CN111694844B - 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 PDFInfo
- Publication number
- CN111694844B CN111694844B CN202010471293.7A CN202010471293A CN111694844B CN 111694844 B CN111694844 B CN 111694844B CN 202010471293 A CN202010471293 A CN 202010471293A CN 111694844 B CN111694844 B CN 111694844B
- Authority
- CN
- China
- Prior art keywords
- algorithm
- parameter
- configuration
- value
- output variable
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000004422 calculation algorithm Methods 0.000 title claims abstract description 468
- 238000007405 data analysis Methods 0.000 title claims abstract description 47
- 238000000034 method Methods 0.000 title claims abstract description 41
- 238000004364 calculation method Methods 0.000 claims description 88
- 239000011159 matrix material Substances 0.000 claims description 21
- 238000011156 evaluation Methods 0.000 claims description 16
- 238000004590 computer program Methods 0.000 claims description 5
- 238000005516 engineering process Methods 0.000 abstract description 3
- 238000010586 diagram Methods 0.000 description 13
- 230000006870 function Effects 0.000 description 7
- 238000004458 analytical method Methods 0.000 description 5
- 238000007726 management method Methods 0.000 description 5
- 238000012545 processing Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 239000004973 liquid crystal related substance Substances 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000002093 peripheral effect Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000000802 evaporation-induced self-assembly Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
- G06F16/2282—Tablespace storage structures; Management thereof
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/25—Integrating or interfacing systems involving database management systems
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Databases & Information Systems (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Software Systems (AREA)
- Data Exchanges In Wide-Area Networks (AREA)
Abstract
The invention relates to 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 a selected algorithm by using the parameters and the parameter values when the error value is smaller than or equal to a preset error threshold value; and acquiring enterprise data, and carrying out data analysis on the enterprise data by using the configured algorithm to obtain the running condition of the enterprise. The invention also relates to a blockchain technique, 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
Technical Field
The present invention relates to the field of big data, and in particular, to a method and an apparatus for analyzing enterprise operation data based on a configuration algorithm, and an electronic device.
Background
With the rise of big data, the data analysis by using a large amount of data becomes an increasingly important means, such as the analysis of the operation status of an enterprise by using enterprise data, but in the process of processing data by using big data technology, multiple algorithms are needed, and the used algorithms are complex and various, so how to realize the efficient and low-cost configuration algorithm to analyze the operation status of the enterprise is more and more important.
At present, most of algorithm configuration is carried out manually, and for algorithm configuration service personnel, after the configuration method of an algorithm configuration table is understood, the algorithm configuration service personnel not only need to learn a code language, but also have code writing capability, 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 an enterprise operation data analysis method, device, electronic equipment and computer readable storage medium based on a configuration algorithm, 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, including:
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 an 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 related 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 smaller 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 running condition of the enterprise.
Optionally, one of the algorithms in the algorithm configuration main table is sequentially selected, a parameter is selected from the algorithm configuration parameter table according to the selected algorithm, a parameter value is selected from the algorithm parameter value table, the selected algorithm is calculated according to the selected parameter and the parameter value, and an output variable set is obtained, including:
Selecting algorithm codes according to the 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;
obtaining the parameter values in the algorithm parameter value table according to the algorithm calculation numbers;
inputting the parameter value into the algorithm execution scheme for calculation to obtain an output variable;
Repeating the above operation until all algorithms in the algorithm configuration main table are calculated, and summarizing all output variables to obtain the output variable set.
Optionally, the performing evaluation calculation on the output variable set to obtain a core correlation value includes:
And performing evaluation calculation on the output variable set by using the following evaluation algorithm to obtain a core correlation value Hcorre:
Wherein Y i represents the value of the i-th output variable in the set of output variables; j represents a j-th output variable in the set of output variables; n is the number of output variables in the output variable set, W ij is a binary core weight matrix, the value of which is determined by whether the ith output variable and the jth output variable are adjacent, And representing the average value of all the output variables in the output variable set, wherein S is the standard deviation of all the output variables in the output variable set.
Optionally, the W ij is a binary core weight matrix, and the value of the core weight matrix is determined by whether the ith output variable and the jth output variable are adjacent, including:
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, and if the adjacency calculation result is larger than the preset adjacency threshold, taking a value of W ij as 1;
If the adjacency calculation result is smaller than or equal to the preset adjacency threshold value, the value of W ij is 0.
Optionally, the performing the 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:
And 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:
Wherein, W i is the sum of all elements of the ith row in the core weight matrix, W j is the sum of all elements of the jth column in the core weight matrix, and E (I) is an adjacency parameter.
Optionally, the algorithm configuration master table, the algorithm configuration parameter table, and the algorithm parameter value table are stored in a blockchain, 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 carrying out algorithm configuration again.
Optionally, the performing error calculation on the core correlation value to obtain an error value includes:
calculating an error value D of the core correlation value by using the following error algorithm:
D＝Hβ+ε
ε＝λWε+μ
Wherein: h represents the core correlation value; w ε core weight matrix values; lambda represents a preset core error coefficient; epsilon represents a core correlation value random error term; μ represents a normally distributed random error term; beta represents a preset coefficient.
In order to solve the above problems, the present invention further provides an enterprise operation data analysis device based on a configuration algorithm, the device comprising:
The algorithm selection module is used for 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 an 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 carrying out evaluation calculation on the output variable set to obtain a core correlation value;
The algorithm configuration module is used for carrying out error calculation on the core related 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 utilizing the selected parameter and the parameter value to obtain a configured algorithm;
And the data analysis module is used for acquiring enterprise data, and carrying out data analysis on the enterprise data by utilizing the configured algorithm to obtain the running condition of the enterprise.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
A memory storing at least one instruction; and
And the processor executes the instructions stored in the memory to realize the enterprise operation data analysis method based on the configuration algorithm.
In order to solve the above problems, the present invention further provides a computer-readable storage medium having at least one instruction stored therein, the at least one instruction being executed by a processor in an electronic device to implement the enterprise operation data analysis method based on the configuration algorithm as described in any one of the above.
According to the embodiment of the invention, the algorithm configuration main table, the algorithm configuration parameter table and the algorithm parameter value table are automatically acquired, so that manpower resources are saved; evaluating and calculating the output variable set to obtain a core correlation value, and calculating the core correlation value of an algorithm to ensure that the subsequent evaluation of the algorithm configuration has unified standards; performing error calculation on the core related value to obtain an error value, when the error value is smaller than or equal to a preset error threshold value, configuring the selected algorithm by utilizing the selected parameter and the parameter 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 according to an error calculation result by adopting an error calculation evaluation method; and acquiring enterprise data, carrying out data analysis on the enterprise data by using the configured algorithm to obtain the running condition of the enterprise, and analyzing the enterprise data by using the configured algorithm to accurately analyze the running 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.
Drawings
FIG. 1 is a flow chart 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 diagram illustrating an internal structure of an electronic device 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 table of an enterprise operation data analysis method based on a configuration algorithm according to an embodiment of the present invention;
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The invention provides an enterprise operation data analysis method based on a configuration algorithm. Referring to fig. 1, a flowchart of an algorithm configuration method according to an embodiment of the invention is shown. The method may be performed by an apparatus, which may be implemented in software and/or hardware. For example, in an 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, 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 an 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.
In the embodiment of the present invention, referring to the schematic diagram of the algorithm configuration master table shown in fig. 4, the algorithm configuration master 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 number of each input parameter.
Further, referring to the schematic diagram of the algorithm parameter value table shown in fig. 6, the algorithm parameter value table records the parameter values required for each algorithm calculation.
S2, 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.
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;
obtaining the parameter values in the algorithm parameter value table according to the algorithm calculation numbers;
and carrying the parameter values into the algorithm execution scheme to calculate to obtain output variables, and summarizing all the output variables until the algorithm in the algorithm configuration main table is completely calculated to obtain an algorithm output variable set.
For example: in the algorithm sequence of the algorithm configuration main table, the specific algorithm execution scheme of the algorithm code 'Bel 1' of the first algorithm is A+B;
according to the parameters in the specific algorithm execution scheme, finding out a parameter A and a parameter B which are coded as 'Bel 1' by an algorithm from the algorithm configuration parameter table, and respectively obtaining algorithm calculation numbers A1 and B2 by the parameter sequence numbers 1 and 2 of the related parameter A and the parameter B;
and finding out the parameter values with the parameter variable name BEL_SB in the algorithm parameter value table according to the algorithm calculation numbers A1 and B2 to be 1 and 2 respectively.
And if the parameter value is brought into the algorithm A+B, 1+2 exists, and finally, the result of the output variable BEL_INIT in the algorithm configuration main table is obtained to be 3.
And 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, performing evaluation calculation on the output variable set to obtain a core correlation value.
In the embodiment of the 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 is required to be utilized to evaluate, analyze and calculate the algorithm output variable set so as to obtain the core related value;
In the embodiment of the invention, the following evaluation algorithm is utilized to evaluate, analyze and calculate the output variable set to obtain a core correlation value Hcorre:
Wherein Y i represents the value of the i-th output variable in the set of output variables; j represents a j-th output variable in the set of output variables; n is the number of output variables in the output variable set, W ij is a binary core weight matrix, And representing the average value of all the output variables in the output variable set, wherein S is the standard deviation of all the output variables in the output variable set.
Further, the method for evaluating the core weight matrix W ij 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, and if the adjacency calculation result is larger than the preset adjacency threshold, taking a value of W ij as 1;
If the adjacency calculation result is smaller than or equal to the preset adjacency threshold value, the value of W ij is 0.
Further, the embodiment of the invention performs the adjacency calculation on the ith output variable and the jth output variable by using the following algorithm to obtain an adjacency calculation result VAR:
Wherein, W i is the sum of all elements of the ith row in the core weight matrix, W j is the sum of all elements of the jth column in the core weight matrix, and E (I) is an 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, and if the calculation result VAR is larger than 0, indicating that the output variable i is adjacent to the output variable j, wherein W ij =1;
If the calculated result VAR is less than or equal to 0, it indicates that the output variable i and the output variable j are not adjacent, and W ij =0.
And S4, performing error calculation on the core related 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 smaller than or equal to a preset error threshold value to obtain the configured algorithm.
And if the error value is smaller than or equal to the error threshold value, indicating that the algorithm configuration main table, the algorithm configuration parameter table and the algorithm parameter value table are available, and configuring the selected algorithm according to the algorithm configuration main table, the algorithm configuration parameter table and the algorithm parameter value.
The embodiment of the invention calculates the error value D of the core correlation value by using the following error algorithm:
D＝Hβ+ε
ε＝λW∈+μ
Wherein; d represents the error value; h represents the core correlation value; w ε core weight matrix values; lambda represents a preset core error coefficient; epsilon represents a core correlation value random error term; μ represents a normally distributed random error term; beta represents a preset coefficient and represents the influence degree of H on D.
Preferably, the preset error threshold may be 0.8.
If the error value is smaller than or equal to 0.8, the algorithm configuration main table, the algorithm configuration parameter table and the algorithm parameter value table received by the S1 meet the algorithm configuration requirement, and the algorithm configuration can be completed directly according to the algorithm configuration main table, the algorithm configuration parameter table and the algorithm parameter value table, so that the configured algorithm is obtained.
Further, the embodiment of the invention further comprises the step of storing the algorithm with the configuration completed in a pre-constructed algorithm database.
In the embodiment of the invention, the algorithm database can be an existing cloud database, or can be a local disk, hard disk and the like of a computer.
Further, the embodiment of the invention further comprises:
And if the error value is larger than the error threshold value, indicating that the algorithm configuration main table, the algorithm configuration parameter table and the algorithm parameter value table are unavailable, receiving a new algorithm configuration main table, a new algorithm configuration parameter table and a new algorithm parameter value table, and carrying out algorithm configuration again. It should be emphasized that, to further ensure the privacy and security of the above-described algorithm configuration master table, algorithm configuration parameter table, and algorithm parameter value table, the above-described algorithm configuration master table, algorithm configuration parameter table, and algorithm parameter value table may also be stored in a node of a blockchain.
For example, if the error value is greater than 0.8, the algorithm configuration main table, the algorithm configuration parameter table and the algorithm parameter value table received in S1 do not meet 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 running condition of the enterprise.
Further, the enterprise data may be obtained directly from an existing database, where the enterprise data includes, but is not limited to, cash flow status of the enterprise, credit rating of the enterprise, liability status of the enterprise, and the database may be a mysql database, an Oracle database, etc.
Preferably, after the enterprise data is obtained, the configured algorithm is utilized to analyze the enterprise data to obtain an analysis result, and then the operation condition of the enterprise is judged according to the analysis result.
As shown in fig. 2, a functional block diagram of the enterprise operation data analysis device based on the configuration algorithm according to the present invention is shown.
The enterprise operation data analysis apparatus 100 based on the configuration algorithm of the present invention may be installed in an electronic device. Depending on the functions implemented, the enterprise operating data analysis device based on the configuration algorithm may include 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. The module of the present invention may also be referred to as a unit, meaning a series of computer program segments capable of being executed by the processor of the electronic device and of performing fixed functions, stored in the memory of the electronic device.
In the present embodiment, the functions concerning the respective modules/units are as follows:
The algorithm selection module 101 is configured to obtain an algorithm configuration main table, an algorithm configuration parameter table and an algorithm parameter value table, where the algorithm configuration main table includes an algorithm to be configured, the algorithm configuration parameter table includes parameters of an algorithm in the algorithm configuration main table, and the algorithm parameter value table includes values of parameters in the algorithm configuration parameter table;
The variable set calculation 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 calculation module 103 is configured to perform evaluation calculation on 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 used for acquiring enterprise data, and performing data analysis on the enterprise data by using the configured algorithm to obtain the running 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 comprises an algorithm to be configured, the algorithm configuration parameter table comprises parameters of an 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.
In the embodiment of the present invention, referring to the schematic diagram of the algorithm configuration master table shown in fig. 4, the algorithm configuration master 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 number of each input parameter.
Further, referring to the schematic diagram of the algorithm parameter value table shown in fig. 6, the algorithm parameter value table records the 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 a parameter from the algorithm configuration parameter table according to the selected algorithm, selects a parameter value from the algorithm parameter value table, and calculates the selected algorithm according to the selected parameter and the parameter value to obtain an output variable set.
In detail, the variable set calculation module 102 selects 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; obtaining the parameter values in the algorithm parameter value table according to the algorithm calculation numbers; and carrying the parameter values into the algorithm execution scheme to calculate to obtain output variables, and summarizing all the output variables until the algorithm in the algorithm configuration main table is completely calculated to obtain an algorithm output variable set.
For example: in the algorithm sequence of the algorithm configuration main table, the specific algorithm execution scheme of the algorithm code 'Bel 1' of the first algorithm is A+B;
according to the parameters in the specific algorithm execution scheme, finding out a parameter A and a parameter B which are coded as 'Bel 1' by an algorithm from the algorithm configuration parameter table, and respectively obtaining algorithm calculation numbers A1 and B2 by the parameter sequence numbers 1 and 2 of the related parameter A and the parameter B;
and finding out the parameter values with the parameter variable name BEL_SB in the algorithm parameter value table according to the algorithm calculation numbers A1 and B2 to be 1 and 2 respectively.
And if the parameter value is brought into the algorithm A+B, 1+2 exists, and finally, the result of the output variable BEL_INIT in the algorithm configuration main table is obtained to be 3.
And 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 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 is required to be utilized to evaluate, analyze and calculate the algorithm output variable set so as to obtain the core related value;
In the embodiment of the invention, the following evaluation algorithm is utilized to evaluate, analyze and calculate the output variable set to obtain a core correlation value Hcorre:
Wherein Y i represents the value of the i-th output variable in the set of output variables; j represents a j-th output variable in the set of output variables; n is the number of output variables in the output variable set, W ij is a binary core weight matrix, And representing the average value of all the output variables in the output variable set, wherein S is the standard deviation of all the output variables in the output variable set.
Further, the method for evaluating the core weight matrix W ij 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, and if the adjacency calculation result is larger than the preset adjacency threshold, taking a value of W ij as 1;
If the adjacency calculation result is smaller than or equal to the preset adjacency threshold value, the value of W ij is 0.
Further, the ith output variable and the jth output variable are subjected to adjacency calculation by using the following algorithm, so as to obtain an adjacency calculation result VAR:
Wherein, W i is the sum of all elements of the ith row in the core weight matrix, W j is the sum of all elements of the jth column in the core weight matrix, and E (I) is an 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, and if the calculation result VAR is larger than 0, indicating that the output variable i is adjacent to the output variable j, wherein W ij =1;
If the calculated result VAR is less than or equal to 0, it indicates that the output variable i and the output variable j are not adjacent, and W ij =0.
The algorithm configuration module 104 performs error calculation on the core related 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 smaller 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, indicating 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, an algorithm configuration parameter table and an algorithm parameter value table, and re-performs algorithm configuration;
the embodiment of the invention calculates the error value D of the core correlation value by using the following error algorithm:
D＝Hβ+ε
ε＝λWε+μ
Wherein; d represents the error value; h represents the core correlation value; w ε core weight matrix values; lambda represents a preset core error coefficient; epsilon represents a core correlation value random error term; μ represents a normally distributed random error term; beta represents a preset coefficient and 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 main 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 completed directly according to the algorithm configuration main 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, or can be a local disk, hard disk and the like of a computer.
And the data analysis module 105 is used for acquiring enterprise data, and performing data analysis on the enterprise data by using the configured algorithm to obtain the running condition of the enterprise.
Further, the enterprise data may be obtained directly from an existing database, where the enterprise data includes, but is not limited to, cash flow status of the enterprise, credit rating of the enterprise, liability status of the enterprise, and the database may be a mysql database, an Oracle database, etc.
Preferably, after the enterprise data is obtained, the configured algorithm is utilized to analyze the enterprise data to obtain an analysis result, and then the operation condition of the enterprise is judged according to the analysis result.
Fig. 3 is a schematic structural diagram of an electronic device for implementing the method for analyzing enterprise operation data based on 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, including flash memory, a mobile hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an 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 in other embodiments also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD) or 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 for storing application software installed in the electronic device 1 and various types of data, such as codes of algorithm configuration programs, but also for temporarily storing data that has been output or is to be output.
The processor 10 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects respective components of the entire electronic device using various interfaces and lines, executes various functions of the electronic device 1 and processes data by running or executing programs or modules stored in the memory 11 (for example, executing an enterprise operation data analysis program based on a configuration algorithm, etc.), and calling data stored in the memory 11.
The bus may be a peripheral component interconnect standard (PERIPHERAL COMPONENT INTERCONNECT, PCI) bus, or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.
Fig. 3 shows only an electronic device with components, it being understood by a person 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 shown, or may combine certain components, or may be arranged in different components.
For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to each component, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described herein.
Further, the electronic device 1 may also comprise a network interface, optionally the network interface may comprise a wired interface and/or a wireless interface (e.g. WI-FI interface, bluetooth interface, etc.), typically used for establishing a communication connection between the electronic device 1 and other electronic devices.
The electronic device 1 may optionally further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or 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, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device 1 and for displaying a visual user interface.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
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 an 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 related 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 smaller 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 running condition of the enterprise. It should be emphasized that, to further ensure the privacy and security of the above-described algorithm configuration master table, algorithm configuration parameter table, and algorithm parameter value table, the above-described algorithm configuration master table, algorithm configuration parameter table, and algorithm parameter value table may also be stored in a node of a blockchain.
In particular, the specific implementation method of the above instructions by the processor 10 may refer to the description of the relevant steps in the corresponding embodiment of fig. 2, which is not repeated herein.
Further, the modules/units integrated in the electronic device 1 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The blockchain (Blockchain), essentially a de-centralized database, is a string of data blocks that are generated in association using cryptographic methods, each of which contains information from a batch of network transactions for verifying the validity (anti-counterfeit) of its information and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services 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 characteristics 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 evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (9)
1. An enterprise operation data analysis method based on a configuration algorithm, which is characterized by being applied to electronic equipment and comprising 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 an 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 related 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 smaller than or equal to a preset error threshold value to obtain a configured algorithm;
acquiring enterprise data, and performing data analysis on the enterprise data by using the configured algorithm to obtain the running condition of an enterprise;
Wherein, one algorithm in the algorithm configuration main table is selected in turn, parameters are selected from the algorithm configuration parameter table according to the selected algorithm, parameter values are selected from the algorithm parameter value table, the selected algorithm is calculated according to the selected parameters and the parameter values, and an output variable set is obtained, which comprises: selecting algorithm codes according to the 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; obtaining the parameter values in the algorithm parameter value table according to the algorithm calculation numbers; inputting the parameter value into the algorithm execution scheme for calculation to obtain an output variable; repeating the above operation until all algorithms in the algorithm configuration main table are calculated, and summarizing all output variables to obtain the output variable set.
2. The method for analyzing enterprise operation data based on configuration algorithm of claim 1, wherein the evaluating and calculating the output variable set to obtain a core correlation value comprises:
And performing evaluation calculation on the output variable set by using the following evaluation algorithm to obtain a core correlation value Hcorre:
Hcorre =
Wherein, Representing the value of the ith output variable in said set of output variables,/>Representing the value of the j-th output variable in the set of output variables, n being the number of output variables in the set of output variables,/>Is a binary core weight matrix, the value of which is determined by whether the ith output variable and the jth output variable are adjacent or not,/>And representing the average value of all the output variables in the output variable set, wherein S is the standard deviation of all the output variables in the output variable set.
3. The configuration algorithm-based enterprise operating data analysis method of claim 2, wherein the steps ofIs a binary core weight matrix, and the/>The value is determined by whether the ith output variable and the jth output variable are adjacent, 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, and if the adjacency calculation result is larger than the preset adjacency threshold ；
If the adjacency calculation result is less than or equal to the preset adjacency threshold value, then。
4. The method for analyzing enterprise operation data based on configuration algorithm as claimed in claim 3, wherein the performing the adjacency calculation on the ith output variable and the jth output variable by using a preset adjacency algorithm to obtain adjacency calculation results 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 ：
=/>
E(I)=
Wherein,=Is the sum of all elements of the ith row in the core weight matrix,/>Is the sum of all elements in the j-th column of the core weight matrix,/>Is an adjacency parameter.
5. The configuration algorithm-based enterprise operating data analysis 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 carrying out algorithm configuration again.
6. The method for analyzing enterprise operation data based on configuration algorithm as claimed in any one of claims 1 to 5, wherein the performing error calculation on the core correlation value to obtain an error value includes:
calculating an error value D of the core correlation value by using the following error algorithm:
D=Hβ+ε
ε=λμ
Wherein: h represents the core correlation value; core weight matrix values; lambda represents a preset core error coefficient; epsilon represents a core correlation value random error term; μ represents a normally distributed random error term; beta represents a preset coefficient.
7. A configuration algorithm-based enterprise operation data analysis apparatus for implementing the configuration algorithm-based enterprise operation data analysis method according to any one of claims 1 to 6, characterized in that the apparatus comprises:
The algorithm selection module is used for 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 an 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 carrying out evaluation calculation on the output variable set to obtain a core correlation value;
The algorithm configuration module is used for carrying out error calculation on the core related 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 utilizing the selected parameter and the parameter value to obtain a configured algorithm;
And the data analysis module is used for acquiring enterprise data, and carrying out data analysis on the enterprise data by utilizing the configured algorithm to obtain the running condition of the enterprise.
8. An electronic device, the electronic device comprising:
at least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the configuration algorithm-based enterprise operational data analysis method of any one of claims 1 to 6.
9. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the configuration algorithm based enterprise operation data analysis method according to any one of claims 1 to 6.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010471293.7A CN111694844B (en) | 2020-05-28 | 2020-05-28 | Enterprise operation data analysis method and device based on configuration algorithm and electronic equipment |
| PCT/CN2021/090719 WO2021238563A1 (en) | 2020-05-28 | 2021-04-28 | Enterprise operation data analysis method and apparatus based on configuration algorithm, and electronic device and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010471293.7A CN111694844B (en) | 2020-05-28 | 2020-05-28 | Enterprise operation data analysis method and device based on configuration algorithm and electronic equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111694844A CN111694844A (en) | 2020-09-22 |
| CN111694844B true CN111694844B (en) | 2024-05-07 |
Family
ID=72478749
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010471293.7A Active CN111694844B (en) | 2020-05-28 | 2020-05-28 | Enterprise operation data analysis method and device based on configuration algorithm and electronic equipment |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN111694844B (en) |
| WO (1) | WO2021238563A1 (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111694844B (en) * | 2020-05-28 | 2024-05-07 | 平安科技（深圳）有限公司 | Enterprise operation data analysis method and device based on configuration algorithm and electronic equipment |
| CN113448954B (en) * | 2021-06-29 | 2024-02-06 | 平安证券股份有限公司 | Service data execution method and device, electronic equipment and computer storage medium |
| CN114742476B (en) * | 2022-06-07 | 2022-09-02 | 国网浙江省电力有限公司 | Digital purchasing supply data acquisition method and acquisition platform based on block chain |
| CN114969651B (en) * | 2022-07-28 | 2022-10-25 | 成都乐超人科技有限公司 | Intelligent wind control system construction method and device based on big data AI technology |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103544299A (en) * | 2013-10-30 | 2014-01-29 | 刘峰 | Construction method for commercial intelligent cloud computing system |
| CN106850196A (en) * | 2016-12-05 | 2017-06-13 | 上海交通大学 | The improved continuous variable quantum key delivering method for resisting actual attack |
| CN107844837A (en) * | 2017-10-31 | 2018-03-27 | 第四范式（北京）技术有限公司 | The method and system of algorithm parameter tuning are carried out for machine learning algorithm |
| CN108681487A (en) * | 2018-05-21 | 2018-10-19 | 千寻位置网络有限公司 | The distributed system and tuning method of sensing algorithm arameter optimization |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8752059B2 (en) * | 2007-03-27 | 2014-06-10 | International Business Machines Corporation | Computer data processing capacity planning using dependency relationships from a configuration management database |
| US9305013B2 (en) * | 2008-08-28 | 2016-04-05 | Red Hat, Inc. | URI file system |
| CN109255059A (en) * | 2018-08-31 | 2019-01-22 | 平安科技（深圳）有限公司 | Product basic algorithm determines method, apparatus, computer equipment and storage medium |
| CN110569230A (en) * | 2019-08-30 | 2019-12-13 | 苏州浪潮智能科技有限公司 | Method, system and equipment for interconversion between database design model and design document |
| CN111694844B (en) * | 2020-05-28 | 2024-05-07 | 平安科技（深圳）有限公司 | Enterprise operation data analysis method and device based on configuration algorithm and electronic equipment |
-
2020
- 2020-05-28 CN CN202010471293.7A patent/CN111694844B/en active Active
-
2021
- 2021-04-28 WO PCT/CN2021/090719 patent/WO2021238563A1/en active Application Filing
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103544299A (en) * | 2013-10-30 | 2014-01-29 | 刘峰 | Construction method for commercial intelligent cloud computing system |
| CN106850196A (en) * | 2016-12-05 | 2017-06-13 | 上海交通大学 | The improved continuous variable quantum key delivering method for resisting actual attack |
| CN107844837A (en) * | 2017-10-31 | 2018-03-27 | 第四范式（北京）技术有限公司 | The method and system of algorithm parameter tuning are carried out for machine learning algorithm |
| CN108681487A (en) * | 2018-05-21 | 2018-10-19 | 千寻位置网络有限公司 | The distributed system and tuning method of sensing algorithm arameter optimization |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111694844A (en) | 2020-09-22 |
| WO2021238563A1 (en) | 2021-12-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111694844B (en) | Enterprise operation data analysis method and device based on configuration algorithm and electronic equipment | |
| CN112541745B (en) | User behavior data analysis method and device, electronic equipment and readable storage medium | |
| CN111932562B (en) | Image identification method and device based on CT sequence, electronic equipment and medium | |
| CN111652279B (en) | Behavior evaluation method and device based on time sequence data and readable storage medium | |
| CN113327136B (en) | Attribution analysis method, attribution analysis device, electronic equipment and storage medium | |
| CN112990374B (en) | Image classification method, device, electronic equipment and medium | |
| CN112486957B (en) | Database migration detection method, device, equipment and storage medium | |
| CN114881616A (en) | Business process execution method and device, electronic equipment and storage medium | |
| CN112579621B (en) | Data display method and device, electronic equipment and computer storage medium | |
| CN114491047A (en) | Multi-label text classification method and device, electronic equipment and storage medium | |
| CN111652282B (en) | Big data-based user preference analysis method and device and electronic equipment | |
| CN111831708A (en) | Missing data-based sample analysis method and device, electronic equipment and medium | |
| CN111339072B (en) | User behavior-based change value analysis method and device, electronic equipment and medium | |
| CN112541688B (en) | Service data verification method and device, electronic equipment and computer storage medium | |
| CN116403693A (en) | Method, device, equipment and storage medium for dispatching questionnaire | |
| CN113918296B (en) | Model training task scheduling execution method and device, electronic equipment and storage medium | |
| CN113705686B (en) | Image classification method, device, electronic equipment and readable storage medium | |
| CN112579046B (en) | User story analysis method and device, electronic equipment and storage medium | |
| CN113657546B (en) | Information classification method, device, electronic equipment and readable storage medium | |
| CN113312409B (en) | Task monitoring method and device, electronic equipment and computer readable storage medium | |
| CN114926214B (en) | Model construction-based product pricing method, device, equipment and storage medium | |
| CN115098688B (en) | Multi-label classification model training method and device, electronic equipment and storage medium | |
| CN112328960B (en) | Optimization method and device for data operation, electronic equipment and storage medium | |
| CN115225489B (en) | Dynamic control method for queue service flow threshold, electronic equipment and storage medium | |
| CN117830010A (en) | Order state consistency detection method, device, equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |