Technical performance evaluation method and device for field support safeguard system

Abstract

The invention discloses a technical performance evaluation method and device for a field support safeguard system. The method comprises the following steps: obtaining a test index set of each subsystem; the test index set comprises test result information of a plurality of test indexes; the test result information is a data sequence; performing data preprocessing on all the test index sets to obtain a preprocessed index set; performing performance evaluation processing on the preprocessing index set to obtain a performance evaluation value set; and performing fusion evaluation processing on the performance evaluation value set to obtain a technical performance evaluation result value of the field support guarantee system.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of technical evaluation and data processing, in particular to a technical performance evaluation method and device for field support system. BACKGROUND

[0002] With the increasing complexity of field operation tasks, the reliability and effectiveness of field support system become the key factors affecting the success of the task. Field support system is usually composed of multiple subsystems, such as material supply, equipment maintenance, medical support, communication support, etc. These subsystems work together in complex and variable field environment to ensure the smooth progress of the task. However, the traditional field support system evaluation method has many shortcomings. For example, the evaluation process often relies on subjective experience, lacking systematic quantitative analysis means; the data processing process is easily disturbed by noise and outliers, resulting in inaccurate evaluation results; in addition, the data between different subsystems lack unified time alignment and category check, making it difficult to achieve comprehensive evaluation. In recent years, with the development of information technology and data analysis technology, more and more researches have begun to focus on how to comprehensively and objectively evaluate the field support system through scientific methods.

[0003] At the same time, the construction of the existing technical performance evaluation index system of field support system often lacks the pertinence to the actual field environment, the data processing method is not perfect, and it is difficult to adapt to the complex and variable field operation scene. In addition, field support system faces many challenges in practical application. For example, field repair shop needs to repair and support equipment quickly in complex environment, while the existing evaluation method is difficult to evaluate and optimize this dynamic support process in real time. Therefore, it is of great practical significance to develop a scientific, systematic and efficient technical performance evaluation method for field support system. SUMMARY

[0004] The present application mainly solves the problem of developing a scientific, systematic and efficient technical performance evaluation method for field support system, and discloses a technical performance evaluation method and device for field support system.

[0005] In the first aspect of the present application, a technical performance evaluation method for field support system is disclosed, comprising: S1, obtaining a test index set of each subsystem of the field support system; the test index set comprises test results information of a plurality of test indexes; the test results information is a data sequence; S2, data preprocessing is performed on all test index sets to obtain a preprocessed index set; S3, performance evaluation processing is performed on the preprocessed index set to obtain a performance evaluation value set; S4, performing a fusion evaluation process on the performance evaluation value set to obtain a technical performance evaluation result value of the field support guarantee system.

[0006] The data preprocessing on all test index sets to obtain a preprocessed index set comprises: S21, performing data cleaning processing on all test index sets to obtain a first data set; S22, performing time alignment processing on the first data set to obtain a second data set; S23, performing category inspection processing on the second data set to obtain a third data set; S24, performing reliability discrimination processing on the third data set to obtain a preprocessed index set.

[0007] The reliability discrimination processing on the third data set to obtain a preprocessed index set comprises: S241, for each test index set in the third data set, taking each type of data sequence in the test index set as a row vector to construct an approximation matrix; taking the data acquisition information of each type of data sequence in the test index set to construct an acquisition matrix; the row vector of the acquisition matrix is the data acquisition information of a type of data sequence; taking the approximation matrix as the dependent variable and the acquisition matrix as the independent variable to construct a pattern matching model; S242, performing solving processing on the pattern matching model to obtain a matching model of each test index set; the expression of the matching model is , is the input acquisition matrix of the matching model, and the matrix is obtained by solving the pattern matching model; S243, using the matching model of each test index set to perform calculation processing on the data acquisition information of the data sequence of the corresponding test index set to obtain a solving matrix; S244, for each data in the solving matrix, subtracting the corresponding data in the approximation matrix which has the same row and column as the data in the solving matrix and taking the absolute value to obtain a difference value of the data; S245, deleting the data with a difference value greater than a set difference threshold from the third data set; S246, performing S241 to S245 on all types of test index sets in the third data set to obtain a preprocessed index set.

[0008] The performance evaluation processing on the preprocessed index set to obtain a performance evaluation value set comprises: Obtaining a standard index value set of each subsystem; Subtracting the test result information of each test index in the test index set of each subsystem in the preprocessing index set from the corresponding standard index in the standard index value set of the corresponding subsystem respectively to obtain an index difference value sequence; For each subsystem, all index difference value sequences are used to construct a corresponding index evaluation matrix; The index evaluation matrix is subjected to evaluation calculation processing to obtain evaluation values of all test indexes of the subsystem; The evaluation values of all test indexes of the subsystem are used to construct a performance evaluation value set; the performance evaluation value set includes the evaluation values of all test indexes of each subsystem.

[0009] The evaluation calculation processing includes: , Wherein, is a preset scale factor, is the evaluation value of the i-th test index of the subsystem, is the element of the i-th row and the j-th column of the index evaluation matrix of the subsystem, is the mean value of the i-th row of the index evaluation matrix of the subsystem, and M is the column dimension of the index evaluation matrix.

[0010] The performance evaluation value set is subjected to fusion evaluation processing to obtain a technical performance evaluation result value of the field support system, which includes: The performance evaluation value set is used to construct a performance evaluation matrix; The performance evaluation matrix is subjected to dimension increasing processing to obtain a three-dimensional evaluation matrix; The three-dimensional evaluation matrix is subjected to fusion evaluation calculation processing to obtain a technical performance evaluation result value of the field support system.

[0011] The expression of the dimension increasing processing is: , Wherein, and represent the i-th row vector and the j-th row vector of the performance evaluation matrix respectively, represents the i-th row and the j-th column vector of the three-dimensional evaluation matrix, and dot represents vector cross multiplication operation.

[0012] The second aspect of the present application discloses a technical performance evaluation device of a field support system, which comprises: A memory storing executable program codes; A processor coupled with the memory; The processor calls the executable program code stored in the memory to execute the technical performance evaluation method of the field support system.

[0013] The third aspect of the present application discloses a computer storage medium, which stores computer instructions, and when the computer instructions are called by a computer, the technical performance evaluation method of the field support system is executed.

[0014] The fourth aspect of the present application discloses an information data processing terminal, which is used to implement the technical performance evaluation method of the field support system.

[0015] The beneficial effects of the present application are: The present application provides a technical performance evaluation method of a field support system, which has the following beneficial effects: 1. Objectivity and accuracy: By obtaining the test index set of each subsystem and preprocessing the data, including data cleaning, time alignment, category checking and reliability discrimination, noise and outliers are effectively removed, ensuring the quality and consistency of the data, thereby providing a reliable basis for subsequent performance evaluation. Compared with the traditional subjective evaluation method, the present application can more objectively and accurately reflect the actual performance of the field support system.

[0016] 2. Systematic and comprehensive: The present application not only evaluates the performance of a single subsystem, but also integrates the performance evaluation values of all subsystems to obtain the comprehensive evaluation result value of the entire field support system. This systematic evaluation method can comprehensively consider the interaction and synergistic effect between each subsystem, avoiding the one-sidedness caused by single index evaluation, and providing a scientific basis for the overall optimization of the field support system.

[0017] 3. Efficiency and practicality: The evaluation calculation process of the present application uses a scientific mathematical model to quickly calculate the evaluation values of the test indexes of each subsystem and construct the performance evaluation value set. This efficient evaluation method can complete the comprehensive evaluation of the field support system in a short time, and is suitable for complex and variable field operation scenarios, providing strong support for real-time optimization and dynamic adjustment of the support system.

[0018] 4. Adaptability and flexibility: The present application performs time alignment and category checking on the data in the data preprocessing stage, which can effectively handle the data differences between different subsystems, making it suitable for various types of field support systems. In addition, by adjusting the preset proportion factor and other parameters, the present application can be flexibly adjusted according to different evaluation requirements and environmental conditions, and has strong adaptability and universality. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 An implementation flowchart of the method of the present application. DETAILED DESCRIPTION

[0020] For a better understanding of the present application, an embodiment is given here.

[0021] Figure 1 An implementation flowchart of the method of the present application.

[0022] In a first aspect of the embodiments of the present application, a technical performance evaluation method of a field support system is disclosed, comprising: S1, obtaining a test index set of each subsystem; the test index set comprises test results information of a plurality of test indexes; the test results information is a data sequence; S2, performing data preprocessing on all test index sets to obtain a preprocessed index set; S3, performing performance evaluation processing on the preprocessed index set to obtain a performance evaluation value set; S4, performing fusion evaluation processing on the performance evaluation value set to obtain a technical performance evaluation result value of the field support system.

[0023] The data preprocessing on all test index sets to obtain a preprocessed index set comprises: S21, performing data cleaning processing on all test index sets to obtain a first data set; S22, performing time alignment processing on the first data set to obtain a second data set; S23, performing category inspection processing on the second data set to obtain a third data set; S24, performing reliability discrimination processing on the third data set to obtain a preprocessed index set; The reliability discrimination processing on the third data set to obtain a preprocessed index set comprises: S241, for each test index set in the third data set, taking each type of data sequence in the test index set as a row vector to construct an approximation matrix; taking the data acquisition information of each type of data sequence in the test index set to construct an acquisition matrix; the row vector of the acquisition matrix is the data acquisition information of a type of data sequence; taking the approximation matrix as the dependent variable and the acquisition matrix as the independent variable to construct a pattern matching model; S242, solving the pattern matching model to obtain a matching model of each test index set; the expression of the matching model is , To match the input of the model, a collection matrix is acquired, and the matrix By solving the pattern matching model, a solution matrix is obtained. S243, using the matching model of each test index set, the data collection information of the data sequence of the corresponding test index set is calculated and processed to obtain a solution matrix. S244, for each data in the solution matrix, subtract the corresponding data in the approximation matrix with the same row and column as the data in the solution matrix and take the absolute value to obtain the difference value of the data. S245, the data with a difference value greater than a set difference threshold is deleted from the third data set. S246, for all types of test index sets in the third data set, S241 to S245 are executed to obtain a preprocessed index set.

[0024] The pattern matching model formula is the core of building the association between test data and collection information, and is mainly used for subsequent data reliability discrimination. Its beneficial effects are reflected in three aspects: accurate establishment of corresponding relationship: by clearly defining the mathematical association between test data sequence (such as power supply stability data of the accommodation subsystem) and collection information (such as collection time and place code), a strong binding between data source and data itself is formed, avoiding the problem of data and collection scene disconnection in subsequent analysis. Ensure the stability of model solving: the introduction of the unit matrix in the formula can effectively avoid the occurrence of singular problems such as matrix irreversibility and numerical divergence in the solving process, ensuring the stability of the matching model structure obtained subsequently, and providing a reliable calculation basis for reliability discrimination. Reduce the probability of data misjudgment: by quantifying the matching rule of collection information and test data, the rationality of the data can be verified based on the model subsequently, which greatly reduces the probability of reliable data misjudgment or abnormal data retention caused by subjective factors compared with traditional experience judgment.

[0025] The performance evaluation value set is obtained by performing performance evaluation processing on the preprocessed index set, including: A standard index value set of each subsystem is obtained. The test result information of each test index in the test index set of each subsystem in the preprocessed index set is subtracted from the corresponding standard index in the standard index value set of the corresponding subsystem to obtain an index difference value sequence. For each subsystem, all index difference value sequences are used to construct a corresponding index evaluation matrix. The index evaluation matrix is evaluated and calculated to obtain the evaluation value of all test indexes of the subsystem. The performance evaluation value set is constructed by using the evaluation values of all test indexes of the subsystems, and includes the evaluation values of all test indexes of each subsystem. The evaluation calculation process includes: , wherein, is a preset scale factor, is the evaluation value of the i-th test index of the subsystem, is an element of the i-th row and j-th column of the index evaluation matrix of the subsystem, is the average value of the i-th row of the index evaluation matrix of the subsystem, and M is the column dimension of the index evaluation matrix.

[0026] The evaluation calculation process is used to calculate the evaluation value of each test index in a single subsystem (such as a medical subsystem or a meal subsystem). The scale factor in the formula can adjust the value range according to the characteristics of the index (such as the "emergency response time" of the medical subsystem and the "hot food supply efficiency" of the meal subsystem), and solve the problem that the evaluation values cannot be directly compared due to the large difference in the order of magnitude of different indexes (such as "response time in minutes and supply efficiency in percentage"). By calculating the relationship between the single test data and the overall average value of the index, both the overall performance level of the index (such as the average performance of a certain index in multiple tests) and the local abnormal data (such as a large deviation in a certain test) can be highlighted, so that the evaluation value can more accurately reflect the real performance of the index. The data column dimension (i.e., the total amount of test data of the index) is integrated into the formula to avoid extreme evaluation values caused by a small number of tests of a certain index (such as medical emergency tests in special scenarios), and to ensure that the index evaluation value can objectively reflect the actual performance regardless of the amount of data.

[0027] The performance evaluation value set is fused and evaluated to obtain a technical performance evaluation result value of the field support system, including: The performance evaluation value set is fused and evaluated to obtain a technical performance evaluation result value of the field support system, including: The performance evaluation matrix is constructed by using the performance evaluation value set. The three-dimensional evaluation matrix is obtained by performing dimension increasing processing on the performance evaluation matrix. The dimension increasing processing expression is: , wherein, and represent the i-th row vector and the j-th row vector of the performance evaluation matrix, respectively, represents the i-th row and j-th column vector of the three-dimensional evaluation matrix, and dot represents the cross product operation of vectors.

[0028] The expression of the dimension increasing processing is used to extend the two-dimensional sub-system index evaluation matrix to a three-dimensional matrix, and the core value is to capture the correlation between parts. By performing dot product operation on the evaluation value vectors of any two sub-systems (or index groups), the correlation degree between them can be quantified (such as whether the tent setting speed of the accommodation sub-system and the field stove group deployment efficiency of the food sub-system are coordinated), breaking the limitations of traditional two-dimensional evaluation isolated analysis of a single sub-system. The correlation degree between sub-systems / indices is included in the evaluation system as a new dimension, so that the subsequent fusion evaluation not only considers the performance of a single sub-system, but also takes into account the coordination ability of each sub-system, which is particularly important for field support systems (which require multiple system cooperation) and is more in line with actual use scenarios. Provide data basis for overall fusion: the three-dimensional matrix stores complete information of "sub-system-sub-system-correlation degree", and the subsequent fusion evaluation can directly call these correlation data, avoid one-sided evaluation due to information missing, and make the overall result more comprehensive.

[0029] The performance evaluation matrix is constructed by using the set of performance evaluation values, and each sub-system in the set of performance evaluation values is used as a row vector.

[0030] The expression of the fusion evaluation calculation processing is: , wherein, represents the kth element of the vector of the ith row and the jth column of the three-dimensional evaluation matrix, I, J and K are the first dimension, the second dimension and the third dimension of the three-dimensional evaluation matrix respectively, and G is the technical performance evaluation result value of the field support system.

[0031] The data cleaning processing includes filling missing values, smoothing noise data, smoothing or deleting outlier points; the smoothing of noise data is to first identify noise data, and then smooth the noise data according to the data before and after the noise data; the noise data is a value less than the detection sensitivity of the sensor of the observation data or greater than the measurement upper limit of the sensor of the observation data. The identification of the outlier point can use Kalman filtering method. For the determination of the filling value of the missing value, the measurement values within a certain sampling interval before and after the missing value can be averaged.

[0032] The time alignment processing can be realized by using a time registration processing algorithm; the time registration processing is to unify different types of data to the same time reference; the time registration processing can use interpolation method, Lagrange three-point interpolation method, etc. The category checking processing is to check whether each data in the data set is consistent with the preset data type, and to delete the inconsistent data from the data set.

[0033] The pattern matching model is expressed as: , , Wherein, Indicates a unit matrix with the row dimension of the matrix As the dimension, the matrix Indicates a matrix to be solved, Indicates a collection matrix, Indicates an approximation matrix; The solving algorithm of the pattern matching model can adopt a genetic algorithm and a particle filtering algorithm; The test index set is obtained by performance testing of each subsystem; The data collection information can be collection time information or numerical coding information of collection location; The subsystem of the field index guarantee system includes a lodging subsystem, a meal subsystem, a medical treatment subsystem and the like.

[0034] The second aspect of the present application discloses a technical performance evaluation device of a field support guarantee system, which comprises: A memory storing executable program codes; A processor coupled with the memory; The processor calls the executable program codes stored in the memory to execute the technical performance evaluation method of the field support guarantee system.

[0035] The third aspect of the present application discloses a computer storage medium storing computer instructions, which are called by a computer to execute the technical performance evaluation method of the field support guarantee system.

[0036] The fourth aspect of the present application discloses an information data processing terminal for realizing the technical performance evaluation method of the field support guarantee system.

[0037] The fifth aspect of the present application discloses a technical performance evaluation device of a field support guarantee system for realizing the technical performance evaluation method of the field support guarantee system, which comprises an index collection module, a preprocessing module and a performance evaluation module. The index collection module is used for obtaining a test index set of each subsystem; the test index set comprises test result information of a plurality of test indexes; and the test result information is a data sequence. The preprocessing module is connected with the index collection module and the performance evaluation module respectively, and is used for performing data preprocessing on all test index sets to obtain a preprocessed index set. The performance evaluation module is used for performing performance evaluation processing on the preprocessed index set to obtain a performance evaluation value set, and performing fusion evaluation processing on the performance evaluation value set to obtain a technical performance evaluation result value of the field support system.

[0038] The technical performance evaluation result value of the field support system is used for representing the performance excellence degree of the system, and the smaller the value is, the better the system performance is.

[0039] Each module of the technical performance evaluation device of the field support system comprises a software system and a hardware system, and the software system is used for realizing a corresponding algorithm.

[0040] In the embodiment of the present application, all calculation expressions or mathematical functions are subjected to dimensionless processing on variables involved before calculation.

[0041] In the embodiment of the present application, the input variable values of all calculation expressions or mathematical functions meet the rationality requirements of the input variable value range of the calculation expressions or mathematical functions, and can ensure that the calculation expressions or mathematical functions can be calculated smoothly without violating the physical laws or mathematical rules.

[0042] The above merely describes the embodiments of the present application, and is not used for limiting the present application. The present application can be variously changed and modified by those skilled in the art. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the scope of claims of the present application.

Claims

1. A method for evaluating technical performance of a field support system, characterized by, The method comprises the following steps: S1, obtaining a test index set of each subsystem of the field support system; the test index set comprises test result information of a plurality of test indexes; The test result information is a data sequence; S2, performing data preprocessing on all test index sets to obtain a preprocessed index set; S3, performing performance evaluation processing on the preprocessed index set to obtain a performance evaluation value set; S4, performing fusion evaluation processing on the performance evaluation value set to obtain a technical performance evaluation result value of the field support system.

2. The method of claim 1, wherein the technical performance of the field support system is evaluated based on the at least one of the plurality of technical performance evaluation items. The data preprocessing on all test index sets to obtain a preprocessed index set comprises the following steps: S21, performing data cleaning processing on all test index sets to obtain a first data set; S22, performing time alignment processing on the first data set to obtain a second data set; S23, performing category inspection processing on the second data set to obtain a third data set; S24, performing reliability discrimination processing on the third data set to obtain a preprocessed index set.

3. The method of claim 2, wherein the technical performance of the field support system is evaluated based on the evaluation result of the field support system. The reliability discrimination processing on the third data set to obtain a preprocessed index set comprises the following steps: S241, for each test index set in the third data set, taking each type of data sequence in the test index set as a row vector to construct an approximation matrix; taking the data acquisition information of each type of data sequence in the test index set to construct an acquisition matrix; the row vector of the acquisition matrix is the data acquisition information of one type of data sequence; taking the approximation matrix as the dependent variable and the acquisition matrix as the independent variable to construct a pattern matching model; S242, performing a solving process on the pattern matching model to obtain a matching model of each test index set; an expression of the matching model is , is a collection matrix input for the matching model, and the matrix is obtained by solving the pattern matching model. S243, using the matching model of each test index set to calculate and process the data acquisition information of the data sequence of the corresponding test index set to obtain a solution matrix; S244, for each data in the solution matrix, subtracting the corresponding data in the approximation matrix which has the same row and column as the data in the solution matrix and taking the absolute value to obtain a difference value of the data; S245, deleting the data with a difference value greater than a set difference threshold from the third data set; S246, performing S241 to S245 on all types of test index sets in the third data set to obtain a preprocessed index set.

4. The method for evaluating the technical performance of a field support system as described in claim 1, characterized in that, The performance evaluation processing on the preprocessed index set to obtain a performance evaluation value set comprises the following steps: Obtaining a standard index value set of each subsystem; Subtracting the test result information of each test index in the test index set of each subsystem in the preprocessed index set from the corresponding standard index in the standard index value set of the corresponding subsystem to obtain an index difference value sequence; For each subsystem, using all index difference value sequences to construct a corresponding index evaluation matrix; Performing evaluation calculation processing on the index evaluation matrix to obtain evaluation values of all test indexes of the subsystem. The performance evaluation value set is constructed by using the evaluation values of all test indexes of the subsystems, and includes the evaluation values of all test indexes of each subsystem.

5. The method for evaluating the technical performance of a field support system as described in claim 1, characterized in that, The evaluation calculation process includes: ， wherein, is a preset scale factor, is an evaluation value of the ith test index of the subsystem, is an element of the ith row and jth column of the index evaluation matrix of the subsystem, is the mean value of the ith row of the index evaluation matrix of the subsystem, and M is the column dimension of the index evaluation matrix.

6. The method of claim 1, wherein the technical performance evaluation of the field support system is based on at least one of the following: The fusion evaluation process is performed on the performance evaluation value set to obtain a technical performance evaluation result value of the field support system, including: ​ The performance evaluation matrix is constructed by using the performance evaluation value set; The three-dimensional evaluation matrix is obtained by performing dimension increasing processing on the performance evaluation matrix; The fusion evaluation calculation process is performed on the three-dimensional evaluation matrix to obtain a technical performance evaluation result value of the field support system.

7. The method for evaluating the technical performance of a field support system as described in claim 1, characterized in that, The expression of the dimension increasing processing is: ， wherein, and denote the i-th row vector and the j-th row vector of the performance evaluation matrix, respectively, denotes the i-th row, j-th column vector of the three-dimensional evaluation matrix, and dot denotes the vector cross product operation.

8. A technical performance evaluation device for a field support system, characterized by, The device includes: A memory storing executable program codes; A processor coupled with the memory; The processor invokes the executable program codes stored in the memory to execute the technical performance evaluation method of the field support system according to any one of claims 1 to 7.

9. A computer storable medium, characterized by The computer storage medium stores computer instructions, which are invoked by a computer to execute the technical performance evaluation method of the field support system according to any one of claims 1 to 7.

10. An information data processing terminal, characterized by The information data processing terminal is used to implement the technical performance evaluation method of the field support system according to any one of claims 1 to 7.

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