A performance evaluation device and method for a hot food fast food security system

Abstract

The application discloses a performance evaluation device and method of a hot food fast food guarantee system, and the method comprises the following steps: collecting historical performance test data sets; the historical performance test data sets comprise test condition data sets and test test data sets; the test condition data sets comprise test condition setting data sequences of each test of the hot food fast food guarantee system; performing estimation processing on the historical performance test data sets to obtain a key test condition information set; collecting test test data sequence sets under test conditions corresponding to the key test condition information set; the test test data sequence sets comprise test test sequences of performance indexes of each type; and performing performance comprehensive evaluation processing on the test test data sequence sets to obtain a comprehensive performance evaluation value of the hot food fast food guarantee system.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of performance evaluation and industrial data processing, and particularly relates to a performance evaluation device and method for a hot food fast food guarantee system. BACKGROUND

[0002] In today's fast-paced life, the importance of hot food fast food guarantee system is increasingly prominent. Whether in the food supply scene of the catering industry on a large scale, or in special circumstances such as emergency rescue, hot food fast food guarantee system needs to run stably and efficiently to ensure that people can obtain quality hot food in time.

[0003] However, the existing performance evaluation method of hot food fast food guarantee system has many defects. On the one hand, the past evaluation often depends on a single or a few performance indicators, which cannot fully reflect the comprehensive performance of the system in complex and diverse actual operating environment. For example, only the food heating speed is concerned, while other key factors such as food holding time, energy consumption efficiency, equipment failure rate are ignored. On the other hand, the traditional evaluation method lacks fine consideration of test conditions in the process of data collection and analysis. Many test conditions that affect the performance of the system, such as environmental temperature, humidity, initial state of food materials, are not systematically included in the evaluation system, resulting in a large deviation between the evaluation results and the actual use. This makes the hot food fast food guarantee system may have unstable performance, cannot meet the actual demand and other problems in actual application, which brings a lot of inconvenience to the users, and even affects the smooth execution of the task in some emergency situations.

[0004] Therefore, how to realize an evaluation device and method for accurately and quickly evaluating the performance of hot food fast food guarantee system is a problem to be solved. SUMMARY

[0005] The present application mainly solves the problem of how to realize an evaluation device and method for accurately and quickly evaluating the performance of hot food fast food guarantee system, and discloses a performance evaluation device and method for hot food fast food guarantee system.

[0006] In a first aspect, a performance evaluation method for a hot food fast food guarantee system is disclosed, comprising:

[0007] S1, Collect historical performance test dataset; the historical performance test dataset includes test condition dataset and experimental test dataset; the test condition dataset includes test condition setting data sequence for each test of the hot food fast food supply system; the experimental test dataset includes experimental test data sequence measured when the hot food fast food supply system is tested according to the test condition setting data sequence; each test condition setting data sequence in the test condition dataset has a corresponding experimental test data sequence; each data point in the test condition setting data sequence in the test condition dataset corresponds to a type of test condition data; the sequence number of each type of test condition data corresponds to the sequence number of the data in the test condition setting data sequence;

[0008] S2, perform estimation processing on the historical performance test dataset to obtain a set of key test condition information;

[0009] S3, under the test conditions corresponding to the key test condition information set, a set of test data sequences is collected; the set of test data sequences includes test test sequences for each type of performance index; the test test sequence is a sequence of values ​​collected during each test.

[0010] S4, perform comprehensive performance evaluation processing on the set of test data sequences to obtain the comprehensive performance evaluation value of the hot food fast food guarantee system.

[0011] The estimation process performed on the historical performance test dataset yields a set of key test condition information, including:

[0012] S21, Perform function modeling on the historical performance test dataset to obtain a multivariate experimental function;

[0013] S22, Differentiate the multivariate experimental function to obtain the derivative matrix;

[0014] S23, perform value discrimination processing on the derivative matrix to obtain a set of key experimental condition information.

[0015] The process of performing function modeling on the historical performance test dataset yields a multivariate experimental function, including:

[0016] S211, using the test condition dataset and the experimental test dataset, respectively, a condition matrix and an experimental matrix are constructed; the row vectors of the condition matrix and the experimental matrix are the experimental condition setting data sequence and the experimental test data sequence, respectively.

[0017] S212, Perform dimension calculation on the condition matrix and the test matrix to obtain the order N of the multivariate test function;

[0018] S213, using the test condition dataset as the multivariate independent variable and the experimental test dataset as the multivariate dependent variable, perform Nth-order polynomial multivariate function fitting on the multivariate independent variable and the multivariate dependent variable to obtain the multivariate experimental function.

[0019] The step of performing dimensionality calculation on the condition matrix and the test matrix to obtain the order N of the multivariate test function includes:

[0020] The rank of the condition matrix is ​​calculated. ;

[0021] The test matrix is ​​processed by eigenvalue calculation to obtain an eigenvalue vector; the eigenvalue vector is a vector constructed from all the eigenvalues ​​of the test matrix.

[0022] The mean of the eigenvalue vector is obtained statistically. and variance ;

[0023] The rank and eigenvalue vector of the condition matrix are processed by a first calculation to obtain the order N of the multivariate experimental function;

[0024] The expression for the first calculation is:

[0025] ,

[0026] in, This indicates rounding up to the nearest integer.

[0027] The eigenvalue calculation process for the test matrix involves first pruning the test matrix to obtain a corresponding square matrix, and then performing eigenvalue calculation on the square matrix. The pruning process involves pruning the matrix into an n×n dimensional square matrix corresponding to the maximum value n in both the row and column dimensions.

[0028] The step of performing value discrimination processing on the derivative matrix to obtain a set of key experimental condition information includes:

[0029] S231, Perform dimension modulus calculation on each column vector of the derivative matrix to obtain the corresponding dimension modulus;

[0030] S232, for each column vector, determine whether its dimension modulus is greater than the set dimension threshold, and obtain the first discrimination result for each column vector;

[0031] S233, Calculate the index values ​​of all column vectors whose first discrimination result is yes;

[0032] S234, using all the statistically obtained sequence values, determine the type of test condition data corresponding to the sequence value; using all the determined types of test condition data, construct a set of key test condition information.

[0033] The comprehensive performance evaluation of the test data sequence set is performed to obtain the comprehensive performance evaluation value of the hot food fast food supply system, including:

[0034] S41, obtain the standard value of each type of performance metric;

[0035] S42, for each type of performance index in the test test data sequence set, subtract the standard value of the corresponding performance index from the test test sequence to obtain the difference sequence corresponding to the performance index of that type;

[0036] S43, using the difference sequences corresponding to all types of performance indicators, construct the difference matrix;

[0037] S44, perform matrix evaluation processing on the difference matrix to obtain the comprehensive performance evaluation value of the hot food fast food guarantee system.

[0038] The expression for the matrix evaluation process is:

[0039] ,

[0040] ,

[0041] ,

[0042] in, Let be the element in the i-th row and j-th column of the difference matrix. Let represent the mean of the i-th row of the difference matrix. and , representing the first and second intermediate evaluation values, respectively, and m and n, representing the row and column dimensions of the difference matrix, respectively. This is a comprehensive performance evaluation value for the hot food and fast food supply system.

[0043] According to a second aspect of the present invention, a performance evaluation device for a hot food fast food delivery system is disclosed, the device comprising:

[0044] Memory containing executable program code;

[0045] A processor coupled to the memory;

[0046] The processor calls the executable program code stored in the memory to execute the performance evaluation method of the hot food fast food guarantee system.

[0047] 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 performance evaluation method of the hot food fast food supply system.

[0048] In a fourth aspect, the present invention discloses an information data processing terminal, which is used to implement the performance evaluation method of the hot food fast food guarantee system.

[0049] The beneficial effects of this invention are as follows:

[0050] First, by comprehensively collecting historical performance test datasets, encompassing rich datasets of test conditions and experimental test data, various complex factors affecting system performance can be fully considered. From environmental conditions to equipment parameter settings, and corresponding performance index test data, a solid foundation is laid for subsequent accurate evaluation of system performance.

[0051] Secondly, when estimating historical performance test datasets, data processing methods such as function modeling, differentiation, and value discrimination are employed to accurately determine the set of key experimental conditions. This allows subsequent evaluations to focus on the most critical factors affecting system performance, significantly improving evaluation efficiency and accuracy.

[0052] Furthermore, by collecting a set of test data sequences under the test conditions corresponding to the key test condition information set, and constructing a difference matrix by comparing it with the standard value, a comprehensive performance evaluation value can be obtained. This evaluation method can comprehensively and objectively reflect the actual performance of the hot food fast food guarantee system.

[0053] Compared with traditional evaluation methods, this invention can more accurately identify the performance shortcomings of the system in different actual scenarios, providing a strong basis for the optimization and improvement of the system. It helps to improve the overall performance of the hot food fast food supply system, ensuring that it can operate stably and efficiently in various complex environments, meeting people's demand for high-quality and reliable hot food fast food supply. It has extremely high practical value in both commercial catering and special emergency scenarios. Attached Figure Description

[0054] Figure 1 This is a flowchart illustrating the implementation of the method of the present invention. Detailed Implementation

[0055] To better understand the content of this invention, an embodiment is provided here.

[0056] Figure 1 This is a flowchart illustrating the implementation of the method of the present invention.

[0057] In a first aspect, the present invention discloses a performance evaluation method for a hot food fast food delivery system, comprising:

[0058] S1, Collect historical performance test dataset; the historical performance test dataset includes test condition dataset and experimental test dataset; the test condition dataset includes test condition setting data sequence for each test of the hot food fast food supply system; the experimental test dataset includes experimental test data sequence measured when the hot food fast food supply system is tested according to the test condition setting data sequence; each test condition setting data sequence in the test condition dataset has a corresponding experimental test data sequence; each data point in the test condition setting data sequence in the test condition dataset corresponds to a type of test condition data; the sequence number of each type of test condition data corresponds to the sequence number of the data in the test condition setting data sequence;

[0059] The test dataset and the corresponding test indicators include staple food processing time, stir-frying time, maximum stir-frying processing volume, frozen storage capacity, and tableware cleaning and disinfection capabilities.

[0060] The test condition setting data sequence includes the values ​​of several types of test condition data;

[0061] The test data sequence includes test values ​​for several types of performance indicators of the hot food fast food supply system;

[0062] S2, perform estimation processing on the historical performance test dataset to obtain a set of key test condition information;

[0063] S3, under the test conditions corresponding to the key test condition information set, a set of test data sequences is collected; the set of test data sequences includes test test sequences for each type of performance index; the test test sequence is a sequence of values ​​collected during each test.

[0064] S4, Perform a comprehensive performance evaluation on the set of test data sequences to obtain the comprehensive performance evaluation value of the hot food fast food guarantee system;

[0065] The estimation process performed on the historical performance test dataset yields a set of key test condition information, including:

[0066] S21, Perform function modeling on the historical performance test dataset to obtain a multivariate experimental function;

[0067] S22, Differentiate the multivariate experimental function to obtain the derivative matrix;

[0068] S23, perform value discrimination processing on the derivative matrix to obtain a set of key experimental condition information;

[0069] The process of performing function modeling on the historical performance test dataset yields a multivariate experimental function, including:

[0070] S211, using the test condition dataset and the experimental test dataset, respectively, a condition matrix and an experimental matrix are constructed; the row vectors of the condition matrix and the experimental matrix are the experimental condition setting data sequence and the experimental test data sequence, respectively.

[0071] S212, Perform dimension calculation on the condition matrix and the test matrix to obtain the order N of the multivariate test function;

[0072] S213, using the test condition dataset as the multivariate independent variable and the experimental test dataset as the multivariate dependent variable, perform Nth-order polynomial multivariate function fitting on the multivariate independent variable and the multivariate dependent variable to obtain the multivariate experimental function.

[0073] The phrase "using the test condition dataset as a multivariate independent variable" means using each type of test condition data in the test condition dataset as a multivariate independent variable, and its value is taken according to the value in the test condition dataset; the phrase "using the experimental test dataset as a multivariate dependent variable" means using the test value of each type of technical indicator in the experimental test dataset as the dependent variable; the Nth-order polynomial multivariate function is a high-order linear multivariate polynomial.

[0074] The elements in the i-th row and j-th column of the derivative matrix are the j-th dependent variable of the multivariate experimental function, i.e., the j-th type of experimental condition data, obtained by taking the partial derivative with respect to the i-th type of independent variable, i.e., the i-th type of performance index test data;

[0075] The type of the multivariate independent variables is the type of experimental condition data;

[0076] The type of the multivariate dependent variable is the type of performance index;

[0077] The step of performing dimensionality calculation on the condition matrix and the test matrix to obtain the order N of the multivariate test function includes:

[0078] The rank of the condition matrix is ​​calculated. ;

[0079] The test matrix is ​​processed by eigenvalue calculation to obtain an eigenvalue vector; the eigenvalue vector is a vector constructed from all the eigenvalues ​​of the test matrix.

[0080] The mean of the eigenvalue vector is obtained statistically. and variance ;

[0081] The rank and eigenvalue vector of the condition matrix are processed by a first calculation to obtain the order N of the multivariate experimental function;

[0082] The expression for the first calculation is:

[0083] ,

[0084] in, This indicates rounding up to the nearest integer.

[0085] The eigenvalue processing can be implemented using a matrix eigenvalue decomposition algorithm;

[0086] The expression processed in the first calculation is used to determine the order of the multivariate experimental function. By combining the rank of the condition matrix with the mean and variance of the eigenvalue vector of the experimental matrix, the order can be matched with the actual characteristics of the experimental data. The rank of the condition matrix reflects the independence of the experimental conditions, while the mean and variance of the eigenvalues ​​of the experimental matrix reflect the concentration and dispersion of the performance index test data. The combined calculation can avoid overfitting caused by an excessively high order (such as the fitting function excessively fitting individual outliers and losing generality), and also avoid underfitting caused by an excessively low order (such as the fitting function failing to reflect the true relationship between the experimental conditions and the performance index). The finally determined order ensures that the subsequently constructed multivariate experimental function not only fits historical test data but also has adaptability to new experimental data, providing a foundation for accurately describing the relationship between experimental conditions and performance indexes.

[0087] The step of performing value discrimination processing on the derivative matrix to obtain a set of key experimental condition information includes:

[0088] S231, Perform dimension modulus calculation on each column vector of the derivative matrix to obtain the corresponding dimension modulus;

[0089] S232, for each column vector, determine whether its dimension modulus is greater than the set dimension threshold, and obtain the first discrimination result for each column vector;

[0090] S233, Calculate the index values ​​of all column vectors whose first discrimination result is yes;

[0091] S234, using all the statistically obtained sequence values, determine the type of test condition data corresponding to the sequence value; using all the determined types of test condition data, construct a set of key test condition information;

[0092] The expression for calculating the dimensional modulus is:

[0093] ,

[0094] in, Let be the element in the i-th row and j-th column of the derivative matrix. Let be the mean of the j-th column of the derivative matrix. Let m be the magnitude of the dimension of the j-th column vector of the derivative matrix, and m be the row dimension of the derivative matrix.

[0095] The expression used for calculating the dimensional modulus is employed to compute the dimensional modulus of the column vectors of the derivative matrix. By quantifying the impact of experimental conditions on performance indicators, it provides an objective basis for selecting key experimental conditions. The column vectors of the derivative matrix correspond to the partial derivatives (i.e., the degree of influence) of a particular experimental condition on all performance indicators. The expression calculates the cumulative deviation of each column vector element from the column mean. A larger modulus indicates a more significant overall impact of the experimental condition on different performance indicators, and vice versa. This quantification method avoids the bias of relying on subjective experience to judge key experimental conditions, ensuring that the selected key experimental conditions truly have a significant impact on the performance of the hot food fast-food supply system (such as staple food processing time and maximum processing volume of stir-fried dishes). Subsequent data collection only under these key conditions reduces invalid experiments and improves evaluation efficiency.

[0096] The comprehensive performance evaluation of the test data sequence set is performed to obtain the comprehensive performance evaluation value of the hot food fast food supply system, including:

[0097] S41, obtain the standard value of each type of performance metric;

[0098] S42, for each type of performance index in the test test data sequence set, subtract the standard value of the corresponding performance index from the test test sequence to obtain the difference sequence corresponding to the performance index of that type;

[0099] S43, using the difference sequences corresponding to all types of performance indicators, construct the difference matrix;

[0100] S44, Perform matrix evaluation processing on the difference matrix to obtain the comprehensive performance evaluation value of the hot food fast food guarantee system;

[0101] The expression for the matrix evaluation process is:

[0102] ,

[0103] ,

[0104] ,

[0105] in, Let be the element in the i-th row and j-th column of the difference matrix. Let represent the mean of the i-th row of the difference matrix. and , representing the first and second intermediate evaluation values, respectively, and m and n, representing the row and column dimensions of the difference matrix, respectively. This is a comprehensive performance evaluation value for the hot food and fast food supply system.

[0106] The expression for the first intermediate evaluation value in the matrix evaluation process is used to calculate the sum of squares of the elements within each row of the difference matrix and the row mean. This accurately captures the fluctuations in test data for individual performance indicators. Each row of the difference matrix corresponds to a performance indicator (such as tableware cleaning and disinfection capability, or frozen storage capacity), and each column corresponds to the difference value (the difference between the test value and the standard value) from a single test. The expression, calculated using the sum of squares, amplifies the impact of large deviations from the mean (i.e., abnormal fluctuation data) and accumulates the degree of fluctuation across all tests for that indicator. This calculation method clearly reflects the stability of each performance indicator. The smaller the value, the closer the test data of the indicator is to its own average difference level, and the more stable the performance; conversely, the larger the value, the more volatile the performance, providing core data support for the stability of a single indicator for subsequent comprehensive evaluation.

[0107] Second Intermediate Assessment Value The calculation expression, through the calculation of the difference matrix row dimension × (column dimension - 1), can eliminate the influence of different number of trials and different numbers of indicators on the comprehensive evaluation value. When the number of trials (column dimension) or the number of performance indicators (row dimension) changes in the evaluation scenario, it can be directly used. This can lead to an inability to make evaluation values ​​comparable across different scenarios (such as scenarios with a large number of trials). It tends to be too large and cannot be compared with scenarios involving fewer occurrences. The normalization factor provided by this expression can... This is converted into a fluctuation value in a single dimension to ensure that the calculated comprehensive performance evaluation value (RSP) has a unified quantitative benchmark regardless of changes in the number of trials or indicators, thus avoiding evaluation bias caused by differences in data dimensions.

[0108] The historical performance test dataset is obtained through historical test results of the hot food fast food supply system, or through digital testing of the hot food fast food supply system using a virtual simulation system.

[0109] In all embodiments of the present invention, the variables involved in all computational expressions or mathematical functions have been dimensionlessized before computation.

[0110] In all embodiments of the present invention, the values ​​of the independent variables in the input of all computational expressions or mathematical functions meet the reasonable requirements of the input range of the computational expressions or mathematical functions, and can ensure that the computational expressions or mathematical functions can be calculated smoothly without violating physical laws or mathematical rules.

[0111] According to a second aspect of the present invention, a performance evaluation device for a hot food fast food delivery system is disclosed, the device comprising:

[0112] Memory containing executable program code;

[0113] A processor coupled to the memory;

[0114] The processor calls the executable program code stored in the memory to execute the performance evaluation method of the hot food fast food guarantee system.

[0115] 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 performance evaluation method of the hot food fast food supply system.

[0116] In a fourth aspect, the present invention discloses an information data processing terminal, which is used to implement the performance evaluation method of the hot food fast food guarantee system.

[0117] 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 performance evaluation method for a hot food fast food delivery system, characterized in that, include: S1, Collect historical performance test dataset; the historical performance test dataset includes test condition dataset and experimental test dataset; The test condition dataset includes a test condition setting data sequence for each test of the hot food fast food supply system; the test test dataset includes a test test data sequence measured when the hot food fast food supply system is tested according to the test condition setting data sequence; each test condition setting data sequence in the test condition dataset has a corresponding test test data sequence. Each data point in the test condition setting data sequence of the test condition dataset corresponds to a type of test condition data; the sequence number of each type of test condition data corresponds to the sequence number of the data in the test condition setting data sequence. S2, perform estimation processing on the historical performance test dataset to obtain a set of key test condition information, including: S21, Perform function modeling on the historical performance test dataset to obtain a multivariate experimental function; S22, Differentiate the multivariate experimental function to obtain the derivative matrix; S23, perform value discrimination processing on the derivative matrix to obtain a set of key experimental condition information, including: S231, Perform dimension modulus calculation on each column vector of the derivative matrix to obtain the corresponding dimension modulus; S232, for each column vector, determine whether its dimension modulus is greater than the set dimension threshold, and obtain the first discrimination result for each column vector; S233, Calculate the index values ​​of all column vectors whose first discrimination result is yes; S234, using all the statistically obtained sequence values, determine the type of test condition data corresponding to the sequence value; using all the determined types of test condition data, construct a set of key test condition information; S3, under the test conditions corresponding to the key test condition information set, a set of test data sequences is collected; the set of test data sequences includes test test sequences for each type of performance index; the test test sequence is a sequence of values ​​collected during each test. S4, perform comprehensive performance evaluation processing on the test data sequence set to obtain the comprehensive performance evaluation value of the hot food fast food supply system, including: S41, obtain the standard value of each type of performance metric; S42, for each type of performance index in the test test data sequence set, subtract the standard value of the corresponding performance index from the test test sequence to obtain the difference sequence corresponding to the performance index of that type; S43, using the difference sequences corresponding to all types of performance indicators, construct the difference matrix; S44, Perform matrix evaluation processing on the difference matrix to obtain the comprehensive performance evaluation value of the hot food fast food guarantee system; The expression for the matrix evaluation process is: ， ， ， in, Let be the element in the i-th row and j-th column of the difference matrix. Let represent the mean of the i-th row of the difference matrix. and , representing the first and second intermediate evaluation values, respectively, and m and n, representing the row and column dimensions of the difference matrix, respectively. This is a comprehensive performance evaluation value for the hot food and fast food supply system.

2. The performance evaluation method for the hot food fast food delivery system as described in claim 1, characterized in that, The process of performing function modeling on the historical performance test dataset yields a multivariate experimental function, including: S211, using the test condition dataset and the experimental test dataset, respectively, a condition matrix and an experimental matrix are constructed; the row vectors of the condition matrix and the experimental matrix are the experimental condition setting data sequence and the experimental test data sequence, respectively. S212, Perform dimension calculation on the condition matrix and the test matrix to obtain the order N of the multivariate test function; S213, using the test condition dataset as the multivariate independent variable and the experimental test dataset as the multivariate dependent variable, perform Nth-order polynomial multivariate function fitting on the multivariate independent variable and the multivariate dependent variable to obtain the multivariate experimental function.

3. The performance evaluation method for the hot food fast food delivery system as described in claim 2, characterized in that, The step of performing dimensionality calculation on the condition matrix and the test matrix to obtain the order N of the multivariate test function includes: The rank of the condition matrix is ​​calculated. ; The test matrix is ​​processed by eigenvalue calculation to obtain an eigenvalue vector; the eigenvalue vector is a vector constructed from all the eigenvalues ​​of the test matrix. The mean of the eigenvalue vector is obtained statistically. and variance ; The rank and eigenvalue vector of the condition matrix are processed by a first calculation to obtain the order N of the multivariate experimental function; The expression for the first calculation is: ， in, This indicates rounding up to the nearest integer.

4. A performance evaluation device for a hot food fast food delivery system, 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 performance evaluation method of the hot food fast food guarantee system as described in any one of claims 1 to 3.

5. 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 performance evaluation method of the hot food fast food guarantee system as described in any one of claims 1 to 3.

6. An information data processing terminal, characterized in that, The information data processing terminal is used to implement the performance evaluation method of the hot food fast food guarantee system as described in any one of claims 1 to 3.

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