A pathological data analysis method based on multiple variables

Abstract

The present invention provides a method for pathological data analysis based on multiple variables, which relates to the technical field of pathological data processing. The method includes the following steps: obtaining a storage quality index; obtaining preprocessed pathological data; obtaining a preprocessing quality index; obtaining a comprehensive evaluation index. By transmitting the obtained initial pathological data to a preset storage location to obtain a storage quality index, then preprocessing the initial pathological data to obtain preprocessed pathological data and further obtaining a preprocessing quality index, and finally obtaining a comprehensive evaluation index according to the storage quality index and the preprocessing quality index, the present invention achieves the effect of improving the accuracy of pathological data analysis, and solves the problem of low accuracy of pathological data analysis in the existing technology during the process of pathological data analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of pathological data processing for brain diseases, and particularly to a pathological data analysis method based on multivariate analysis. Background Art

[0002] With the rapid development of mass spectrometry technology, bioinformatics, and computer technology, pathological data analysis methods based on multivariate analysis have also been continuously improving. However, at the same time, some challenges are also faced. For example, multivariate analysis involves a relatively large amount of mathematical knowledge and high computational complexity, and it requires the assistance of professional statistical software and computer equipment for processing; in addition, the collection and collation of pathological data also require a large amount of human and material resources. Multivariate analysis, also known as multivariate analysis, is a statistical analysis method for studying multi-factor and multi-index problems. In the medical field, especially in pathological data analysis, multivariate analysis plays a crucial role. Since the pathological process is often affected by multiple factors and there may be complex interactions between these factors, multivariate analysis can more comprehensively and deeply reveal the essence and laws of pathological phenomena. Pathological data analysis is an important means to improve the accuracy of pathological diagnosis and clinical treatment effects. By collecting and collating pathological specimens, clinical data, and relevant reports, a large dataset can be established, and these datasets contain rich pathological information. In-depth analysis of these data helps medical staff to timely discover problems in pathological diagnosis, optimize clinical treatment plans, and thus improve the quality of medical care.

[0003] Existing methods use methods such as cluster analysis to classify pathological samples and use methods such as regression analysis to establish prediction models, which helps to discover the pathological characteristics of different disease types and can predict the progression trend of diseases.

[0004] For example, the pathological data processing method, device and electronic device announced in the invention patent announcement with the announcement number CN110517747B include: obtaining multiple pathological reports to be processed; preprocessing the multiple pathological reports to be processed to obtain multiple pathological corpus data; performing vector conversion on the multiple pathological corpus data to obtain a pathological matrix of the multiple pathological reports to be processed; performing dimensionality reduction processing on the pathological matrix to determine a pathological feature matrix corresponding to the multiple pathological reports to be processed, where each row vector in the pathological feature matrix represents the feature vector of a pathological report to be processed; performing vector conversion on the multiple pathological corpus data to obtain a pathological matrix of the multiple pathological reports to be processed, including: for each pathological corpus data among the multiple pathological corpus data, performing vector conversion on each word in each pathological corpus data using the bag-of-words model to obtain the word vector of each word in this pathological corpus data; calculating the average value of all the word vectors in this pathological corpus data to obtain the pathological vector of this pathological corpus data; the multiple pathological vectors corresponding to the multiple pathological corpus data form a first pathological matrix; for each pathological corpus data among the multiple pathological corpus data, performing vector conversion on each word in each pathological corpus data using the bag-of-words model to obtain the word vector of each word in this pathological corpus data; determining the weight corresponding to each word vector according to the multiple pathological reports to be processed; performing weighted summation on all the word vectors in this pathological corpus data to obtain the pathological vector of this pathological corpus data; the multiple pathological vectors corresponding to the multiple pathological corpus data form a second pathological matrix; for each pathological corpus data among the multiple pathological corpus data, performing vector conversion on each pathological corpus data using the bag-of-words model to obtain the sentence vector of this pathological corpus data; the multiple sentence vectors corresponding to the multiple pathological corpus data form a third pathological matrix; performing weighted summation on the first pathological matrix, the second pathological matrix and the third pathological matrix to obtain the pathological matrix.

[0005] For example, the processing system for the data stream of digital pathological images announced in the invention patent announcement with the announcement number CN114038541B includes: an acquisition unit for acquiring digital pathological images; a first storage unit for reading the metadata of the digital pathological images, performing byte-level chunking on the digital pathological images based on the metadata in byte order to obtain multiple sub-chunks including multiple image sub-slices of adjacent regions, and then storing the multiple sub-chunks and generating sub-slice information for each image sub-slice; a metadata unit for recording the metadata and sub-slice information; a parsing unit for, after receiving a data request for acquiring a data stream, acquiring the data stream corresponding to at least one image sub-slice at a time in a manner of sharing the handle of the opened sub-chunk based on the metadata, the byte offset and the byte size of the image sub-slice.

[0006] However, in the process of implementing the technical solution of the present invention in the embodiments of the present invention, it is found that the above technologies have at least the following technical problems:

[0007] In the prior art, due to problems such as tissue being squeezed or burned, the sampling of pathological samples is not accurate enough, resulting in low accuracy of pathological data analysis. Summary of the Invention

[0008] By providing a pathological data analysis method based on multiple variables in an embodiment of the present invention, the problem of low accuracy of pathological data analysis in the prior art is solved, and the improvement of the accuracy of pathological data analysis is realized.

[0009] An embodiment of the present invention provides a pathological data analysis method based on multiple variables, including the following steps:

[0010] S1. Transmit the initial pathological data obtained according to the preset pathological data to a preset storage location, and obtain a storage quality index according to the initial pathological data, where the storage quality index is used to evaluate the integrity of the initial pathological data stored in the preset storage location;

[0011] S2. Preprocess the initial pathological data to obtain preprocessed pathological data, where the preprocessing is used to ensure that the initial pathological data meets the storage integrity requirements;

[0012] S3. Obtain a preprocessing quality index according to the preprocessed pathological data, where the preprocessing quality index includes a noise elimination quality index and a noise improvement quality index, the noise elimination quality index is used to evaluate the error level in the preprocessing process of the initial pathological data, and the noise improvement quality index is used to evaluate the improvement degree of the quality of the initial pathological data;

[0013] S4. Obtain a comprehensive evaluation index according to the storage quality index and the preprocessing quality index, where the comprehensive evaluation index is used to reflect the overall quality of the pathological data.

[0014] Optionally, the specific process for obtaining the storage quality index is as follows: Obtain storage data through a storage measurement device during a preset storage period, where the storage data includes a storage rate measurement value and a transmission rate measurement value, and the storage measurement device includes a network protocol analyzer and a timer; Obtain the storage rate measurement value according to the standard storage capacity and the standard storage time, and at the same time obtain the transmission rate measurement value according to the standard transmission data volume and the standard transmission time; Obtain a preset storage data group from a preset database, and combine the storage data to obtain the storage quality index, where the preset storage data group includes a storage rate threshold, a transmission rate threshold, a preset first storage weight, and a preset second storage weight.

[0015] Optionally, the storage quality index is calculated using the following formula:

[0016]

[0017] In the formula, denotes the storage quality index for the m-th storage preset time period, where m = 1, 2,..., n. Here, m represents the serial number of the storage preset time period, and n represents the total number of storage preset time periods, V 1m denotes the storage rate measurement value for the m-th storage preset time period, V 2m denotes the transmission rate measurement value for the m-th storage preset time period. V0 represents the storage rate threshold, V1 represents the transmission rate threshold, δ1 represents the preset first storage weight, and δ2 represents the preset second storage weight.

[0018] Optionally, the specific process for obtaining the preprocessed pathological data is as follows: Compare the storage quality index with the preset storage quality range to obtain the first preprocessed data, where the first preprocessed data represents the initial pathological data corresponding to the storage quality index within the preset storage quality range; perform missing value processing on the first preprocessed data to obtain the second preprocessed data; perform normalization processing on the second preprocessed data to obtain the preprocessed pathological data.

[0019] Optionally, the specific process for obtaining the noise cancellation quality index is as follows: Obtain the noise temperature data through a preprocessing measurement device during the preprocessing preset time period. The noise temperature data includes the standard noise power, standard bandwidth, and standard coefficient. The preprocessing measurement device includes a power meter and a spectrum analyzer; obtain the noise temperature measurement value based on the standard noise power, standard bandwidth, and standard coefficient; obtain the preset noise temperature threshold from the preset database, and combine it with the noise temperature measurement value to obtain the noise cancellation quality index.

[0020] Optionally, the noise cancellation quality index is calculated using the following formula:

[0021]

[0022] In the formula, denotes the noise cancellation quality index for the s-th preprocessing preset time period, where s = 1, 2,..., t. Here, s represents the serial number of the preprocessing preset time period, and t represents the total number of preprocessing preset time periods, T S denotes the noise temperature measurement value for the s-th preprocessing preset time period, T0 represents the preset noise temperature threshold, and e represents the natural constant.

[0023] Optionally, the specific process for obtaining the noise improvement quality index is as follows: Obtain the signal-to-noise ratio measurement value through a preprocessing measurement device during the preprocessing preset time period; obtain the signal-to-noise ratio measurement value based on the standard signal power and standard noise power; obtain the preset signal-to-noise ratio threshold from the preset database, and combine it with the signal-to-noise ratio measurement value to obtain the noise improvement quality index.

[0024] Optionally, the noise improvement quality index is calculated using the following formula:

[0025]

[0026] In the formula, represents the noise improvement quality index in the s-th preprocessing preset time period, s = 1, 2,..., t, s represents the number of the preprocessing preset time period, t represents the total number of the preprocessing preset time periods, D s represents the signal-to-noise ratio measurement value in the s-th preprocessing preset time period, D0 represents the preset signal-to-noise ratio threshold, and e represents the natural constant.

[0027] Optionally, the specific process of obtaining the comprehensive evaluation index is as follows: Obtain the first pathological data analysis weight, the second pathological data analysis weight, and the third pathological data analysis weight from the preset database; obtain the comprehensive evaluation index according to the first pathological data analysis weight, the second pathological data analysis weight, the third pathological data analysis weight, the storage quality index, the noise elimination quality index, and the noise improvement quality index.

[0028] One or more technical solutions provided in the embodiments of the present invention have at least the following technical effects or advantages:

[0029] (1) By transmitting the initial pathological data obtained according to the preset pathological data to the preset storage location, obtaining the storage quality index according to the initial pathological data, then preprocessing the initial pathological data to obtain the preprocessed pathological data, then obtaining the preprocessing quality index according to the preprocessed pathological data, and finally obtaining the comprehensive evaluation index according to the storage quality index and the preprocessing quality index, the quality of the pathological data is comprehensively evaluated, and then the accuracy of the pathological data analysis is improved, effectively solving the problem of low accuracy of pathological data analysis in the prior art.

[0030] (2) By obtaining the preset pathological data from the preset database, then performing the first screening on the data to be detected according to the preset pathological data to obtain the first data, then performing the second screening on the data to be detected according to the preset imaging data to obtain the second data, and finally performing the second screening on the data to be detected according to the preset biological data to obtain the third data, the reliability of the obtained pathological data is improved, and then the quality of the obtained pathological data is improved.

[0031] (3) By obtaining the comprehensive evaluation index according to the first pathological data analysis weight, the second pathological data analysis weight, the third pathological data analysis weight, the storage quality index, the noise elimination quality index, and the noise improvement quality index, the quality of the pathological data is improved in multiple aspects, and then the comprehensiveness of the improvement of the pathological data quality is realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1Flowchart of the pathological data analysis method based on multiple variables provided by the embodiments of the present invention;

[0033] Figure 2 Variation statistical chart of the stored quality index provided by the embodiments of the present invention. Detailed implementation manners

[0034] The embodiments of the present invention provide a pathological data analysis method based on multiple variables, which solves the problem of low accuracy in pathological data analysis in the prior art. By transmitting the obtained initial pathological data to a preset storage location, obtaining a storage quality index according to the initial pathological data, then preprocessing the initial pathological data to obtain preprocessed pathological data, and then obtaining a preprocessing quality index according to the preprocessed pathological data, and finally obtaining a comprehensive evaluation index according to the storage quality index and the preprocessing quality index, the accuracy of pathological data analysis is improved.

[0035] The technical solution in the embodiments of the present invention is to solve the problem of low quality of pathological data in the process of pathological data analysis. The general idea is as follows:

[0036] By transmitting the obtained initial pathological data to a preset storage location and obtaining a storage quality index, then preprocessing the initial pathological data to obtain preprocessed pathological data and further obtaining a preprocessing quality index, and finally obtaining a comprehensive evaluation index according to the storage quality index and the preprocessing quality index, the accuracy of pathological data analysis is improved.

[0037] To better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings of the specification and specific implementation manners.

[0038] As Figure 1 shown, it is a flowchart of the pathological data analysis method based on multiple variables provided by the embodiments of the present invention. The method includes the following steps:

[0039] S1. Transmit the initial pathological data obtained according to the preset pathological data to a preset storage location, and obtain a storage quality index according to the initial pathological data. The storage quality index is used to evaluate the integrity of the initial pathological data stored in the preset storage location.

[0040] S2. Preprocess the initial pathological data to obtain preprocessed pathological data. The preprocessing is used to ensure that the initial pathological data meets the storage integrity requirements. The preprocessed pathological data represents the initial pathological data after preprocessing.

[0041] S3. Obtain a preprocessing quality index based on the preprocessed pathological data. The preprocessing quality index includes a noise elimination quality index and a noise improvement quality index. The noise elimination quality index is used to evaluate the error level in the preprocessing process of the initial pathological data, and the noise improvement quality index is used to evaluate the degree of improvement in the quality of the initial pathological data.

[0042] S4. Obtain a comprehensive evaluation index based on the storage quality index and the preprocessing quality index. The comprehensive evaluation index is used to reflect the overall quality of the pathological data.

[0043] In this embodiment, the storage quality index directly reflects the integrity and reliability of the initial pathological data at the preset storage location, and is the basis for ensuring the smooth progress of subsequent data processing flows. The noise elimination quality index evaluates the ability to eliminate noise and errors in the preprocessing process. A high noise elimination quality index means that the preprocessing process effectively reduces the noise and errors in the data, thereby improving the purity and accuracy of the data. The noise improvement quality index further evaluates the overall improvement degree of the data quality after preprocessing. By optimizing the preprocessing process, the overall quality of the pathological data can be significantly improved. There is a relationship of mutual influence between the storage quality index and the preprocessing quality index. The level of storage quality directly affects the quality of the input data in the preprocessing process, and thus affects the preprocessing quality index. Obtain preset pathological data from a preset database. The preset pathological data includes preset pathological data, preset imaging data, and preset biological data. Perform a first screening on the data to be detected according to the preset pathological data to obtain the first data. Perform a second screening on the data to be detected according to the preset imaging data to obtain the second data. Perform a third screening on the data to be detected according to the preset biological data to obtain the third data. The third data represents the data to be detected that conforms to the preset biological data. The preset pathological data includes pathological diagnostic criteria for hemorrhagic shock and anemia, a pathological image library, a pathological report template, facial recognition, etc., which are crucial for judging whether there are pathological changes, lesion types, and critical conditions in the sample to be detected. The preset imaging data includes typical imaging manifestations of diseases, imaging diagnostic criteria, an imaging image library, etc., which are of great significance for detecting, locating, and evaluating the lesion site, size, shape, blood supply, etc. The preset biological data includes gene sequences, single nucleotide polymorphism data, protein expression profiles, metabolite profiles, etc., which play an important role in revealing the pathogenesis of diseases, predicting disease risks, and evaluating treatment effects. The preset pathological data uses the preset pathological, imaging, and biological data as screening criteria, which can exclude the data to be detected that do not meet the standards, thereby improving the accuracy and reliability of the finally screened initial pathological data; achieving an improvement in the accuracy of pathological data analysis.

[0044] Optionally, the specific process for obtaining the storage quality index is as follows: Obtain storage data through a storage measurement device during a preset storage period. The storage data includes a storage rate measurement value and a transmission rate measurement value. The storage measurement device includes a network protocol analyzer and a timer. The preset storage period represents the preset period during the transmission and storage of the initial pathological data; Obtain the storage rate measurement value based on the standard storage capacity and the standard storage time, and at the same time obtain the transmission rate measurement value based on the standard transmission data volume and the standard transmission time. The standard storage capacity represents the storage capacity of the initial pathological data during the preset storage period, the standard storage time represents the time required to store the data of the initial pathological data during the preset storage period, the standard transmission data volume represents the data volume transmitted by the initial pathological data during the preset storage period, and the standard transmission time represents the time required to transmit the data of the initial pathological data during the preset storage period; Obtain a preset storage data group from a preset database, and combine the storage data to obtain the storage quality index. The preset storage data group includes a storage rate threshold, a transmission rate threshold, a preset first storage weight, and a preset second storage weight. The preset first storage weight is used to evaluate the influence degree of the storage rate measurement value on the storage quality index, and the preset second storage weight is used to evaluate the influence degree of the transmission rate measurement value on the storage quality index.

[0045] In this embodiment, the storage rate threshold is represented by the mean value of the storage rate data in the preset database, the transmission rate threshold is represented by the mean value of the transmission rate data in the preset database. The timer is a device for measuring time, which can be accurate to seconds, milliseconds, or even shorter time units. The network protocol analyzer is a dedicated test tool for monitoring the data flow in a data communication system and verifying whether the data exchange is carried out correctly according to the protocol regulations. By capturing packets, the network traffic can be analyzed in detail to understand the data transmission situation on the network; The accuracy of pathological data analysis is improved.

[0046] Specifically, the preset first storage weight is obtained from the preset database. In a specific embodiment, a mapping set of the storage capacity and storage time and their corresponding weights is constructed based on the relationship between the storage capacity and storage time and the corresponding storage quality index during the historical period, and the real-time storage capacity and storage time are input into the mapping set to obtain the corresponding preset first storage weight.

[0047] Specifically, the preset second storage weight is obtained from the preset database. In a specific embodiment, a mapping set of the transmission data volume and transmission time and their corresponding weights is constructed based on the relationship between the transmission data volume and transmission time and the corresponding storage quality index during the historical period, and the real-time transmission data volume and transmission time are input into the mapping set to obtain the corresponding preset second storage weight.

[0048] Optionally, the storage quality index is calculated using the following formula:

[0049]

[0050] In the formula, represents the storage quality index in the m-th storage preset time period, where m = 1, 2,..., n, m represents the number of the storage preset time period, and n represents the total number of storage preset time periods. V 1m represents the measured storage rate in the m-th storage preset time period. V 2m represents the measured transmission rate in the m-th storage preset time period. V0 represents the storage rate threshold, V1 represents the transmission rate threshold, δ1 represents the preset first storage weight, and δ2 represents the preset second storage weight.

[0051] In this embodiment, C m represents the standard storage capacity in the m-th storage preset time period, and t 1m represents the standard storage time in the m-th storage preset time period. X m represents the standard transmission data volume in the m-th storage preset time period, and t 2m represents the standard transmission time in the m-th storage preset time period.

[0052] Specifically, the algorithm of this embodiment combines the standard storage capacity C m , the standard storage time t 1m , the standard transmission data volume X m and the standard transmission time t 2m , and comprehensively analyzes to obtain the storage quality index. In this formula, the standard storage capacity C m , the standard storage time t 1m , the standard transmission data volume X m and the standard transmission time t 2m have a common adjustment relationship. As the standard storage capacity C m and the standard transmission data volume X m increase, the standard storage time t 1m and the standard transmission time t 2m decrease, and the storage quality index gradually increases. At this time, the standard storage capacity C m and the standard transmission data volume X m are positively correlated with the storage quality index, and the standard storage time t 1m and the standard transmission time t 2m are negatively correlated with the storage quality index. Therefore, precise analysis is carried out by establishing a mathematical form.

[0053] Specifically, as Figure 2 shown, it is the change statistical chart of the storage quality index provided by the embodiment of the present invention. Assume that the measured storage rate is V 1mranges from 100 - 500 (MB / s), and the measured transmission rate V 2m ranges from 500 - 1000 (MB / s), the storage rate threshold V0 is 200 (MB / s), the transmission rate threshold V1 is 300 (MB / s), the preset first storage weight δ1 is 0.5, the preset second storage weight δ2 is 0.5. As can be seen from the figure, as the measured storage rate V 1m and the measured transmission rate V 2m gradually increase, the storage quality index gradually increases, which means that the integrity of the initial pathological data stored at the preset storage location is relatively good, and the initial pathological data is stored more qualitatively and reliably at the preset storage location. The high storage rate ensures that the data is completely and without omission recorded in a short time, while the high transmission rate guarantees that the data can be transmitted to the target location quickly and accurately, reducing the risk of data loss caused by delay or interruption; improving the accuracy of pathological data analysis is achieved.

[0054] Optionally, the specific acquisition process of the preprocessed pathological data is as follows: comparing the storage quality index with the preset storage quality range to obtain the first preprocessed data, where the first preprocessed data represents the initial pathological data corresponding to the storage quality index within the preset storage quality range; performing missing value processing on the first preprocessed data to obtain the second preprocessed data, and the missing value processing means filling the missing values with the median of the first preprocessed data, and the second preprocessed data represents the initial pathological data after missing value processing; performing normalization processing on the second preprocessed data to obtain the preprocessed pathological data, and the normalization processing is used to eliminate the influence of different dimensions.

[0055] In this embodiment, the preset storage quality range is set based on the pathological data in the preset database, the preset storage quality is represented by the mean value of the storage quality data in the preset database, and the storage quality is divided into different levels according to the preset storage quality threshold, such as "excellent", "good", "general", "poor", etc., and each level corresponds to a specific storage quality range. The normalization processing eliminates the influence of different dimensions on data analysis, enabling features of different magnitudes to be compared and calculated on the same scale. By steps such as screening, filling missing values, and normalization, the quality and usability of the data are effectively improved; improving the accuracy of pathological data analysis is achieved.

[0056] Optionally, the specific process of obtaining the noise cancellation quality index is as follows: During a preprocessing preset time period, noise temperature data is obtained through a preprocessing measurement device. The noise temperature data includes standard noise power, standard bandwidth, and a standard coefficient. The preprocessing measurement device includes a power meter and a spectrum analyzer; based on the standard noise power, standard bandwidth, and standard coefficient, a noise temperature measurement value is obtained. The preprocessing preset time period represents the preset time period during the preprocessing of the initial pathological data; the standard noise power represents the power of the noise of the initial pathological data during the preprocessing preset time period, the standard bandwidth represents the spectral bandwidth of the noise of the initial pathological data during the preprocessing preset time period, and the standard coefficient is obtained from a preset database; a preset noise temperature threshold is obtained from the preset database, and the noise cancellation quality index is obtained in combination with the noise temperature measurement value.

[0057] In this embodiment, the preset noise temperature threshold is represented by the mean value of the noise temperature data in the preset database. The working principle of the power meter is based on the law of electric energy conversion and energy conservation in the power system. Specifically, the power meter measures the voltage and current values in the circuit and uses Ohm's law and the instantaneous power formula to calculate the power value. The power meter measures the voltage and current values in the circuit and calculates the power value using relevant formulas. The spectrum analyzer is an instrument for studying the spectrum structure of electrical signals and is used for measuring signal parameters such as signal distortion, modulation degree, spectral purity, frequency stability, and intermodulation distortion; the accuracy of pathological data analysis is improved.

[0058] Optionally, the noise cancellation quality index is calculated using the following formula:

[0059]

[0060] In the formula, represents the noise cancellation quality index in the s-th preprocessing preset time period, s = 1, 2,..., t. s represents the number of the preprocessing preset time period, and t represents the total number of preprocessing preset time periods. T S represents the noise temperature measurement value in the s-th preprocessing preset time period, T0 represents the preset noise temperature threshold, and e represents the natural constant.

[0061] In this embodiment, P s represents the standard noise power in the s-th preprocessing preset time period, B s represents the standard bandwidth in the s-th preprocessing preset time period, k represents the standard coefficient. The algorithm of this embodiment combines the standard noise power P s and the standard bandwidth B s , and comprehensively analyzes to obtain the noise cancellation quality index. In this formula, the standard noise power P s and the standard bandwidth B sThere is a co-regulation relationship between them. As the standard noise power P s increases, the standard bandwidth B s decreases, and the noise cancellation quality index gradually decreases. At this time, the standard noise power P s is negatively correlated with the noise cancellation quality index, and the standard bandwidth B s is positively correlated with the noise cancellation quality index. Therefore, precise analysis is carried out by establishing a mathematical form.

[0062] Specifically, assume that the range of the standard noise power P s is 0 - 100 (nW), the range of the standard bandwidth B s is 1 - 500 (Hz), the standard coefficient k is 1 (nW / (Hz*K)), and the preset noise temperature threshold T0 is 50 (K). Then the statistical change table of the noise cancellation quality index is shown in Table 1:

[0063] Table 1 Statistical change table of the noise cancellation quality index

[0064]

[0065] As can be seen from the table, as the standard noise power P s gradually increases and the standard bandwidth B s gradually decreases, the noise cancellation quality index gradually decreases. This means that while removing noise, more useful information in the initial pathological data is retained, and the interference of noise is effectively suppressed or eliminated. The error level in the preprocessing process of the initial pathological data gradually decreases, reflecting the improvement of the purity and accuracy of the data after preprocessing; the accuracy of pathological data analysis is improved.

[0066] Optionally, the specific acquisition process of the noise improvement quality index is as follows: Obtain the signal-to-noise ratio measurement value through a preprocessing measurement device during a preset preprocessing time period; obtain the signal-to-noise ratio measurement value according to the standard signal power and the standard noise power, where the standard signal power represents the power of the useful signal of the initial pathological data during the preset preprocessing time period; obtain the preset signal-to-noise ratio threshold from a preset database and combine it with the signal-to-noise ratio measurement value to obtain the noise improvement quality index.

[0067] In this embodiment, the preset signal-to-noise ratio threshold is represented by the mean value of the signal-to-noise ratio data in the preset database. In all links of signal processing, ensuring signal quality is crucial. The noise improvement quality index, as an indicator to measure the noise improvement effect, helps to timely detect and correct noise problems during signal processing, thus ensuring the quality of the final signal; the accuracy of pathological data analysis is improved.

[0068] Optionally, the noise improvement quality index is calculated using the following formula:

[0069]

[0070] In the formula, represents the noise improvement quality index in the s-th preprocessing preset time period, where s = 1, 2,..., t, s represents the number of the preprocessing preset time period, and t represents the total number of preprocessing preset time periods, D s represents the signal-to-noise ratio measurement value in the s-th preprocessing preset time period, D0 represents the preset signal-to-noise ratio threshold, and e represents the natural constant.

[0071] In this embodiment, P 0S represents the standard signal power in the s-th preprocessing preset time period, P s represents the standard noise power in the s-th preprocessing preset time period. The algorithm of this embodiment combines the standard signal power P 0S and the standard noise power P s , and comprehensively analyzes to obtain the noise improvement quality index. In this formula, the standard signal power P 0S and the standard noise power P s have a common adjustment relationship. As the standard signal power P 0S increases and the standard noise power P s decreases, the noise improvement quality index gradually increases. At this time, the standard signal power P 0S is positively correlated with the noise improvement quality index, and the standard noise power P s is negatively correlated with the noise improvement quality index. Therefore, precise analysis is carried out by establishing a mathematical form;

[0072] It should be noted that the expression of the standard signal power P 0S is: Among them, P 0S ′ represents the standard signal power data in the s-th preprocessing preset time period, P 0S 0 represents the reference standard signal power data in the s-th preprocessing preset time period, and ΔP 0S represents the reference deviation of the standard signal power. The expression of the standard noise power P s is: Among them, P s ′ represents the standard noise power data in the s-th preprocessing preset time period, P s 0 represents the reference standard noise power data in the s-th preprocessing preset time period, and ΔP s represents the reference deviation of the standard noise power.

[0073] It should be understood that the standard signal power data of the s-th preprocessing preset time period is obtained by measurement, the reference standard signal power data is represented by the result of calculating the average value of the standard signal power data of the s-th preprocessing preset time period, the standard noise power reference deviation is obtained by calculating the standard signal power data and the reference standard signal power data through the deviation formula, the standard noise power data of the s-th preprocessing preset time period is obtained by measurement, the reference standard noise power data is represented by the result of calculating the average value of the standard noise power data of the s-th preprocessing preset time period, the standard noise power reference deviation is obtained by calculating the standard noise power data and the reference standard noise power data through the deviation formula, and the deviation formula is used to measure the difference between the reference value and the true measurement value, and is represented by the result of taking the average value of the difference between the reference value and the true measurement value; the accuracy of pathological data analysis is improved.

[0074] Optionally, the specific acquisition process of the comprehensive evaluation index is as follows: obtain the first pathological data analysis weight, the second pathological data analysis weight, and the third pathological data analysis weight from the preset database. The first pathological data analysis weight is used to evaluate the influence degree of the storage quality index on the comprehensive evaluation index, the second pathological data analysis weight is used to evaluate the influence degree of the noise elimination quality index on the comprehensive evaluation index, and the third pathological data analysis weight is used to evaluate the influence degree of the noise improvement quality index on the comprehensive evaluation index; obtain the comprehensive evaluation index according to the first pathological data analysis weight, the second pathological data analysis weight, the third pathological data analysis weight, the storage quality index, the noise elimination quality index, and the noise improvement quality index.

[0075] In this embodiment, the comprehensive evaluation index is calculated based on the storage quality index and the preprocessing quality index, which comprehensively reflects the overall quality of the pathological data. By comprehensively considering the quality evaluation results of the storage and preprocessing links, the comprehensive evaluation index provides a comprehensive evaluation of the overall quality of the pathological data; the accuracy of pathological data analysis is improved.

[0076] Specifically, the comprehensive evaluation index is calculated using the following formula:

[0077]

[0078] In the formula, τ represents the comprehensive evaluation index, represents the storage quality index in the m-th storage preset time period, m = 1, 2,..., n, m represents the number of the storage preset time period, and n represents the total number of the storage preset time periods, represents the noise elimination quality index in the s-th preprocessing preset time period, s = 1, 2,..., t, s represents the number of the preprocessing preset time period, and t represents the total number of the preprocessing preset time periods, Denote the noise improvement quality index in the s-th preprocessing preset time period, θ1 denote the first pathological data analysis weight, θ2 denote the second pathological data analysis weight, and θ3 denote the third pathological data analysis weight.

[0079] Specifically, the first pathological data analysis weight is obtained from a preset database; in a specific embodiment, a mapping set of the storage rate and transmission rate and their corresponding weights is constructed based on the relationship between the storage rate, transmission rate and the corresponding storage quality index in the historical period, and the real-time storage rate and transmission rate are input into the mapping set to obtain the corresponding first pathological data analysis weight.

[0080] Specifically, the second pathological data analysis weight is obtained from a preset database; in a specific embodiment, a mapping set of the noise power and bandwidth and their corresponding weights is constructed based on the relationship between the noise power, bandwidth and the corresponding noise cancellation quality index in the historical period, and the real-time noise power and bandwidth are input into the mapping set to obtain the corresponding second pathological data analysis weight.

[0081] Specifically, the third pathological data analysis weight is obtained from a preset database; in a specific embodiment, a mapping set of the signal power and noise power and their corresponding weights is constructed based on the relationship between the signal power, noise power and the corresponding noise improvement quality index in the historical period, and the real-time signal power and noise power are input into the mapping set to obtain the corresponding third pathological data analysis weight.

[0082] In summary, in the embodiment of the present invention, the initial pathological data obtained according to the preset pathological data is transmitted to a preset storage location, the storage quality index is obtained according to the initial pathological data, then the initial pathological data is preprocessed to obtain preprocessed pathological data, then the preprocessing quality index is obtained according to the preprocessed pathological data, and finally the comprehensive evaluation index is obtained according to the storage quality index and the preprocessing quality index, thereby realizing the comprehensive evaluation of the quality of pathological data, and further realizing the improvement of the accuracy of pathological data analysis, effectively solving the problem of low accuracy of pathological data analysis in the prior art.

[0083] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0084] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices produce a means for implementing the functions specified in one flow Figure 1 one flow or more flows and / or blocks Figure 1 or a means for implementing the functions specified in one block or more blocks.

[0085] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including an instruction means that implements the functions specified in one flow Figure 1 one flow or more flows and / or blocks Figure 1 or a means for implementing the functions specified in one block or more blocks.

[0086] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or more flows and / or blocks Figure 1 or a means for implementing the functions specified in one block or more blocks.

[0087] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they know the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0088] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these changes and modifications.

Claims

1. A multivariate-based pathological data analysis method, characterized in that: The following steps are involved: S1, transmitting initial pathology data acquired according to preset pathology data to a preset storage location, and acquiring a storage quality index according to the initial pathology data, wherein the storage quality index is used to evaluate the integrity of the initial pathology data stored in the preset storage location; The specific process of obtaining the storage quality index is as follows: Acquiring storage data by means of a storage measurement device during a preset storage time period, wherein the storage data includes a storage rate measurement value and a transmission rate measurement value, and the storage measurement device includes a network protocol analyzer and a timer; Obtaining a storage rate measurement value based on a standard storage capacity and a standard storage time, and obtaining a transmission rate measurement value based on a standard transmission data volume and a standard transmission time; Acquire a preset storage data group from a preset database, and acquire a storage quality index in combination with the storage data, wherein the preset storage data group includes a storage rate threshold, a transmission rate threshold, a preset first storage weight, and a preset second storage weight; The storage quality index is calculated using the following formula: In the formula, represents the storage quality index of the mth storage preset time period, m=1,2,...,n, m represents the number of the storage preset time period, n represents the total number of storage preset time periods, V 1m represents the storage rate measurement value in the mth storage preset time period, V 2m represents the transmission rate measurement value in the mth storage preset time period, V0 represents the storage rate threshold, V1 represents the transmission rate threshold, δ1 represents the preset first storage weight, and δ2 represents the preset second storage weight; S2, preprocessing the initial pathology data to obtain preprocessed pathology data, wherein the preprocessing is used to ensure that the initial pathology data meets the storage integrity requirement; S3, acquiring a preprocessing quality index according to the preprocessed pathological data, wherein the preprocessing quality index includes a noise elimination quality index and a noise improvement quality index, wherein the noise elimination quality index is used to evaluate the error level in the preprocessing process of the initial pathological data, and the noise improvement quality index is used to evaluate the improvement degree of the quality of the initial pathological data; S4, obtaining a comprehensive evaluation index according to the storage quality index and the preprocessing quality index, wherein the comprehensive evaluation index is used to reflect the overall quality of the pathology data.

2. The multivariate-based pathological data analysis method according to claim 1, characterized in that: The specific process of obtaining the preprocessed pathological data is as follows: Comparing the storage quality index with a preset storage quality range to obtain first preprocessed data, wherein the first preprocessed data represents initial pathological data corresponding to the storage quality index within the preset storage quality range; Performing missing value processing on the first preprocessed data to obtain second preprocessed data; The second preprocessed data is normalized to obtain preprocessed pathological data.

3. The multivariate-based pathological data analysis method according to claim 1, characterized in that: The specific process of obtaining the noise elimination quality index is as follows: Acquiring noise temperature data by a preprocessing measurement device during a preprocessing preset time period, wherein the noise temperature data includes standard noise power, standard bandwidth and standard coefficient, and the preprocessing measurement device includes a power meter and a spectrum analyzer; Obtain noise temperature measurements based on standard noise power, standard bandwidth, and standard coefficients; A preset noise temperature threshold is obtained from a preset database, and a noise cancellation quality index is obtained in combination with the noise temperature measurement value.

4. The multivariate-based pathological data analysis method according to claim 3, characterized in that: The noise cancellation quality index is calculated using the following formula: In the formula, represents the noise elimination quality index in the sth preprocessing preset time period, s=1, 2, ..., t, s represents the number of the preprocessing preset time period, t represents the total number of preprocessing preset time periods, T S represents the noise temperature measurement value in the sth preprocessing preset time period, T0 represents the preset noise temperature threshold, and e represents a natural constant.

5. The multivariate-based pathological data analysis method according to claim 1, characterized in that: The specific process of obtaining the noise improvement quality index is as follows: obtaining a signal-to-noise ratio measurement value by a preprocessing measurement device during a preprocessing preset time period; Obtaining a signal-to-noise ratio measurement value based on a standard signal power and a standard noise power; A preset signal-to-noise ratio threshold is obtained from a preset database, and a noise improvement quality index is obtained in combination with the signal-to-noise ratio measurement value.

6. The multivariate-based pathological data analysis method according to claim 5, characterized in that: The noise improvement quality index is calculated using the following formula: In the formula, represents the noise improvement quality index in the sth preprocessing preset time period, s=1,2,...,t, s represents the number of the preprocessing preset time period, t represents the total number of preprocessing preset time periods, D s represents the signal-to-noise ratio measurement value in the sth preprocessing preset time period, D0 represents the preset signal-to-noise ratio threshold, and e represents a natural constant.

7. The multivariate-based pathological data analysis method according to claim 1, characterized in that: The specific process of obtaining the comprehensive evaluation index is as follows: Obtaining a first pathology data analysis weight, a second pathology data analysis weight, and a third pathology data analysis weight from a preset database; A comprehensive evaluation index is obtained according to the first pathology data analysis weight, the second pathology data analysis weight, the third pathology data analysis weight, the storage quality index, the noise elimination quality index and the noise improvement quality index.

8. The multivariate-based pathological data analysis method according to claim 7, characterized in that: The comprehensive evaluation index is calculated using the following formula: In the formula, τ represents the comprehensive evaluation index, represents the storage quality index in the mth storage preset time period, m=1,2,...,n, m represents the number of the storage preset time period, n represents the total number of storage preset time periods, represents the noise elimination quality index in the sth preprocessing preset time period, s=1, 2, ..., t, s represents the number of the preprocessing preset time period, t represents the total number of preprocessing preset time periods, represents the noise improvement quality index in the sth preprocessing preset time period, θ1 represents the first pathology data analysis weight, θ2 represents the second pathology data analysis weight, and θ3 represents the third pathology data analysis weight.

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