A test evaluation method and device for an industrial park comprehensive management system

Abstract

The application discloses a kind of industrial park comprehensive management system test evaluation method and device, the method includes: the performance index data set of industrial park comprehensive management system is obtained by acquisition;The performance index data set includes the performance index test data sub-set of each sub-system;The performance index test data sub-set includes the test data sequence of each performance index;The performance index data set is preprocessed, and the to-be-evaluated data set is obtained;The to-be-evaluated data set is tested and evaluated, and the performance evaluation result value of industrial park comprehensive management system is obtained.

Description

Technical Field

[0001] This invention relates to the fields of industrial data processing, performance testing and evaluation, and strategy optimization, specifically to a testing and evaluation method and apparatus for an integrated management system for industrial parks. Background Technology

[0002] Industrial park integrated management systems are core tools for the efficient operation of modern industrial parks, and their performance directly impacts the park's management efficiency, resource utilization, and production benefits. However, existing testing and evaluation methods for industrial park integrated management systems have many shortcomings. On the one hand, traditional testing methods often focus on the performance testing of single functional modules, lacking a comprehensive evaluation of the overall system performance and failing to reflect the system's overall performance in complex real-world operating environments. On the other hand, existing methods often neglect data integrity and reliability during the data processing phase, making them susceptible to interference from abnormal data or noise, leading to distorted evaluation results. Furthermore, for integrated management systems with multiple subsystems and performance indicators, the lack of effective data synchronization and classification mechanisms makes the evaluation process complex and inefficient. Therefore, designing a method that can comprehensively, accurately, and efficiently evaluate the performance of industrial park integrated management systems is a pressing technical problem that needs to be solved. Summary of the Invention

[0003] This invention primarily addresses the problem of designing a method and apparatus for comprehensively, accurately, and efficiently evaluating the performance of an integrated management system for industrial parks. This invention discloses a testing and evaluation method and apparatus for an integrated management system for industrial parks.

[0004] In a first aspect, this invention discloses a testing and evaluation method for an integrated management system of an industrial park, comprising:

[0005] S1, collect the performance index data set of the industrial park integrated management system; the performance index data set includes a subset of performance index test data for each subsystem; the performance index test data subset includes a test data sequence for each performance index.

[0006] S2, preprocess the performance index data set to obtain the data set to be evaluated;

[0007] S3, perform test and evaluation processing on the dataset to be evaluated to obtain the performance evaluation result value of the industrial park integrated management system.

[0008] The preprocessing of the performance index data set to obtain the data set to be evaluated includes:

[0009] S21, perform data cleaning on the performance index dataset to obtain the first dataset;

[0010] S22, perform time alignment processing on the first dataset to obtain the second dataset;

[0011] S23, Perform category checking on the second dataset to obtain the third dataset;

[0012] S24, perform credibility discrimination processing on the third dataset to obtain the dataset to be evaluated.

[0013] The process of testing and evaluating the dataset to be evaluated to obtain the performance evaluation result value of the industrial park integrated management system includes:

[0014] S31, Perform statistical feature analysis on the dataset to be evaluated to obtain sampled feature values;

[0015] S32, Based on the sampled feature values, the dataset to be evaluated is evenly divided to obtain several time-period subsets of the dataset to be evaluated;

[0016] S33, perform test evaluation on the subset of data to be evaluated for each time period to obtain the sub-evaluation value for each time period;

[0017] S34 integrates and evaluates the sub-evaluation values ​​of all time periods to obtain the performance evaluation result value of the industrial park integrated management system.

[0018] The step of performing statistical feature analysis on the dataset to be evaluated to obtain sampled feature values ​​includes:

[0019] S311, using the test data sequence of each performance index in the dataset to be evaluated as a row vector, a test matrix is ​​constructed;

[0020] S312, calculate the rank and norm of the test matrix;

[0021] S313, Perform a Demon transformation on each row vector of the test matrix to obtain the corresponding transformation vector; use all the transformation vectors to construct the transformation test matrix;

[0022] S314, Calculate the feature dimension of the transformation test matrix and the test matrix to obtain the feature dimension;

[0023] S315, determine the greatest common divisor of the feature dimension and the column dimension of the test matrix, which is the sampled feature value.

[0024] The expression for calculating the feature dimension is:

[0025]

[0026] Where ω1 and ω2 are preset weighting factors, M and N are the row and column dimensions of the transformation test matrix, respectively, μ and γ are the rank and norm of the test matrix, respectively, and D i Let A represent the i-th dimension factor, D be the feature dimension, and A be the feature dimension. ij and B ij These are the elements in the i-th row and j-th column of the transformation test matrix and the test matrix, respectively. This indicates that for min(D) i Round down.

[0027] The process of testing and evaluating the subset of data to be evaluated for each time period to obtain a sub-evaluation value for each time period includes:

[0028] S331, For each time period, based on the test data subset of each performance indicator in the corresponding subset of data to be evaluated, perform sub-item performance evaluation processing to obtain the evaluation value and weight value of each subsystem;

[0029] S332, using the weight value of each subsystem, the evaluation value of each subsystem is weighted and summed to obtain the sub-evaluation value for each time period.

[0030] The test data subsets for each performance indicator in the subset of data to be evaluated are used for itemized performance evaluation to obtain the evaluation value and weight value of each subsystem, including:

[0031] S3311, for each subsystem in the subset of data to be evaluated, obtain the standard value of each performance indicator in the performance indicator test data subset;

[0032] S3312, using the test data sequence of each performance indicator in the performance indicator test data subset, subtract the standard value of the corresponding performance indicator from the test data sequence of the performance indicator to obtain the corresponding difference sequence;

[0033] S3313, Perform feature matrix transformation on each difference sequence of the performance index test data subset to obtain the corresponding feature matrix;

[0034] S3314, evaluate and calculate all feature matrices of the performance index test data subset to obtain the evaluation value and weight value of the corresponding subsystem.

[0035] According to a second aspect of the present invention, a testing and evaluation device for an integrated management system of an industrial park is disclosed, the device comprising:

[0036] Memory containing executable program code;

[0037] A processor coupled to the memory;

[0038] The processor calls the executable program code stored in the memory to execute the test and evaluation method of the industrial park integrated management system.

[0039] In a third aspect, the present invention discloses a computer-storable medium storing computer instructions, which, when invoked by a computer, are used to execute the test and evaluation method of the industrial park integrated management system.

[0040] In a fourth aspect, the present invention discloses an information data processing terminal, which is used to implement the testing and evaluation method of the industrial park integrated management system.

[0041] The beneficial effects of this invention are as follows:

[0042] This invention provides a testing and evaluation method for an integrated management system of an industrial park. Through systematic data preprocessing and a refined testing and evaluation process, it effectively solves the problems existing in the prior art. First, by performing multi-step preprocessing on the performance index dataset, including data cleaning, time alignment, category checking, and reliability judgment, the integrity and accuracy of the input data are ensured, providing a reliable data foundation for subsequent evaluation.

[0043] This invention employs statistical feature analysis and uniform partitioning methods to dynamically adjust the evaluation granularity based on the actual system operation, making the evaluation results more closely reflect the actual performance of the industrial park integrated management system. Furthermore, by independently evaluating the subset of data to be evaluated for each time period and then fusing all sub-evaluation values, not only is the granularity of the evaluation improved, but the stability and reliability of the evaluation results are also enhanced. Compared with existing technologies, this invention can comprehensively evaluate the performance of the industrial park integrated management system, effectively improving evaluation efficiency and accuracy, and providing strong support for the optimized management and decision-making of industrial parks. Attached Figure Description

[0044] Figure 1 This is a flowchart illustrating the implementation of the method of the present invention. Detailed Implementation

[0045] To better understand the content of this invention, an embodiment is provided here.

[0046] Figure 1 This is a flowchart illustrating the implementation of the method of the present invention.

[0047] In a first aspect, this invention discloses a testing and evaluation method for an integrated management system of an industrial park, comprising:

[0048] S1, collect the performance index data set of the industrial park integrated management system; the performance index data set includes a subset of performance index test data for each subsystem; the performance index test data subset includes a test data sequence for each performance index.

[0049] S2, preprocess the performance index data set to obtain the data set to be evaluated;

[0050] S3, perform test and evaluation processing on the dataset to be evaluated to obtain the performance evaluation result value of the industrial park integrated management system;

[0051] The preprocessing of the performance index data set to obtain the data set to be evaluated includes:

[0052] S21, perform data cleaning on the performance index dataset to obtain the first dataset;

[0053] S22, perform time alignment processing on the first dataset to obtain the second dataset;

[0054] S23, Perform category checking on the second dataset to obtain the third dataset;

[0055] S24, perform credibility discrimination processing on the third dataset to obtain the dataset to be evaluated.

[0056] The process of testing and evaluating the dataset to be evaluated to obtain the performance evaluation result value of the industrial park integrated management system includes:

[0057] S31, Perform statistical feature analysis on the dataset to be evaluated to obtain sampled feature values;

[0058] S32, Based on the sampled feature values, the dataset to be evaluated is evenly divided to obtain several time-period subsets of the dataset to be evaluated;

[0059] The step of uniformly dividing the dataset to be evaluated based on sampling feature values ​​to obtain several time-period subsets of the dataset to be evaluated involves uniformly dividing the test data sequence of each performance indicator in the dataset to be evaluated into data sequences with the number of elements equal to the sampling feature value, and using the data sequences of all performance indicators in the same time period to construct the data subset to be evaluated for the time period.

[0060] S33, perform test evaluation on the subset of data to be evaluated for each time period to obtain the sub-evaluation value for each time period;

[0061] S34 integrates and evaluates the sub-evaluation values ​​of all time periods to obtain the performance evaluation result value of the industrial park integrated management system.

[0062] The fusion evaluation involves using a preset weighting factor to sum the sub-evaluation values ​​for each time period, resulting in a performance evaluation result for the industrial park's integrated management system. The weighting factor can be obtained by taking the reciprocal of the average time value at each time point.

[0063] The step of performing statistical feature analysis on the dataset to be evaluated to obtain sampled feature values ​​includes:

[0064] S311, using the test data sequence of each performance index in the dataset to be evaluated as a row vector, a test matrix is ​​constructed;

[0065] S312, calculate the rank and norm of the test matrix;

[0066] S313, Perform a Demon transformation on each row vector of the test matrix to obtain the corresponding transformation vector; use all the transformation vectors to construct the transformation test matrix;

[0067] S314, Calculate the feature dimension of the transformation test matrix and the test matrix to obtain the feature dimension;

[0068] S315, determine the greatest common divisor of the feature dimension and the column dimension of the test matrix, which is the sampled feature value.

[0069] The expression for calculating the feature dimension is:

[0070]

[0071] Where ω1 and ω2 are preset weighting factors, M and N are the row and column dimensions of the transformation test matrix, respectively, μ and γ are the rank and norm of the test matrix, respectively, and D i Let A represent the i-th dimension factor, D be the feature dimension, and A be the feature dimension. ij and B ij These are the elements in the i-th row and j-th column of the transformation test matrix and the test matrix, respectively. This indicates that for min(D) i Round down.

[0072] The expression for calculating the feature dimension comprehensively considers multiple factors, including the rank and norm of the test matrix, as well as the relationship between the transformed test matrix and its corresponding elements. The interaction between the rank and norm is reflected by tan(μ / γ), and arcsin(A... ij / B ij This reflects the proportional relationship between the matrix elements before and after the transformation, exp(-|R ij -T ij|) This approach considers the degree of difference between elements, thus comprehensively capturing the characteristics of the data and making the calculated feature dimensions more accurately reflect the inherent structure of the data. By considering min(D)... i The final feature dimension D is obtained by rounding down. This method ensures that the feature dimension is an integer, which is consistent with the concept of dimension in practical applications. At the same time, it avoids the computational complexity and uncertainty caused by decimal dimensions.

[0073] The process of testing and evaluating the subset of data to be evaluated for each time period to obtain a sub-evaluation value for each time period includes:

[0074] S331, For each time period, based on the test data subset of each performance indicator in the corresponding subset of data to be evaluated, perform sub-item performance evaluation processing to obtain the evaluation value and weight value of each subsystem;

[0075] S332, using the weight value of each subsystem, the evaluation value of each subsystem is weighted and summed to obtain the sub-evaluation value for each time period.

[0076] The test data subsets for each performance indicator in the subset of data to be evaluated are used for itemized performance evaluation to obtain the evaluation value and weight value of each subsystem, including:

[0077] S3311, for each subsystem in the subset of data to be evaluated, obtain the standard value of each performance indicator in the performance indicator test data subset;

[0078] S3312, using the test data sequence of each performance indicator in the performance indicator test data subset, subtract the standard value of the corresponding performance indicator from the test data sequence of the performance indicator to obtain the corresponding difference sequence;

[0079] S3313, Perform feature matrix transformation on each difference sequence of the performance index test data subset to obtain the corresponding feature matrix;

[0080] S3314, evaluate and calculate all feature matrices of the performance index test data subset to obtain the evaluation value and weight value of the corresponding subsystem;

[0081] The expression for the transformation of the characteristic matrix is:

[0082]

[0083] Where s(k) is the kth element of the difference sequence, NS is the length of the difference sequence, t(a,b) is the element in the a-th row and b-th column of the feature matrix, u() is the transformation function, u(kT1-zT1) is the value of u() at kT1-zT1, and T1 and F1 are the time-domain transformation length and frequency-domain transformation length, respectively.

[0084] The expression for the feature matrix transformation, through complex transformations of the difference sequence, can comprehensively capture the characteristics of the data in both the time and frequency domains. s(k) represents the elements of the difference sequence, and u(kT1-aT1) serves as the transformation function; its values ​​at different positions can uncover local features of the data in the time domain. This approach incorporates frequency domain information, enabling the transformed feature matrix to simultaneously reflect the time and frequency domain characteristics of the data, providing richer information for subsequent evaluation calculations. The expression considers the length NS of the difference sequence, as well as the time-domain and frequency-domain transformation lengths, allowing for transformation of difference sequences of varying lengths. This makes the method versatile in handling test data with different performance metrics, effectively extracting features regardless of data length variations.

[0085] The transformation function can be a Gaussian function.

[0086] The evaluation calculation of all feature matrices of the performance index test data subset to obtain the evaluation value and weight value of the corresponding subsystem includes:

[0087] For each feature matrix, the corresponding trace number and the mean of the variances of all row vectors are calculated;

[0088] All feature matrices are processed to obtain the evaluation values ​​and weight values ​​of the corresponding subsystems;

[0089] The calculation expressions for the evaluation value and weight value are as follows:

[0090]

[0091] Where L2() is the second-order Legendre function, and D i and Let be the mean and trace of the variances of all row vectors of the i-th feature matrix, respectively; M1 is the total number of feature matrices corresponding to the subsystem; and qz and fp are the weight and evaluation values ​​of the corresponding subsystem, respectively.

[0092] The mean of the variances of all row vectors is obtained by averaging the variances of all row vectors of each feature matrix.

[0093] The industrial park integrated management system includes a personnel access management subsystem, a park tool management subsystem, an oil, electricity and water operation monitoring and storage consumption replenishment management subsystem, and a central control software subsystem.

[0094] The personnel access management subsystem, the park tool management subsystem, and the oil, electricity and water operation monitoring and storage consumption replenishment management subsystem all include sensors and a host computer; the host computer runs corresponding management programs.

[0095] The personnel access management subsystem, the park tool management subsystem, and the oil, electricity and water operation monitoring and storage consumption replenishment management subsystem are all connected to the central control software subsystem.

[0096] The central control software subsystem is connected, including a server and control software running on the server.

[0097] The data cleaning process includes filling in missing values, smoothing noisy data, and smoothing or deleting outliers. Smoothing noisy data involves first identifying the noisy data, and then smoothing it based on the data preceding and following it. The noisy data refers to values ​​whose values ​​are less than the sensor's detection sensitivity for the observed data, or greater than the sensor's measurement limit for the observed data. Outlier identification can be performed using Kalman filtering. The filling values ​​for missing values ​​can be determined by averaging the measurements within a certain sampling interval before and after the missing value.

[0098] The time alignment process can be implemented using a time registration algorithm; the time registration process unifies different types of data onto the same time base; the time registration process can employ methods such as extrapolation / extrapolation and Lagrange three-point interpolation.

[0099] The category check process involves checking whether each piece of data in the dataset matches a preset data type, and deleting any data that does not match from the dataset.

[0100] The credibility determination process performed on the third dataset yields a dataset to be evaluated, including:

[0101] S241, For each type of data attribute in the third data set, with the data collection time of the data as the independent variable and the data value of the data as the dependent variable, perform autoregressive-moving average modeling to obtain the first approximation model of the data attribute.

[0102] S242, using all the information sequences of the information sequence set of each type of data attribute as row vectors, construct the information matrix of the data attribute of the type;

[0103] S243, Perform singular value calculation on the information matrix to obtain a singular value sequence;

[0104] S244, using the element values ​​of the singular value sequence as known dependent variables and the element indices of the singular value sequence as known independent variables, a curve to be approximated is constructed using the known independent variables and the known dependent variables; polynomial fitting is performed on the curve to be approximated to obtain the second approximation model of the data class attribute;

[0105] S245, Multiply the second approximation model and the first approximation model to obtain the fusion verification model of the class data attributes;

[0106] S246, Using the fusion test model for each type of data attribute, calculate and process the data acquisition time of the data of the aforementioned data attributes to obtain an approximate dependent variable;

[0107] S247, determine whether the absolute value of the difference between the approximate dependent variable and the corresponding data is greater than a set first regression discrimination threshold; if it is greater than the first regression discrimination threshold, delete the data from the third data set; if it is less than or equal to the first regression discrimination threshold, do not process the data.

[0108] S248, perform fusion processing on all data in the third data set after executing S246 to S247 to obtain the data set to be evaluated;

[0109] According to a second aspect of the present invention, a testing and evaluation device for an integrated management system of an industrial park is disclosed, the device comprising:

[0110] Memory containing executable program code;

[0111] A processor coupled to the memory;

[0112] The processor calls the executable program code stored in the memory to execute the test and evaluation method of the industrial park integrated management system.

[0113] In a third aspect, the present invention discloses a computer-storable medium storing computer instructions, which, when invoked by a computer, are used to execute the test and evaluation method of the industrial park integrated management system.

[0114] In a fourth aspect, the present invention discloses an information data processing terminal, which is used to implement the testing and evaluation method of the industrial park integrated management system.

[0115] The above description is merely an embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A testing and evaluation method for an integrated management system of an industrial park, characterized in that, include: S1, collects the performance index data set of the industrial park integrated management system; The performance index data set includes a subset of performance index test data for each subsystem; The performance indicator test data subset includes the test data sequence for each performance indicator; S2, preprocess the performance index data set to obtain the data set to be evaluated; S3, perform test and evaluation processing on the dataset to be evaluated to obtain the performance evaluation result value of the industrial park integrated management system, including: S31, Perform statistical feature analysis on the dataset to be evaluated to obtain sampled feature values, including: S311, using the test data sequence of each performance index in the dataset to be evaluated as a row vector, a test matrix is ​​constructed; S312, calculate the rank and norm of the test matrix; S313, Perform a Demon transformation on each row vector of the test matrix to obtain the corresponding transformation vector; use all the transformation vectors to construct the transformation test matrix; S314, Calculate the feature dimension of the transformation test matrix and the test matrix to obtain the feature dimension; S315, determine the greatest common divisor of the feature dimension and the column dimension of the test matrix, which is the sampled feature value; S32, Based on the sampled feature values, the dataset to be evaluated is evenly divided to obtain several time-period subsets of the dataset to be evaluated; S33, perform test evaluation on the subset of data to be evaluated for each time period to obtain the sub-evaluation value for each time period; S34 integrates and evaluates the sub-evaluation values ​​of all time periods to obtain the performance evaluation result value of the industrial park integrated management system.

2. The testing and evaluation method for the integrated management system of industrial parks as described in claim 1, characterized in that, The preprocessing of the performance index data set to obtain the data set to be evaluated includes: S21, perform data cleaning on the performance index dataset to obtain the first dataset; S22, perform time alignment processing on the first dataset to obtain the second dataset; S23, Perform category checking on the second dataset to obtain the third dataset; S24, perform credibility discrimination processing on the third dataset to obtain the dataset to be evaluated.

3. The testing and evaluation method for the integrated management system of industrial parks as described in claim 1, characterized in that, The expression for calculating the feature dimension is: Where ω1 and ω2 are preset weighting factors, M and N are the row and column dimensions of the transformation test matrix, respectively, μ and γ are the rank and norm of the test matrix, respectively, and D i Let A represent the i-th dimension factor, D be the feature dimension, and A be the feature dimension. ij and B ij These are the elements in the i-th row and j-th column of the transformation test matrix and the test matrix, respectively. This indicates that for min(D) i Round down.

4. The testing and evaluation method for the integrated management system of industrial parks as described in claim 1, characterized in that, The process of testing and evaluating the subset of data to be evaluated for each time period to obtain a sub-evaluation value for each time period includes: S331, For each time period, based on the test data subset of each performance indicator in the corresponding subset of data to be evaluated, perform sub-item performance evaluation processing to obtain the evaluation value and weight value of each subsystem; S332, using the weight value of each subsystem, the evaluation value of each subsystem is weighted and summed to obtain the sub-evaluation value for each time period.

5. The testing and evaluation method for the integrated management system of industrial parks as described in claim 4, characterized in that, The process involves performing sub-item performance evaluation on each performance index test data subset within the corresponding subset of data to be evaluated, yielding an evaluation value and weight value for each subsystem, including: S3311, for each subsystem in the subset of data to be evaluated, obtain the standard value of each performance indicator in the performance indicator test data subset; S3312, using the test data sequence of each performance indicator in the performance indicator test data subset, subtract the standard value of the corresponding performance indicator from the test data sequence of the performance indicator to obtain the corresponding difference sequence; S3313, Perform feature matrix transformation on each difference sequence of the performance index test data subset to obtain the corresponding feature matrix; S3314, evaluate and calculate all feature matrices of the performance index test data subset to obtain the evaluation value and weight value of the corresponding subsystem.

6. A testing and evaluation device for an integrated management system of an industrial park, characterized in that, The device includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the test and evaluation method of the industrial park integrated management system as described in any one of claims 1 to 5.

7. A computer-storable medium, characterized in that, The computer storage medium stores computer instructions, which, when invoked by the computer, are used to execute the test and evaluation method of the industrial park integrated management system as described in any one of claims 1 to 5.

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