations until all the algorithms in the algorithm configuration main table are calculated, and summarizing all the output variables to obtain the output variable set.
Optionally, the evaluating and calculating the output variable set to obtain a core correlation value includes:
evaluating and calculating the output variable set by using the following evaluation algorithm to obtain a core correlation value Hcorre:
Figure BDA0002514163930000021
Figure BDA0002514163930000022
Figure BDA0002514163930000023
wherein, YiA value representing an ith output variable in the set of output variables; j represents the jth output variable in the set of output variables; n is the number of output variables in the set of output variables, WijA binary core weight matrix whose value is determined by whether the ith output variable and the jth output variable are adjacent or not,
Figure BDA0002514163930000031
and S is the standard deviation of all the output variables in the output variable set.
Optionally, the WijThe binary core weight matrix whose value is determined by whether the ith output variable and the jth output variable are adjacent or not includes:
performing adjacency calculation on the ith output variable and the jth output variable by using a preset adjacency algorithm to obtain an adjacency calculation result;
comparing the adjacency calculation result with a preset adjacency threshold value, and if the adjacency calculation result is greater than the preset adjacency threshold value, WijThe value is 1;
if the adjacency calculation result is less than or equal to the preset adjacency threshold value, WijThe value is 0.
Optionally, the performing an adjacency calculation on the ith output variable and the jth output variable by using a preset adjacency algorithm to obtain an adjacency calculation result includes:
performing adjacency calculation on the ith output variable and the jth output variable by using the following algorithm to obtain an adjacency calculation result VAR:
Figure BDA0002514163930000032
Figure BDA0002514163930000033
wherein the content of the first and second substances,
Figure BDA0002514163930000034
Figure BDA0002514163930000035
Wiis the sum of all elements in the ith row, W, in the core weight matrixjIs the sum of all elements in the jth row in the core weight matrix, and E (I) is the adjacency parameter.
Optionally, the algorithm configuration master table, the algorithm configuration parameter table, and the algorithm parameter value table are stored in a block chain, and the method further includes:
and if the error value is larger than the error threshold value, receiving a new algorithm configuration main table, an algorithm configuration parameter table and an algorithm parameter value table, and performing algorithm configuration again.
Optionally, the performing an error calculation on the core correlation value to obtain an error value includes:
an error value D for the core correlation value is calculated using the following error algorithm:
D＝Hβ+
＝λW+μ
wherein: h represents the core correlation value; wThe method comprises the steps of obtaining a core weight matrix value, representing a preset core error coefficient by lambda, representing a random error item of a core correlation value, representing a random error item of normal distribution by mu, and representing a preset coefficient by β.
In order to solve the above problem, the present invention further provides an enterprise operation data analysis device based on a configuration algorithm, where the device includes:
the system comprises an algorithm selection module, an algorithm configuration parameter table and an algorithm parameter value table, wherein the algorithm configuration main table comprises an algorithm needing configuration, the algorithm configuration parameter table comprises parameters of the algorithm in the algorithm configuration main table, and the algorithm parameter value table comprises values of all the parameters in the algorithm configuration parameter table;
the variable set calculation module is used for sequentially selecting one algorithm in the algorithm configuration main table, selecting parameters from the algorithm configuration parameter table according to the selected algorithm, selecting parameter values from the algorithm parameter value table, and calculating the selected algorithm according to the selected parameters and the parameter values to obtain an output variable set;
the core correlation value calculation module is used for evaluating and calculating the output variable set to obtain a core correlation value;
the algorithm configuration module is used for carrying out error calculation on the core correlation value to obtain an error value, and when the error value is smaller than or equal to a preset error threshold value, configuring the selected algorithm by using the selected parameter and a parameter value to obtain a configured algorithm;
and the data analysis module is used for acquiring enterprise data, and performing data analysis on the enterprise data by using the configured algorithm to obtain the operation condition of the enterprise.
In order to solve the above problem, the present invention also provides an electronic device, including:
a memory storing at least one instruction; and
and the processor executes the instructions stored in the memory to implement any one of the above-mentioned enterprise operation data analysis method based on the configuration algorithm.
In order to solve the above problem, the present invention further provides a computer-readable storage medium, where at least one instruction is stored in the computer-readable storage medium, and the at least one instruction is executed by a processor in an electronic device to implement the configuration algorithm based enterprise operation data analysis method described in any one of the above.
The embodiment of the invention automatically acquires the algorithm configuration main table, the algorithm configuration parameter table and the algorithm parameter value table, thereby saving human resources; evaluating and calculating the output variable set to obtain a core correlation value, and calculating the core correlation value of the algorithm to ensure that a unified standard is generated when the algorithm configuration is subsequently evaluated; performing error calculation on the core correlation value to obtain an error value, configuring the selected algorithm by using the selected parameter and the parameter value when the error value is less than or equal to a preset error threshold value, storing the configured algorithm in a preset algorithm database, and visually finding out whether the selected parameter and the parameter value can be configured for the algorithm or not from an error calculation result by adopting an error calculation evaluation method; and acquiring enterprise data, performing data analysis on the enterprise data by using the configured algorithm to obtain the operation condition of the enterprise, and analyzing the enterprise data by using the configured algorithm to accurately analyze the operation condition of the enterprise. Therefore, the enterprise operation data analysis method and device based on the configuration algorithm and the computer readable storage medium can realize low-cost and high-efficiency enterprise operation data analysis.
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Fig. 1 is a schematic flowchart of an enterprise operation data analysis method based on a configuration algorithm according to an embodiment of the present invention;
fig. 2 is a schematic block diagram of an enterprise operation data analysis device based on a configuration algorithm according to an embodiment of the present invention;
fig. 3 is a schematic internal structural diagram of an electronic device for an enterprise operation data analysis method based on a configuration algorithm according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of an algorithm configuration master table of an enterprise operation data analysis method based on a configuration algorithm according to an embodiment of the present invention;
fig. 5 is a schematic diagram of an algorithm configuration parameter table of an enterprise operation data analysis method based on a configuration algorithm according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of an algorithm parameter value table of an enterprise operation data analysis method based on a configuration algorithm according to an embodiment of the present invention;
the implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention provides an enterprise operation data analysis method based on a configuration algorithm. Fig. 1 is a schematic flowchart of an algorithm configuration method according to an embodiment of the present invention. The method may be performed by an apparatus, which may be implemented by software and/or hardware. For example, in the embodiment of the present invention, the method may be performed by an electronic device.
In this embodiment, the enterprise operation data analysis method based on the configuration algorithm includes:
s1, an algorithm configuration main table, an algorithm configuration parameter table and an algorithm parameter value table are obtained, wherein the algorithm configuration main table comprises algorithms needing configuration, the algorithm configuration parameter table comprises parameters of the algorithms in the algorithm configuration main table, and the algorithm parameter value table comprises values of all the parameters in the algorithm configuration parameter table.
In the embodiment of the present invention, referring to a schematic diagram of an algorithm configuration main table shown in fig. 4, the algorithm configuration main table records the output variable type of each algorithm, the execution scheme of a specific algorithm, the execution sequence of each algorithm, and the like.
Further, referring to the schematic diagram of the algorithm configuration parameter table shown in fig. 5, the algorithm configuration parameter table records the input parameters and parameter names required by each algorithm, and the numbers of each input parameter.
Further, refer to a schematic diagram of an algorithm parameter value table shown in fig. 6, which records parameter values required for each algorithm calculation.
S2, one of the algorithms in the algorithm configuration main table is selected in sequence, parameters are selected from the algorithm configuration parameter table according to the selected algorithm, parameter values are selected from the algorithm parameter value table, and the selected algorithm is calculated according to the selected parameters and the parameter values to obtain an output variable set.
In detail, the S2 includes:
selecting algorithm codes according to the algorithm sequence of the algorithm configuration main table;
acquiring an algorithm execution scheme corresponding to the algorithm code in the algorithm configuration main table according to the algorithm code;
acquiring parameters in the algorithm configuration parameter table and parameter sequence numbers corresponding to the parameters according to the algorithm execution scheme, and acquiring algorithm calculation numbers according to the parameters and the parameter sequence numbers;
acquiring parameter values in the algorithm parameter value table according to the algorithm calculation numbers;
and substituting the parameter values into the algorithm execution scheme for calculation to obtain output variables, and summarizing all the output variables to obtain an algorithm output variable set until all the algorithms in the algorithm configuration master table are calculated.
For example: in the algorithm sequence of the algorithm configuration main table, the specific algorithm execution scheme of the first algorithm code 'Bel 1' is A + B;
according to the parameters in the specific algorithm execution scheme, finding a parameter A and a parameter B of an algorithm code 'Bel 1' and parameter sequence numbers of the relevant parameter A and parameter B are respectively 1 and 2 in the algorithm configuration parameter table, and obtaining algorithm calculation numbers A1 and B2;
and finding the parameter values with the parameter variable name BEL _ SB in the algorithm parameter value table to be 1 and 2 respectively according to the algorithm calculation numbers A1 and B2.
And substituting the parameter value into an algorithm A + B to obtain 1+2, and finally obtaining a result of an output variable BEL _ INIT in the algorithm configuration main table to be 3.
Repeating the steps until all algorithms of the algorithm configuration main table are calculated to obtain output variables, and summarizing all the output variables of the algorithm configuration main table to obtain the output variable set.
And S3, evaluating and calculating the output variable set to obtain a core correlation value.
In the embodiment of the present invention, in order to evaluate whether the calculation of the algorithm configuration main table, the algorithm configuration parameter table, and the algorithm parameter value table is reasonable, a preset evaluation algorithm needs to be used to perform evaluation analysis calculation on the algorithm output variable set to obtain the core correlation value;
in the embodiment of the invention, the following evaluation algorithm is used for evaluating, analyzing and calculating the output variable set to obtain a core correlation value Hcorre:
Figure BDA0002514163930000071
Figure BDA0002514163930000072
Figure BDA0002514163930000073
wherein, YiA value representing an ith output variable in the set of output variables; j represents the jth output variable in the set of output variables; n is the number of output variables in the set of output variables, WijIs a binary core weight matrix and is,
Figure BDA0002514163930000074
and S is the standard deviation of all the output variables in the output variable set.
Further, the core weight matrix WijThe value taking method comprises the following steps:
performing adjacency calculation on the ith output variable and the jth output variable by using a preset adjacency algorithm to obtain an adjacency calculation result;
comparing the adjacency calculation result with a preset adjacency threshold value, and if the adjacency calculation result is greater than the preset adjacency threshold value, WijThe value is 1;
if the adjacency calculation result is less than or equal to the preset adjacency threshold value, WijThe value is 0.
Further, in the embodiment of the present invention, the following algorithm is used to perform adjacency calculation on the ith output variable and the jth output variable, so as to obtain an adjacency calculation result VAR:
Figure BDA0002514163930000081
Figure BDA0002514163930000082
wherein the content of the first and second substances,
Figure BDA0002514163930000083
Figure BDA0002514163930000084
Wiis the sum of all elements in the ith row, W, in the core weight matrixjIs the sum of all elements in the jth row in the core weight matrix, and E (I) is the adjacency parameter.
In detail, the determining whether the output variable i and the output variable j are adjacent according to the calculation result includes:
comparing and judging the calculation result VAR with a preset threshold value 0, if the calculation result VAR is larger than 0, indicating that the output variable i is adjacent to the output variable j, and Wij＝1；
If the VAR is less than or equal to 0, the output variable i and the output variable j are not adjacent, Wij＝0。
And S4, performing error calculation on the core correlation value to obtain an error value, and when the error value is smaller than or equal to a preset error threshold value, configuring the selected algorithm by using the selected parameter and the parameter value to obtain a configured algorithm.
If the error value is less than or equal to the error threshold value, the algorithm configuration main table, the algorithm configuration parameter table and the algorithm parameter value table are available, and the selected algorithm is configured according to the algorithm configuration main table, the algorithm configuration parameter table and the algorithm parameter value table.
The embodiment of the invention calculates the error value D of the core correlation value by using the following error algorithm:
D＝Hβ+
＝λW∈+μ
wherein; d represents the error value; h represents the core correlation value; wThe method comprises the following steps of obtaining a core weight matrix value, representing a preset core error coefficient by lambda, representing a core correlation value random error item, representing a normally distributed random error item by mu, and representing a preset coefficient by β, wherein the preset coefficient represents the influence degree of H on D.
Preferably, the preset error threshold may be 0.8.
If the error value is less than or equal to 0.8, the algorithm configuration master table, the algorithm configuration parameter table, and the algorithm parameter value table received in S1 satisfy the algorithm configuration requirement, and the algorithm configuration can be directly completed according to the algorithm configuration master table, the algorithm configuration parameter table, and the algorithm parameter value table, so as to obtain a configured algorithm.
Further, the embodiment of the present invention further includes storing the configured algorithm in a pre-constructed algorithm database.
In the embodiment of the invention, the algorithm database can be an existing cloud database, and can also be a local disk, a hard disk and the like of a computer.
Further, the embodiment of the present invention further includes:
if the error value is larger than the error threshold value, the algorithm configuration main table, the algorithm configuration parameter table and the algorithm parameter value table are indicated to be unavailable, receiving a new algorithm configuration main table, a new algorithm configuration parameter table and a new algorithm parameter value table, and performing algorithm configuration again. It should be emphasized that, in order to further ensure the privacy and security of the above algorithm configuration master table, algorithm configuration parameter table, and algorithm parameter value table, the above algorithm configuration master table, algorithm configuration parameter table, and algorithm parameter value table may also be stored in a node of a block chain.
For example, if the error value is greater than 0.8, the algorithm configuration master table, the algorithm configuration parameter table, and the algorithm parameter value table received in S1 do not satisfy the algorithm configuration requirement, and need to be received again.
And S5, acquiring enterprise data, and performing data analysis on the enterprise data by using the configured algorithm to obtain the operation condition of the enterprise.
Further, the enterprise data may be obtained directly from an existing database, including, but not limited to, cash flow status of the enterprise, credit rating of the enterprise, liability status of the enterprise, the database may be a mysql database, an Oracle database, and the like.
Preferably, after the enterprise data is obtained, the embodiment of the present invention analyzes the enterprise data by using the configured algorithm to obtain an analysis result, and then determines the operation status of the enterprise according to the analysis result.
Fig. 2 is a functional block diagram of the enterprise operation data analysis device based on the configuration algorithm according to the present invention.
The enterprise operation data analysis device 100 based on the configuration algorithm can be installed in electronic equipment. According to the realized functions, the enterprise operation data analysis device based on the configuration algorithm can comprise an algorithm selection module 101, a variable set calculation module 102, a core correlation value calculation module 103, an algorithm configuration module 104 and a data analysis module 105. A module according to the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the algorithm selection module 101 is configured to obtain an algorithm configuration master table, an algorithm configuration parameter table and an algorithm parameter value table, where the algorithm configuration master table includes an algorithm to be configured, the algorithm configuration parameter table includes parameters of the algorithm in the algorithm configuration master table, and the algorithm parameter value table includes values of the parameters in the algorithm configuration parameter table;
the variable set calculating module 102 is configured to sequentially select one of the algorithms in the algorithm configuration main table, select a parameter from the algorithm configuration parameter table according to the selected algorithm, select a parameter value from the algorithm parameter value table, and calculate the selected algorithm according to the selected parameter and the parameter value to obtain an output variable set;
the core correlation value calculating module 103 is configured to evaluate and calculate the output variable set to obtain a core correlation value;
the algorithm configuration module 104 is configured to perform error calculation on the core correlation value to obtain an error value, and configure the selected algorithm by using the selected parameter and the parameter value when the error value is less than or equal to a preset error threshold value to obtain a configured algorithm;
and the data analysis module 105 is configured to acquire enterprise data, and perform data analysis on the enterprise data by using the configured algorithm to obtain an operating condition of the enterprise.
In detail, the specific implementation steps of each module of the enterprise operation data analysis device based on the configuration algorithm are as follows:
the algorithm selection module 101 obtains an algorithm configuration main table, an algorithm configuration parameter table and an algorithm parameter value table, wherein the algorithm configuration main table includes an algorithm to be configured, the algorithm configuration parameter table includes parameters of the algorithm in the algorithm configuration main table, and the algorithm parameter value table includes values of the parameters in the algorithm configuration parameter table.
In the embodiment of the present invention, referring to a schematic diagram of an algorithm configuration main table shown in fig. 4, the algorithm configuration main table records the output variable type of each algorithm, the execution scheme of a specific algorithm, the execution sequence of each algorithm, and the like.
Further, referring to the schematic diagram of the algorithm configuration parameter table shown in fig. 5, the algorithm configuration parameter table records the input parameters and parameter names required by each algorithm, and the numbers of each input parameter.
Further, refer to a schematic diagram of an algorithm parameter value table shown in fig. 6, which records parameter values required for each algorithm calculation.
The variable set calculation module 102 sequentially selects one of the algorithms in the algorithm configuration main table, selects parameters from the algorithm configuration parameter table according to the selected algorithm, selects parameter values from the algorithm parameter value table, and calculates the selected algorithm according to the selected parameters and the parameter values to obtain an output variable set.
In detail, the variable set calculation module 102 sequentially selects algorithm codes according to the algorithm of the algorithm configuration master table; acquiring an algorithm execution scheme corresponding to the algorithm code in the algorithm configuration main table according to the algorithm code; acquiring parameters in the algorithm configuration parameter table and parameter sequence numbers corresponding to the parameters according to the algorithm execution scheme, and acquiring algorithm calculation numbers according to the parameters and the parameter sequence numbers; acquiring parameter values in the algorithm parameter value table according to the algorithm calculation numbers; and substituting the parameter values into the algorithm execution scheme for calculation to obtain output variables, and summarizing all the output variables to obtain an algorithm output variable set until all the algorithms in the algorithm configuration master table are calculated.
For example: in the algorithm sequence of the algorithm configuration main table, the specific algorithm execution scheme of the first algorithm code 'Bel 1' is A + B;
according to the parameters in the specific algorithm execution scheme, finding a parameter A and a parameter B of an algorithm code 'Bel 1' and parameter sequence numbers of the relevant parameter A and parameter B are respectively 1 and 2 in the algorithm configuration parameter table, and obtaining algorithm calculation numbers A1 and B2;
and finding the parameter values with the parameter variable name BEL _ SB in the algorithm parameter value table to be 1 and 2 respectively according to the algorithm calculation numbers A1 and B2.
And substituting the parameter value into an algorithm A + B to obtain 1+2, and finally obtaining a result of an output variable BEL _ INIT in the algorithm configuration main table to be 3.
Repeating the steps until all algorithms of the algorithm configuration main table are calculated to obtain output variables, and summarizing all the output variables of the algorithm configuration main table to obtain the output variable set.
The core correlation value calculation module 103 performs evaluation calculation on the output variable set to obtain a core correlation value.
In the embodiment of the present invention, in order to evaluate whether the calculation of the algorithm configuration main table, the algorithm configuration parameter table, and the algorithm parameter value table is reasonable, a preset evaluation algorithm needs to be used to perform evaluation analysis calculation on the algorithm output variable set to obtain the core correlation value;
in the embodiment of the invention, the following evaluation algorithm is used for evaluating, analyzing and calculating the output variable set to obtain a core correlation value Hcorre:
Figure BDA0002514163930000121
Figure BDA0002514163930000122
Figure BDA0002514163930000123
wherein, YiA value representing an ith output variable in the set of output variables; j represents the jth output variable in the set of output variables; n is the number of output variables in the set of output variables, WijIs a binary core weight matrix and is,
Figure BDA0002514163930000124
and S is the standard deviation of all the output variables in the output variable set.
Further, the core weight matrix WijThe value taking method comprises the following steps:
performing adjacency calculation on the ith output variable and the jth output variable by using a preset adjacency algorithm to obtain an adjacency calculation result;
comparing the adjacency calculation result with a preset adjacency threshold value, and if the adjacency calculation result is greater than the preset adjacency threshold value, WijThe value is 1;
if the adjacency calculation result is less than or equal to the preset adjacency threshold value, WijThe value is 0.
Further, performing adjacency calculation by using the ith output variable and the jth output variable according to the following algorithm to obtain an adjacency calculation result VAR:
Figure BDA0002514163930000125
Figure BDA0002514163930000126
wherein the content of the first and second substances,
Figure BDA0002514163930000131
Figure BDA0002514163930000132
Wiis the sum of all elements in the ith row, W, in the core weight matrixjIs the sum of all elements in the jth row in the core weight matrix, and E (I) is the adjacency parameter.
In detail, the determining whether the output variable i and the output variable j are adjacent according to the calculation result includes:
comparing and judging the calculation result VAR with a preset threshold value 0, if the calculation result VAR is larger than 0, indicating that the output variable i is adjacent to the output variable j, and Wij＝1；
If the VAR is less than or equal to 0, the output variable i and the output variable j are not adjacent, Wij＝0。
The algorithm configuration module 104 performs error calculation on the core correlation value to obtain an error value, and configures the selected algorithm by using the selected parameter and the parameter value when the error value is less than or equal to a preset error threshold value to obtain a configured algorithm.
Further, if the error value is greater than the error threshold value, which indicates that the algorithm configuration master table, the algorithm configuration parameter table, and the algorithm parameter value table are not available, the algorithm selection module 101 receives a new algorithm configuration master table, a new algorithm configuration parameter table, and a new algorithm parameter value table, and performs algorithm configuration again;
the embodiment of the invention calculates the error value D of the core correlation value by using the following error algorithm:
D＝Hβ+
＝λW+μ
wherein; d represents the error value; h represents the core correlation value; wThe method comprises the following steps of obtaining a core weight matrix value, representing a preset core error coefficient by lambda, representing a core correlation value random error item, representing a normally distributed random error item by mu, and representing a preset coefficient by β, wherein the preset coefficient represents the influence degree of H on D.
Preferably, the preset error threshold may be 0.8.
If the error value is greater than 0.8, the algorithm configuration master table, the algorithm configuration parameter table, and the algorithm parameter value table received by the algorithm selection module 101 do not meet the algorithm configuration requirement and need to be received again.
If the error value is less than or equal to 0.8, the algorithm configuration master table, the algorithm configuration parameter table, and the algorithm parameter value table received by the algorithm selection module 101 meet the algorithm configuration requirement, and the algorithm configuration can be directly completed according to the algorithm configuration master table, the algorithm configuration parameter table, and the algorithm parameter value table.
Further, the configured algorithms may be stored in a pre-built algorithm database.
In the embodiment of the invention, the algorithm database can be an existing cloud database, and can also be a local disk, a hard disk and the like of a computer.
And the data analysis module 105 is configured to acquire enterprise data, and perform data analysis on the enterprise data by using the configured algorithm to obtain an operating condition of the enterprise.
Further, the enterprise data may be obtained directly from an existing database, including, but not limited to, cash flow status of the enterprise, credit rating of the enterprise, liability status of the enterprise, the database may be a mysql database, an Oracle database, and the like.
Preferably, after the enterprise data is obtained, the embodiment of the present invention analyzes the enterprise data by using the configured algorithm to obtain an analysis result, and then determines the operation status of the enterprise according to the analysis result.
Fig. 3 is a schematic structural diagram of an electronic device implementing the enterprise operation data analysis method based on the configuration algorithm according to the present invention.
The electronic device 1 may comprise a processor 10, a memory 11 and a bus, and may further comprise a computer program stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, which includes flash memory, removable hard disk, multimedia card, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only to store application software installed in the electronic device 1 and various types of data, such as codes of an algorithm configuration program, but also to temporarily store data that has been output or is to be output.
The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device 1 by running or executing programs or modules (for example, executing an enterprise operation data analysis program based on a configuration algorithm, etc.) stored in the memory 11 and calling data stored in the memory 11.
The bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.
Fig. 3 shows only an electronic device with components, and it will be understood by those skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than those shown, or some components may be combined, or a different arrangement of components.
For example, although not shown, the electronic device 1 may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so as to implement functions of charge management, discharge management, power consumption management, and the like through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device 1 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
Further, the electronic device 1 may further include a network interface, and optionally, the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a bluetooth interface, etc.), which are generally used for establishing a communication connection between the electronic device 1 and other electronic devices.
Optionally, the electronic device 1 may further comprise a user interface, which may be a Display (Display), an input unit (such as a Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable for displaying information processed in the electronic device 1 and for displaying a visualized user interface, among other things.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The algorithm configuration program 12 stored in the memory 11 of the electronic device 1 is a combination of instructions that, when executed in the processor 10, may implement:
acquiring an algorithm configuration main table, an algorithm configuration parameter table and an algorithm parameter value table, wherein the algorithm configuration main table comprises an algorithm to be configured, the algorithm configuration parameter table comprises parameters of the algorithm in the algorithm configuration main table, and the algorithm parameter value table comprises values of all the parameters in the algorithm configuration parameter table;
sequentially selecting one algorithm in the algorithm configuration main table, selecting parameters from the algorithm configuration parameter table according to the selected algorithm, selecting parameter values from the algorithm parameter value table, and calculating the selected algorithm according to the selected parameters and the parameter values to obtain an output variable set;
evaluating and calculating the output variable set to obtain a core correlation value;
performing error calculation on the core correlation value to obtain an error value, and configuring the selected algorithm by using the selected parameter and the parameter value when the error value is less than or equal to a preset error threshold value to obtain a configured algorithm;
and acquiring enterprise data, and performing data analysis on the enterprise data by using the configured algorithm to obtain the operation condition of the enterprise. It should be emphasized that, in order to further ensure the privacy and security of the above algorithm configuration master table, algorithm configuration parameter table, and algorithm parameter value table, the above algorithm configuration master table, algorithm configuration parameter table, and algorithm parameter value table may also be stored in a node of a block chain.
Specifically, the specific implementation method of the processor 10 for the above instruction may refer to the description of the relevant steps in the embodiment corresponding to fig. 2, which is not repeated herein.
Further, the integrated modules/units of the electronic device 1, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. The computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims (10)

1. An enterprise operation data analysis method based on a configuration algorithm is applied to electronic equipment and comprises the following steps:
acquiring an algorithm configuration main table, an algorithm configuration parameter table and an algorithm parameter value table, wherein the algorithm configuration main table comprises an algorithm to be configured, the algorithm configuration parameter table comprises parameters of the algorithm in the algorithm configuration main table, and the algorithm parameter value table comprises values of all the parameters in the algorithm configuration parameter table;
sequentially selecting one algorithm in the algorithm configuration main table, selecting parameters from the algorithm configuration parameter table according to the selected algorithm, selecting parameter values from the algorithm parameter value table, and calculating the selected algorithm according to the selected parameters and the parameter values to obtain an output variable set;
evaluating and calculating the output variable set to obtain a core correlation value;
performing error calculation on the core correlation value to obtain an error value, and configuring the selected algorithm by using the selected parameter and the parameter value when the error value is less than or equal to a preset error threshold value to obtain a configured algorithm;
and acquiring enterprise data, and performing data analysis on the enterprise data by using the configured algorithm to obtain the operation condition of the enterprise.
2. The method of claim 1, wherein the sequentially selecting one of the algorithms in the main algorithm configuration table, selecting parameters from the parameter algorithm configuration table according to the selected algorithm, selecting parameter values from the parameter algorithm parameter table, and calculating the selected algorithm according to the selected parameters and the parameter values to obtain an output variable set comprises:
selecting algorithm codes according to the sequence of the algorithms of the algorithm configuration main table;
acquiring an algorithm execution scheme corresponding to the algorithm code in the algorithm configuration main table according to the algorithm code;
acquiring parameters in the algorithm configuration parameter table and parameter sequence numbers corresponding to the parameters according to the algorithm execution scheme, and acquiring algorithm calculation numbers according to the parameters and the parameter sequence numbers;
acquiring parameter values in the algorithm parameter value table according to the algorithm calculation numbers;
inputting the parameter values into the algorithm execution scheme for calculation to obtain output variables;
and repeating the operations until all the algorithms in the algorithm configuration main table are calculated, and summarizing all the output variables to obtain the output variable set.
3. The method for analyzing enterprise operation data based on configuration algorithm as claimed in claim 1, wherein said evaluating and calculating said output variable set to obtain core correlation value comprises:
evaluating and calculating the output variable set by using the following evaluation algorithm to obtain a core correlation value Hcorre:
Figure FDA0002514163920000021
Figure FDA0002514163920000022
Figure FDA0002514163920000023
wherein, YiA value representing the ith output variable in the set of output variables, j represents the jth output variable in the set of output variables, n is the number of output variables in the set of output variables, WijA binary core weight matrix whose value is determined by whether the ith output variable and the jth output variable are adjacent or not,
Figure FDA0002514163920000024
and S is the standard deviation of all the output variables in the output variable set.
4. The configuration algorithm-based enterprise operational data analysis method of claim 3, wherein W isijIs a binary core weight matrix, WijThe value is determined by whether the ith output variable and the jth output variable are adjacent or not, and the method comprises the following steps:
performing adjacency calculation on the ith output variable and the jth output variable by using a preset adjacency algorithm to obtain an adjacency calculation result;
comparing the adjacency calculation result with a preset adjacency threshold value, and if the adjacency calculation result is greater than the preset adjacency threshold value, WijThe value is 1;
if the adjacency calculation result is less than or equal to the preset adjacency threshold value, WijThe value is 0.
5. The configuration algorithm-based enterprise operation data analysis method according to claim 4, wherein the performing adjacency calculation on the ith output variable and the jth output variable by using a preset adjacency algorithm to obtain an adjacency calculation result comprises:
performing adjacency calculation on the ith output variable and the jth output variable by using the following algorithm to obtain an adjacency calculation result VAR:
Figure FDA0002514163920000025
Figure FDA0002514163920000031
wherein the content of the first and second substances,
Figure FDA0002514163920000032
Figure FDA0002514163920000033
Wiis in the core weight matrixSum of all elements of i-th row, WjIs the sum of all elements in the jth row in the core weight matrix, and E (I) is the adjacency parameter.
6. The method of claim 1, wherein the algorithm configuration master table, the algorithm configuration parameter table, and the algorithm parameter value table are stored in a blockchain, the method further comprising:
and when the error value is larger than the error threshold value, receiving a new algorithm configuration main table, an algorithm configuration parameter table and an algorithm parameter value table, and performing algorithm configuration again.
7. The method for analyzing enterprise operation data based on configuration algorithm as claimed in any one of claims 1 to 6, wherein the performing error calculation on the core correlation value to obtain an error value comprises:
an error value D for the core correlation value is calculated using the following error algorithm:
D＝Hβ+
＝λW+μ
wherein: h represents the core correlation value; wThe method comprises the steps of obtaining a core weight matrix value, representing a preset core error coefficient by lambda, representing a random error item of a core correlation value, representing a random error item of normal distribution by mu, and representing a preset coefficient by β.
8. An enterprise operation data analysis device based on a configuration algorithm, which is characterized by comprising:
the system comprises an algorithm selection module, an algorithm configuration parameter table and an algorithm parameter value table, wherein the algorithm configuration main table comprises an algorithm needing configuration, the algorithm configuration parameter table comprises parameters of the algorithm in the algorithm configuration main table, and the algorithm parameter value table comprises values of all the parameters in the algorithm configuration parameter table;
the variable set calculation module is used for sequentially selecting one algorithm in the algorithm configuration main table, selecting parameters from the algorithm configuration parameter table according to the selected algorithm, selecting parameter values from the algorithm parameter value table, and calculating the selected algorithm according to the selected parameters and the parameter values to obtain an output variable set;
the core correlation value calculation module is used for evaluating and calculating the output variable set to obtain a core correlation value;
the algorithm configuration module is used for carrying out error calculation on the core correlation value to obtain an error value, and when the error value is smaller than or equal to a preset error threshold value, configuring the selected algorithm by using the selected parameter and a parameter value to obtain a configured algorithm;
and the data analysis module is used for acquiring enterprise data, and performing data analysis on the enterprise data by using the configured algorithm to obtain the operation condition of the enterprise.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a method of enterprise operational data analysis based on a configuration algorithm according to any of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the configuration algorithm-based enterprise operational data analysis method according to any one of claims 1 to 7.
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