Equipment efficiency feedback evaluation method and device

Abstract

The invention discloses an equipment efficiency feedback evaluation method and device. The method comprises the following steps: acquiring an equipment physical index historical sequence set and an equipment evaluation historical index set of a completed equipment system evaluation process; performing modeling processing on the equipment physical index historical sequence set and the equipment evaluation historical index set to obtain an evaluation index prediction model; acquiring an equipment physical index real-time sequence set at the current moment; based on the equipment physical index real-time sequence set and the equipment physical index historical sequence set, performing feedback correction processing on the evaluation index prediction model to obtain an evaluation index feedback prediction model; and performing equipment system efficiency evaluation processing on the equipment physical index real-time sequence set based on an evaluation index feedback prediction model to obtain a real-time evaluation value of the equipment system efficiency.

Description

Technical Field

[0001] The present invention relates to the fields of industrial data processing and equipment system effectiveness evaluation, and in particular to an equipment effectiveness feedback evaluation method and device. Background Art

[0002] In the field of modern equipment management and evaluation, accurate assessment of equipment effectiveness is crucial for optimizing equipment configuration, improving operational efficiency, and reducing maintenance costs. Traditional equipment effectiveness assessment methods primarily rely on static indicator analysis or single-point performance testing. While these methods can reflect the performance status of equipment to a certain extent, they have significant limitations. Existing methods struggle to comprehensively consider the complex relationship between multi-dimensional physical indicators and overall effectiveness when dealing with complex equipment systems, and are unable to effectively address the dynamic changes and uncertainties of equipment systems.

[0003] During the test and evaluation of equipment effectiveness, it is necessary to collect multiple types of physical indicators and evaluation indicators, and then evaluate the effectiveness of the equipment system based on these evaluation indicators. Evaluation indicators can be obtained through simulation or data processing of physical indicators. Current methods for obtaining evaluation indicators based on physical indicators are subjective and cannot be adjusted according to changes in the statistical characteristics of the physical indicators.

[0004] How to establish a dynamically adjusted quantitative analysis model based on physical indicators and obtain evaluation indicators is an urgent problem that needs to be solved. Summary of the Invention

[0005] The present invention mainly solves the problem of how to establish a dynamically adjusted quantitative analysis model based on physical indicators and obtain evaluation indicators. The present invention discloses an equipment performance feedback evaluation method and device.

[0006] In a first aspect, an embodiment of the present invention discloses an equipment effectiveness feedback evaluation method, comprising:

[0007] S1, obtaining a set of equipment physical index history sequences and a set of equipment evaluation history indexes of a completed equipment system evaluation process;

[0008] S2, modeling the equipment physical indicator historical sequence set and the equipment evaluation historical indicator set to obtain an evaluation indicator prediction model;

[0009] S3, obtain the real-time series set of equipment physical indicators at the current moment;

[0010] S4, performing feedback correction processing on the evaluation indicator prediction model based on the real-time series set of equipment physical indicators and the historical series set of equipment physical indicators to obtain an evaluation indicator feedback prediction model;

[0011] S5, based on the evaluation index feedback prediction model, performing equipment system effectiveness evaluation processing on the real-time series set of equipment physical indicators to obtain a real-time evaluation value of the equipment system effectiveness.

[0012] The modeling process of the equipment physical indicator historical sequence set and the equipment evaluation historical indicator set to obtain an evaluation indicator prediction model includes:

[0013] Representing the equipment physical indicator history sequence set and the equipment evaluation history indicator set as a physical indicator matrix and an evaluation indicator sequence, respectively;

[0014] Construct a prediction model for the evaluation index to be solved;

[0015] Solve the evaluation index prediction model to be solved to obtain the evaluation index prediction model.

[0016] The evaluation index prediction model to be solved includes:

[0017] Hp=z,

[0018] Among them, H is the physical indicator matrix, z is the evaluation indicator sequence, p is the coefficient vector of the evaluation indicator prediction model, the dimension of H is m×n, and the dimension of z is m×1.

[0019] The step of solving the evaluation index prediction model to be solved to obtain the evaluation index prediction model includes:

[0020] The fusion solution matrix W is constructed using H and z. The expression of the fusion solution matrix W is:

[0021] W=[-z,H],

[0022] Among them, the dimension of the fusion solution matrix W is m×(n+1);

[0023] Perform singular value decomposition on the fusion solution matrix W to obtain:

[0024] W=UCY H ,

[0025] Among them, U is the left decomposition matrix, C is the intermediate matrix, and Y is the right decomposition matrix;

[0026] Decompose the matrix Y to obtain a vector p; the expression of the decomposition process is:

[0027] p(i)=Y(i+1,n+1),

[0028] Where p(i) represents the i-th element of vector p, and Y(i+1,n+1) represents the element in the i+1-th row and n+1-th column of matrix Y;

[0029] Substitute the vector p into the evaluation index prediction model to be solved to obtain the evaluation index prediction model.

[0030] The step of performing feedback correction processing on the evaluation indicator prediction model based on the real-time series set of equipment physical indicators and the historical series set of equipment physical indicators to obtain the evaluation indicator feedback prediction model includes:

[0031] Performing difference calculation on the real-time series set of equipment physical indicators and the historical series set of equipment physical indicators to obtain a difference vector;

[0032] The difference vector is used to perform feedback correction processing on the evaluation index prediction model to obtain an evaluation index feedback prediction model.

[0033] The method of performing feedback correction processing on the evaluation index prediction model by utilizing the difference vector to obtain the evaluation index feedback prediction model includes:

[0034] Calculate the median value a1 and mode value a2 of the difference vector;

[0035] Calculate the variance value α and the range value β of the coefficient vector of the evaluation index prediction model;

[0036] The difference vector and the coefficient vector are updated and calculated to obtain an updated coefficient vector, and the updated coefficient vector is substituted into the evaluation index prediction model to obtain an evaluation index feedback prediction model.

[0037] The expression for the update calculation process is:

[0038]

[0039] Among them, p i is the i-th element of the coefficient vector, pe i is the i-th element of the updated coefficient vector, A i is the i-th element of the difference vector.

[0040] According to a second aspect of the present invention, a device for evaluating equipment performance feedback is disclosed, comprising:

[0041] a memory storing executable program code;

[0042] a processor coupled to the memory;

[0043] The processor calls the executable program code stored in the memory to execute the equipment effectiveness feedback evaluation method.

[0044] According to a third aspect of the present invention, a computer-storable medium is disclosed. The computer-storable medium stores computer instructions. When the computer instructions are called by a computer, the computer instructions are used to execute the equipment effectiveness feedback evaluation method.

[0045] According to a fourth aspect of the present invention, an information data processing terminal is disclosed. The information data processing terminal is used to implement the equipment effectiveness feedback evaluation method.

[0046] The beneficial effects of the present invention are:

[0047] This invention provides an equipment effectiveness feedback evaluation method. By introducing a historical series of equipment physical indicators and a historical set of equipment evaluation indicators for modeling, a prediction model for evaluation indicators is constructed. Furthermore, feedback correction is performed based on real-time data, thereby achieving real-time, dynamic evaluation of equipment effectiveness. Compared with existing technologies, this invention has the following significant benefits:

[0048] Enhanced dynamic evaluation capabilities: By acquiring historical and real-time series sets of equipment physical indicators, the present invention can comprehensively reflect the dynamic characteristics of equipment at different operating stages, thereby realizing real-time and dynamic evaluation of equipment effectiveness, and solving the evaluation lag problem caused by traditional methods that only rely on static indicators or single time point data.

[0049] Significantly improved evaluation accuracy: The present invention uses historical data to construct an evaluation index prediction model and solves the model through mathematical tools such as singular value decomposition. It can fully tap the potential information in historical data and establish an accurate mapping relationship between equipment physical indicators and effectiveness, thereby significantly improving the accuracy of equipment effectiveness evaluation.

[0050] Feedback correction mechanism optimization: This invention introduces a feedback correction mechanism to dynamically correct the evaluation index prediction model by calculating the difference vector between real-time data and historical data. It can effectively cope with the dynamic changes and uncertainties of the equipment system, ensure the accuracy and reliability of the evaluation results, and further improve the adaptability of equipment effectiveness evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 4 is an implementation flow chart of the method of the present invention. DETAILED DESCRIPTION

[0052] In order to better understand the content of the present invention, an embodiment is given here.

[0053] Figure 1 4 is an implementation flow chart of the method of the present invention.

[0054] In a first aspect, an embodiment of the present invention discloses an equipment effectiveness feedback evaluation method, comprising:

[0055] S1, obtaining a set of equipment physical indicator history sequences and a set of equipment evaluation history sequences of a completed equipment system evaluation process; the set of equipment physical indicator history sequences includes several equipment physical indicator history sequences, and the set of equipment evaluation history sequence includes several equipment evaluation history indicators;

[0056] S2, modeling the equipment physical indicator historical sequence set and the equipment evaluation historical indicator set to obtain an evaluation indicator prediction model;

[0057] S3, obtain the real-time series set of equipment physical indicators at the current moment;

[0058] S4, performing feedback correction processing on the evaluation indicator prediction model based on the real-time series set of equipment physical indicators and the historical series set of equipment physical indicators to obtain an evaluation indicator feedback prediction model;

[0059] S5, performing equipment system effectiveness evaluation processing on the real-time series set of equipment physical indicators based on the evaluation indicator feedback prediction model to obtain a real-time evaluation value of the equipment system effectiveness;

[0060] The present invention combines the equipment physical indicator matrix with the evaluation indicator sequence to construct a comprehensive evaluation model, which can comprehensively consider the impact of multi-dimensional physical indicators on equipment effectiveness. It solves the problem of one-sided evaluation caused by the inability of existing methods to effectively process multi-dimensional data when facing complex equipment systems.

[0061] The modeling process of the equipment physical indicator historical sequence set and the equipment evaluation historical indicator set to obtain an evaluation indicator prediction model includes:

[0062] Representing the equipment physical indicator history sequence set and the equipment evaluation history indicator set as a physical indicator matrix and an evaluation indicator sequence, respectively;

[0063] Construct a prediction model for the evaluation index to be solved;

[0064] Solve the evaluation index prediction model to be solved to obtain the evaluation index prediction model.

[0065] The equipment physical indicator history sequence set and the equipment evaluation history indicator set are expressed as a physical indicator matrix and an evaluation indicator sequence, respectively, including:

[0066] Taking each equipment physical indicator history sequence in the equipment physical indicator history sequence set as a row vector, a physical indicator matrix is constructed;

[0067] Expressing the equipment evaluation historical indicator set as an evaluation indicator sequence;

[0068] The evaluation index prediction model to be solved includes:

[0069] Hp=z,

[0070] Among them, H is the physical indicator matrix, z is the evaluation indicator sequence, and p is the coefficient vector of the evaluation indicator prediction model; the dimension of H is m×n, and the dimension of z is m×1;

[0071] The step of solving the evaluation index prediction model to be solved to obtain the evaluation index prediction model includes:

[0072] The fusion solution matrix W is constructed using H and z. The expression of the fusion solution matrix W is:

[0073] W=[-z,H],

[0074] Among them, the dimension of the fusion solution matrix W is m×(n+1);

[0075] Perform singular value decomposition on the fusion solution matrix W to obtain:

[0076] W=UCY H ,

[0077] Among them, U is the left decomposition matrix, C is the intermediate matrix, and Y is the right decomposition matrix;

[0078] Decompose the matrix Y to obtain a vector p; the expression of the decomposition process is:

[0079] p(i)=Y(i+1,n+1),

[0080] Where p(i) represents the i-th element of vector p, and Y(i+1,n+1) represents the element in the i+1-th row and n+1-th column of matrix Y;

[0081] Substitute the vector p into the evaluation index prediction model to be solved to obtain the evaluation index prediction model.

[0082] The evaluation indicator prediction model to be solved establishes a clear mapping between the equipment's physical indicators and the evaluation indicators by constructing the mathematical relationship Hp = z. During the solution process, the fusion solution matrix W is processed using singular value decomposition. This method effectively extracts key information from the matrix, reduces data dimensionality, removes noise interference, and makes the coefficient vector p obtained by the solution more accurate. After substituting the accurate p into the model, the resulting evaluation indicator prediction model can more accurately reflect the inherent relationship between the equipment's physical indicators and the evaluation indicators, effectively improving the accuracy of the model's prediction of equipment system effectiveness and reducing evaluation errors.

[0083] The above model and solution method construct and modify the evaluation indicator prediction model through matrix operations and a specific solution algorithm, capable of rapidly processing large amounts of equipment physical indicator data. After obtaining a real-time series of current equipment physical indicators, the equipment system's effectiveness can be rapidly evaluated and processed based on the constructed and modified evaluation indicator feedback prediction model, resulting in a real-time evaluation value. This efficient data processing and evaluation mechanism significantly shortens the time required for equipment system effectiveness evaluation, meeting the requirements of modern equipment rapid iteration and real-time monitoring. It also helps equipment management departments make timely decisions and improve equipment operation, maintenance, and management efficiency.

[0084] The step of performing feedback correction processing on the evaluation indicator prediction model based on the real-time series set of equipment physical indicators and the historical series set of equipment physical indicators to obtain the evaluation indicator feedback prediction model includes:

[0085] Performing difference calculation on the real-time series set of equipment physical indicators and the historical series set of equipment physical indicators to obtain a difference vector;

[0086] The difference vector is used to perform feedback correction processing on the evaluation index prediction model to obtain an evaluation index feedback prediction model.

[0087] The difference calculation of the real-time series set of equipment physical indicators and the historical series set of equipment physical indicators to obtain a difference vector includes:

[0088]

[0089] Among them, qe i represents the i-th element of the difference vector, q ij represents the jth element of the ith equipment physical indicator history sequence in the equipment physical indicator history sequence set, e ij represents the jth element of the i-th equipment physical indicator real-time sequence in the equipment physical indicator real-time sequence set, and m is the total number of elements in the equipment physical indicator historical sequence.

[0090] The method of performing feedback correction processing on the evaluation index prediction model by utilizing the difference vector to obtain the evaluation index feedback prediction model includes:

[0091] Calculate the median value a1 and mode value a2 of the difference vector;

[0092] Calculate the variance value α and the range value β of the coefficient vector of the evaluation index prediction model;

[0093] Performing update calculation processing on the difference vector and the coefficient vector to obtain an updated coefficient vector, substituting the updated coefficient vector into the evaluation index prediction model to obtain an evaluation index feedback prediction model;

[0094] The expression for the update calculation process is:

[0095]

[0096] Among them, p i is the i-th element of the coefficient vector, pe i is the i-th element of the updated coefficient vector, A i is the i-th element of the difference vector.

[0097] After developing the evaluation indicator prediction model, feedback corrections are performed on the model based on the current real-time and historical series of the equipment's physical indicators. This feedback correction mechanism enables the model to dynamically adjust its parameters based on the equipment's real-time status and historical trends, promptly adapting to performance changes in different scenarios and operating conditions. Compared to traditional fixed-parameter evaluation models, this evaluation indicator feedback prediction model offers greater environmental adaptability and dynamic response capabilities, ensuring consistently high evaluation accuracy in complex and ever-changing real-world applications.

[0098] The physical indicators of the equipment include detection capability indicators, identification capability indicators, communication capability indicators, mobility capability indicators, etc.;

[0099] The equipment evaluation indicators include reliability indicators, connectivity indicators, support indicators, and mission completion indicators;

[0100] The equipment physical indicator historical sequence set and the equipment evaluation historical indicator set are both historical data of the equipment system evaluation process.

[0101] The equipment system effectiveness evaluation process is performed on the real-time series set of equipment physical indicators based on the evaluation indicator feedback prediction model to obtain a real-time evaluation value of the equipment system effectiveness, including:

[0102] Based on the evaluation index feedback prediction model, the real-time series set of equipment physical indicators is calculated and processed to obtain an equipment evaluation prediction index set;

[0103] Obtain the standard value of each equipment evaluation indicator;

[0104] The equipment evaluation prediction index set and the standard value of the equipment evaluation index are evaluated and calculated to obtain a real-time evaluation value of the equipment system effectiveness.

[0105] The expression for the evaluation calculation process is:

[0106]

[0107] Among them, T i() represents the i-th order polynomial of the first kind Chebyshev polynomial, θ i is the i-th equipment evaluation prediction index value of the equipment evaluation prediction index set, σ i is the i-th standard value of the equipment evaluation index, txp is the real-time evaluation value of the equipment system effectiveness, and L is the number of evaluation indicators.

[0108] According to a second aspect of the present invention, a device for evaluating equipment performance feedback is disclosed, comprising:

[0109] a memory storing executable program code;

[0110] a processor coupled to the memory;

[0111] The processor calls the executable program code stored in the memory to execute the equipment effectiveness feedback evaluation method.

[0112] According to a third aspect of the present invention, a computer-storable medium is disclosed. The computer-storable medium stores computer instructions. When the computer instructions are called by a computer, the computer instructions are used to execute the equipment effectiveness feedback evaluation method.

[0113] According to a fourth aspect of the present invention, an information data processing terminal is disclosed. The information data processing terminal is used to implement the equipment effectiveness feedback evaluation method.

[0114] The foregoing is merely an embodiment of the present invention and is not intended to limit the present invention. It will be apparent to those skilled in the art that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are intended to be included within the scope of the claims of the present invention.

Claims

1. A method for evaluating equipment effectiveness feedback, characterized in that: include: S1, obtaining a set of equipment physical index history sequences and a set of equipment evaluation history indexes of a completed equipment system evaluation process; S2, modeling the equipment physical indicator historical sequence set and the equipment evaluation historical indicator set to obtain an evaluation indicator prediction model; S3, obtain the real-time series set of equipment physical indicators at the current moment; S4, performing feedback correction processing on the evaluation indicator prediction model based on the real-time series set of equipment physical indicators and the historical series set of equipment physical indicators to obtain an evaluation indicator feedback prediction model; S5, based on the evaluation index feedback prediction model, performing equipment system effectiveness evaluation processing on the real-time series set of equipment physical indicators to obtain a real-time evaluation value of the equipment system effectiveness.

2. The equipment effectiveness feedback evaluation method according to claim 1, characterized in that: The modeling process of the equipment physical indicator historical sequence set and the equipment evaluation historical indicator set to obtain an evaluation indicator prediction model includes: Representing the equipment physical indicator history sequence set and the equipment evaluation history indicator set as a physical indicator matrix and an evaluation indicator sequence, respectively; Construct a prediction model for the evaluation index to be solved; Solve the evaluation index prediction model to be solved to obtain the evaluation index prediction model.

3. The equipment effectiveness feedback evaluation method according to claim 2, characterized in that: The evaluation index prediction model to be solved includes: Hp=z, Among them, H is the physical indicator matrix, z is the evaluation indicator sequence, p is the coefficient vector of the evaluation indicator prediction model, the dimension of H is m×n, and the dimension of z is m×1.

4. The equipment effectiveness feedback evaluation method according to claim 3, characterized in that: The step of solving the evaluation index prediction model to be solved to obtain the evaluation index prediction model includes: The fusion solution matrix W is constructed using H and z. The expression of the fusion solution matrix W is: W=[-z,H], Among them, the dimension of the fusion solution matrix W is m×(n+1); Perform singular value decomposition on the fusion solution matrix W to obtain: W=TUCY H , Among them, U is the left decomposition matrix, C is the intermediate matrix, and Y is the right decomposition matrix; Decompose the matrix Y to obtain a vector p; the expression of the decomposition process is: p(i)=Y(i+1,n+1), Where p(i) represents the i-th element of vector p, and Y(i+1,n+1) represents the element in the i+1-th row and n+1-th column of matrix Y; Substitute the vector p into the evaluation index prediction model to be solved to obtain the evaluation index prediction model.

5. The equipment effectiveness feedback evaluation method according to claim 3, characterized in that: The step of performing feedback correction processing on the evaluation indicator prediction model based on the real-time series set of equipment physical indicators and the historical series set of equipment physical indicators to obtain the evaluation indicator feedback prediction model includes: Performing difference calculation on the real-time series set of equipment physical indicators and the historical series set of equipment physical indicators to obtain a difference vector; The evaluation index prediction model is subjected to feedback correction processing by utilizing the difference vector to obtain an evaluation index feedback prediction model.

6. The equipment effectiveness feedback evaluation method according to claim 5, characterized in that: The method of performing feedback correction processing on the evaluation index prediction model by using the difference vector to obtain the evaluation index feedback prediction model includes: Calculate the median value a1 and mode value a2 of the difference vector; Calculate the variance value α and the range value β of the coefficient vector of the evaluation index prediction model; The difference vector and the coefficient vector are updated and calculated to obtain an updated coefficient vector, and the updated coefficient vector is substituted into the evaluation index prediction model to obtain an evaluation index feedback prediction model.

7. The equipment effectiveness feedback evaluation method according to claim 6, characterized in that: The expression for the update calculation process is: Among them, p i is the i-th element of the coefficient vector, pe i is the i-th element of the updated coefficient vector, A i is the i-th element of the difference vector.

8. An equipment performance feedback evaluation device, characterized in that: The device comprises: a memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the equipment effectiveness feedback evaluation method according to any one of claims 1 to 7.

9. A computer storable medium, characterized in that The computer storable medium stores computer instructions, and when the computer instructions are called by a computer, they are used to execute the equipment effectiveness feedback evaluation method according to any one of claims 1 to 7.

10. An information data processing terminal, characterized in that: The information data processing terminal is used to implement the equipment effectiveness feedback evaluation method according to any one of claims 1 to 7.

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