A device information management method based on data analysis

Abstract

The present invention discloses a device information management method based on data analysis, which belongs to the technical field of device management, and includes the following steps: S1, obtaining the device operation log of a rice mill; S2, determining the log monitoring index of the operator according to the operation instructions and operation parameters of the operator in the device operation log; S3, determining the log level of the device operation log; S4, generating a sensitive key for the device operation log of the rice mill according to the identification of the log level and the log monitoring index of all operators in the device operation log. The present invention is conducive to protecting the operation information of the rice mill equipment and further improving the security and management efficiency of the device operation log.

Description

Technical Field

[0001] The present invention belongs to the technical field of equipment management, and in particular relates to an equipment information management method based on data analysis. Background Art

[0002] In modern agriculture and grain processing industries, rice mills are key equipment, and their stable operation and efficient management are crucial to ensuring product quality and improving production efficiency. With the rapid development of the Internet of Things and big data technology, intelligent equipment management has become an industry trend. As an important data source for recording the operating status, operator behavior and operating parameters of rice mills, equipment operation logs contain rich information value and are of great significance for analyzing equipment performance, predicting faults, and optimizing operating procedures.

[0003] However, traditional equipment operation log processing methods are often limited to simple data recording and storage, lacking in-depth protection of log data. This makes it easy for operation logs to be leaked during equipment operation, and the operator's behavior cannot be effectively kept confidential. As the amount of equipment operation log data continues to increase, how to efficiently and securely manage this data and prevent sensitive information from being leaked has become an urgent problem to be solved. Summary of the invention

[0004] In order to solve the above problems, the present invention proposes a device information management method based on data analysis.

[0005] The technical solution of the present invention is: a device information management method based on data analysis comprises the following steps:

[0006] S1. Obtaining the equipment operation log of the rice mill;

[0007] S2. Determine the operator's log monitoring index based on the operator's operation instructions and operation parameters in the equipment operation log;

[0008] S3. Determine the log level of the device operation log;

[0009] S4. Generate a sensitive key for the device operation log of the rice mill according to the log level identifier and the log monitoring index of all operators in the device operation log.

[0010] Furthermore, S2 includes the following sub-steps:

[0011] S21, obtaining the operation instructions recorded in the device operation log and the operation parameters corresponding to the operation instructions;

[0012] S22, constructing an entity relationship between the operator and the operation instruction;

[0013] S23, constructing an attribute relationship between the operator and the operation parameter;

[0014] S24. Determine the log monitoring index of each operator in the equipment operation log according to a plurality of entity relationships and attribute relationships.

[0015] The beneficial effect of the above further scheme is that in the present invention, by comprehensively considering the entity relationship and the attribute relationship, the operation behavior of the operator can be comprehensively evaluated, and a comprehensive log monitoring index can be obtained, which helps to more intuitively reflect the operation of the operator. The essence of establishing the entity relationship between the operator and the operation instruction is to analyze the operation habits of the operator; by constructing the attribute relationship, the essence is to judge whether the parameter settings of the operator when performing the operation are reasonable.

[0016] Furthermore, in S22, the entity relationship R between the operator and the operation instruction entity The expression is: ; In the formula, λ oper The feature vector value of the operation text corresponding to the operation instruction, γ per Indicates the weight between the operation instruction and the operator.

[0017] The operation types may include whitening operation, adjusting the gap between rice knives and the position of the pressure weight, and starting the rice machine, etc. The word embedding model can be used to determine the value of the operation text corresponding to the operation type, and the value of the feature vector can be a continuous floating point number learned through a neural network. Determine whether the operator has the authority to execute the operation instruction. If yes, γ per The value of is larger, otherwise γ per The value of is smaller.

[0018] Furthermore, in S23, the attribute relationship R between the operator and the operation parameter nature The expression is: Where, N actual Indicates the operating parameters, N max Indicates the maximum operating parameters of the operator, N min represents the minimum operating parameter of the operator, and ε represents the compromise factor.

[0019] For example, when adjusting the gap in the milling chamber, the operating range of the rice cutter gap allowed by the operator is 2-2.5mm, that is, N max is 2.5, N min is 2; the actual operating parameter is 2.2, that is, N actual is 2.2. ε∈[0,1].

[0020] If the operation instruction is an instruction such as starting a rice machine and there is no operation parameter, the element value of the attribute relationship is 1.

[0021] Further, S24 includes the following sub-steps:

[0022] S241, determining the management matrix of the operator in each operation instruction according to the entity relationship between the operator and the operation instruction and the attribute relationship between the operator and the operation parameter;

[0023] S242: Determine the log monitoring index of the operator according to the management matrix of each operation instruction of the operator and the user number of the operator.

[0024] The beneficial effect of the above further scheme is that in the present invention, by organizing the relationship between operators, operation instructions and operation parameters into a management matrix, the overall analysis and management of operation behavior is realized. The management matrix not only contains the corresponding relationship between operators and operation instructions, but also incorporates the information of operation parameters, thereby providing a multi-dimensional analysis perspective. Each operator has his own management matrix, highlighting their performance and characteristics under different operation instructions. The log monitoring index is calculated based on the management matrix and the operator user number, which quantifies the overall performance of the operator in the equipment operation log.

[0025] Furthermore, in S241, the operator's management matrix X of the operation instruction is expressed as: ; In the formula, R entity Represents the entity relationship between operators and operation instructions, R nature Represents the attribute relationship between operators and operation parameters.

[0026] Further, in S242, the expression of the operator's log monitoring index is: ; In the formula, represents the maximum eigenvalue of the management matrix of the operator at the kth operation instruction, ε represents the tradeoff factor, K represents the total number of operation instructions corresponding to the operator, USER represents the user ID of the operator, and hash(·) represents the hash operation.

[0027] Further, S4 includes the following sub-steps:

[0028] S41, obtaining an identifier corresponding to the log level of the device operation log;

[0029] S42, performing ASCII encoding on the identifier to obtain the code value of each letter in the identifier;

[0030] S43: Generate a sensitive key for the device operation log of the rice mill according to the code value of each letter in the identifier and the log monitoring index of all operators in the device operation log.

[0031] The beneficial effect of the above further scheme is: in the present invention, logs are divided into different levels according to the importance and urgency of the operation, such as INFO (information), WARN (warning), ERROR (error), etc. The INFO level log is mainly used to record the normal operation information of the rice milling machine to help the operation and maintenance personnel understand the current operation status of the system. The WARN level log is used to record abnormal processes or potential problems triggered by the rice milling machine during business processing. The ERROR level log is used to record error events that occur in the rice milling machine, which may affect the continued operation of the system. Sometimes it also includes more fine-grained levels such as DEBUG (debugging) and FATAL (fatal error). The English letters are the identifiers of the log levels.

[0032] Since the sensitive key is generated based on the code value of the log level identifier and the operator's log monitoring index, each log will have a unique key.

[0033] Furthermore, in S43, the expression of the sensitive key is: ,in, represents the mean code value of all letters in the logo, Indicates the average value of the log monitoring index of all operators in the device operation log.

[0034] The beneficial effects of the present invention are as follows: the equipment operation log of the present invention records key information such as the operating status of the rice mill, the operator's instructions and parameters, and the log monitoring index obtained by analyzing the operator's operating instructions and parameters can be used as one of the bases for sensitive keys; by dividing the logs into levels and generating sensitive keys according to the importance and urgency of different logs, the security of the equipment operation log can be ensured, which is beneficial to protecting the operating information of the rice mill equipment and further improving the security and management efficiency of the equipment operation log. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 The present invention is a flow chart of a device information management method based on data analysis. DETAILED DESCRIPTION

[0036] The embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0037] like Figure 1 As shown, the present invention provides a device information management method based on data analysis, comprising the following steps:

[0038] S1. Obtaining the equipment operation log of the rice mill;

[0039] S2. Determine the operator's log monitoring index based on the operator's operation instructions and operation parameters in the equipment operation log;

[0040] S3. Determine the log level of the device operation log;

[0041] S4. Generate a sensitive key for the device operation log of the rice mill according to the log level identifier and the log monitoring index of all operators in the device operation log.

[0042] In this embodiment of the present invention, S2 includes the following sub-steps:

[0043] S21, obtaining the operation instructions recorded in the device operation log and the operation parameters corresponding to the operation instructions;

[0044] S22, constructing an entity relationship between the operator and the operation instruction;

[0045] S23, constructing an attribute relationship between the operator and the operation parameter;

[0046] S24. Determine the log monitoring index of each operator in the equipment operation log according to a plurality of entity relationships and attribute relationships.

[0047] In the present invention, by comprehensively considering the entity relationship and attribute relationship, the operation behavior of the operator can be comprehensively evaluated, and a comprehensive log monitoring index can be obtained, which helps to more intuitively reflect the operation of the operator. The essence of establishing the entity relationship between the operator and the operation instruction is to analyze the operation habits of the operator; by constructing the attribute relationship, the essence is to judge whether the parameter settings of the operator when performing the operation are reasonable.

[0048] In the embodiment of the present invention, in S22, the entity relationship R between the operator and the operation instruction entity The expression is: ; In the formula, λ oper The feature vector value of the operation text corresponding to the operation instruction, γ per Indicates the weight between the operation instruction and the operator.

[0049] The operation types may include whitening operation, adjusting the gap between rice knives and the position of the pressure weight, and starting the rice machine, etc. The word embedding model can be used to determine the value of the operation text corresponding to the operation type, and the value of the feature vector can be a continuous floating point number learned through a neural network. Determine whether the operator has the authority to execute the operation instruction. If yes, γ per The value of is larger, otherwise γ per The value of is smaller.

[0050] In the embodiment of the present invention, in S23, the attribute relationship R between the operator and the operation parameter nature The expression is: Where, N actual Indicates the operating parameters, N maxIndicates the maximum operating parameters of the operator, N min represents the minimum operating parameter of the operator, and ε represents the compromise factor.

[0051] For example, when adjusting the gap in the milling chamber, the operating range of the rice cutter gap allowed by the operator is 2-2.5mm, that is, N max is 2.5, N min is 2; the actual operating parameter is 2.2, that is, N actual is 2.2. ε∈[0,1].

[0052] If the operation instruction is an instruction such as starting a rice machine and there is no operation parameter, the element value of the attribute relationship is 1.

[0053] In this embodiment of the present invention, S24 includes the following sub-steps:

[0054] S241, determining the management matrix of the operator in each operation instruction according to the entity relationship between the operator and the operation instruction and the attribute relationship between the operator and the operation parameter;

[0055] S242: Determine the log monitoring index of the operator according to the management matrix of each operation instruction of the operator and the user number of the operator.

[0056] In the present invention, by organizing the relationship between operators, operation instructions and operation parameters into a management matrix, the overall analysis and management of operation behavior is realized. The management matrix not only contains the corresponding relationship between operators and operation instructions, but also incorporates the information of operation parameters, thereby providing a multi-dimensional analysis perspective. Each operator has his own management matrix, highlighting their performance and characteristics under different operation instructions. The log monitoring index is calculated based on the management matrix and the operator user number, which quantifies the overall performance of the operator in the equipment operation log.

[0057] In the embodiment of the present invention, in S241, the operator's expression of the management matrix X of the operation instruction is: ; In the formula, R entity Represents the entity relationship between operators and operation instructions, R nature Represents the attribute relationship between operators and operation parameters.

[0058] In the embodiment of the present invention, in S242, the expression of the operator's log monitoring index is: ; In the formula, represents the maximum eigenvalue of the management matrix of the operator at the kth operation instruction, ε represents the tradeoff factor, K represents the total number of operation instructions corresponding to the operator, USER represents the user ID of the operator, and hash(·) represents the hash operation.

[0059] In this embodiment of the present invention, S4 includes the following sub-steps:

[0060] S41, obtaining an identifier corresponding to the log level of the device operation log;

[0061] S42, performing ASCII encoding on the identifier to obtain the code value of each letter in the identifier;

[0062] S43: Generate a sensitive key for the device operation log of the rice mill according to the code value of each letter in the identifier and the log monitoring index of all operators in the device operation log.

[0063] In the present invention, logs are divided into different levels according to the importance and urgency of the operation, such as INFO (information), WARN (warning), ERROR (error), etc. INFO level logs are mainly used to record the normal operation information of the rice milling machine to help operation and maintenance personnel understand the current operation status of the system. WARN level logs are used to record abnormal processes or potential problems triggered by the rice milling machine during business processing. ERROR level logs are used to record error events that occur in the rice milling machine, which may affect the continued operation of the system. Sometimes more fine-grained levels such as DEBUG (debugging) and FATAL (fatal error) are also included. The English letters are the identifiers of the log levels.

[0064] Since the sensitive key is generated based on the code value of the log level identifier and the operator's log monitoring index, each log will have a unique key.

[0065] In the embodiment of the present invention, in S43, the expression of the sensitive key is: ,in, represents the mean code value of all letters in the logo, Indicates the average value of the log monitoring index of all operators in the device operation log.

[0066] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A device information management method based on data analysis, characterized in that: The following steps are involved: S1. Obtaining the equipment operation log of the rice mill; S2. Determine the operator's log monitoring index based on the operator's operation instructions and operation parameters in the equipment operation log; S3. Determine the log level of the device operation log; S4. Generate a sensitive key for the device operation log of the rice mill according to the log level identifier and the log monitoring index of all operators in the device operation log; The S2 comprises the following sub-steps: S21, obtaining the operation instructions recorded in the device operation log and the operation parameters corresponding to the operation instructions; S22, constructing an entity relationship between the operator and the operation instruction; S23, constructing an attribute relationship between the operator and the operation parameter; S24, determining the log monitoring index of each operator in the device operation log according to a plurality of entity relationships and attribute relationships; In S22, the entity relationship R between the operator and the operation instruction entity The expression is: ; In the formula, λ oper The feature vector value of the operation text corresponding to the operation instruction, γ per Indicates the weight between the operation instruction and the operator; In S23, the attribute relationship R between the operator and the operation parameter nature The expression is: Where N actual Indicates the operating parameters, N max Indicates the maximum operating parameters of the operator, N min represents the minimum operating parameter of the operator, and ε represents the compromise factor; The S24 comprises the following sub-steps: S241, determining the management matrix of the operator in each operation instruction according to the entity relationship between the operator and the operation instruction and the attribute relationship between the operator and the operation parameter; S242, determining the log monitoring index of the operator according to the management matrix of each operation instruction of the operator and the user number of the operator; In S241, the operator's management matrix X of the operation instruction is expressed as: Where R entity Represents the entity relationship between the operator and the operation instruction, R nature Represents the attribute relationship between operators and operation parameters; In S242, the expression of the operator's log monitoring index is: ; In the formula, represents the maximum eigenvalue of the management matrix of the operator at the kth operation instruction, ε represents the tradeoff factor, K represents the total number of operation instructions corresponding to the operator, USER represents the user ID of the operator, and hash(·) represents the hash operation.

2. The device information management method based on data analysis according to claim 1, characterized in that: The S4 comprises the following sub-steps: S41, obtaining an identifier corresponding to the log level of the device operation log; S42, performing ASCII encoding on the identifier to obtain the code value of each letter in the identifier; S43: Generate a sensitive key for the device operation log of the rice mill according to the code value of each letter in the identifier and the log monitoring index of all operators in the device operation log.

3. The device information management method based on data analysis according to claim 2, characterized in that: In S43, the expression of the sensitive key is: ,in, represents the mean code value of all letters in the logo, Indicates the average value of the log monitoring index of all operators in the device operation log.

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