A method and system for detecting wound protection performance of hydrocolloid dressing

Abstract

The present application provides a method and system for detecting the wound protection performance of a hydrocolloid dressing, which relates to the technical field of performance detection. The method includes: obtaining basic information of a hydrocolloid dressing; obtaining a historical test log set based on a dressing component type list and a protection performance index; performing a single population component ratio optimization to generate a first dressing component ratio particle set; preparing a hydrocolloid dressing sample and performing a test to obtain a first test log set; when any test log meets the expected protection performance index, outputting the dressing component target ratio information; when any test log does not meet the expected protection performance index, receiving a second dressing component ratio particle set and a second test log set; performing a dual population component ratio optimization to obtain a third dressing component ratio particle set, and returning to perform a cyclic performance test. The present application can solve the technical problem of reduced detection efficiency in the prior art and achieve the technical effect of improving detection efficiency.

Description

Technical Field

[0001] The present application relates to the technical field of performance testing, and in particular to a method and system for testing the wound protection performance of a hydrocolloid dressing. Background Art

[0002] The wound protection performance test of hydrocolloid dressings is a method to evaluate the wound protection effect of hydrocolloid dressings.

[0003] At present, the existing detection process has a large number of test samples, which requires more resources and time to prepare and test. Each test sample needs to be prepared, processed and evaluated, which requires manpower and time, making the entire detection process lengthy and cumbersome. At the same time, a large number of test samples will also increase the difficulty of data analysis and processing. The complexity and time-consuming nature of data analysis may lead to delays or errors in information processing. Therefore, a method is needed to solve the above problems.

[0004] In summary, the prior art has technical problems in that the large number of traditional test samples causes the testing process to become lengthy, reduces the testing efficiency, causes a waste of testing resources, and further affects the quality of the hydrocolloid dressing preparation solution. Summary of the invention

[0005] The purpose of the present application is to provide a method and system for detecting the wound protection performance of a hydrocolloid dressing, so as to solve the technical problem in the prior art that due to the large number of traditional test samples, the detection process becomes lengthy, the detection efficiency is reduced, and the test resources are wasted, which further affects the preparation scheme of the hydrocolloid dressing.

[0006] In view of the above problems, the present application provides a method and system for detecting the wound protection performance of a hydrocolloid dressing.

[0007] In a first aspect, the present application provides a method for detecting the wound protection performance of a hydrocolloid dressing, wherein the method is implemented by a system for detecting the wound protection performance of a hydrocolloid dressing, wherein the method comprises: step one: obtaining basic information of a hydrocolloid dressing, wherein the basic information of a hydrocolloid dressing comprises a list of dressing component types; step two: retrieving a historical test database based on the list of dressing component types and protection performance indicators to obtain a set of historical test logs; step three: performing single population component ratio optimization according to the historical test log set to generate a first dressing component ratio particle set, wherein any one particle stores a set of component ratio optimization values; step four: traversing the first dressing component ratio particle set. Prepare hydrocolloid dressing samples and perform tests to obtain a first test log set; Step 5: When any test log of the first test log set meets the expected protection performance index, output the dressing component target ratio information; Step 6: When any test log of the first test log set does not meet the expected protection performance index, receive a second dressing component ratio particle set and a second test log set through the user end; Step 7: According to the second dressing component ratio particle set and the second test log set, combine the first dressing component ratio particle set and the first test log set to perform dual-population component ratio optimization, obtain a third dressing component ratio particle set, and return to step 4 to perform cyclic performance detection.

[0008] In a second aspect, the present application further provides a system for detecting the wound protection performance of a hydrocolloid dressing, which is used to execute a method for detecting the wound protection performance of a hydrocolloid dressing as described in the first aspect, wherein the system comprises: a hydrocolloid dressing basic information acquisition module, the hydrocolloid dressing basic information acquisition module is used to obtain hydrocolloid dressing basic information, wherein the hydrocolloid dressing basic information includes a dressing component type list; a historical test log set acquisition module, the historical test log set acquisition module is used to retrieve a historical test database based on the dressing component type list and protection performance indicators to obtain a historical test log set; a first dressing component ratio particle set generation module, the first dressing component ratio particle set generation module is used to perform single population component ratio optimization according to the historical test log set to generate a first dressing component ratio particle set, wherein any particle stores a set of component ratio optimization values; a first test log set acquisition module, the first test log set acquisition module is used to traverse the first dressing component ratio particle set. A dressing component ratio particle set is used to prepare a hydrocolloid dressing sample and perform a test to obtain a first test log set; a dressing component target ratio information output module, the dressing component target ratio information output module is used to output dressing component target ratio information when any test log of the first test log set meets the expected protection performance index; a second dressing component ratio particle set receiving module, the second dressing component ratio particle set receiving module is used to receive the second dressing component ratio particle set and the second test log set through the user terminal when any test log of the first test log set does not meet the expected protection performance index; a third dressing component ratio particle set acquisition module, the third dressing component ratio particle set acquisition module is used to perform dual-population component ratio optimization based on the second dressing component ratio particle set and the second test log set, combined with the first dressing component ratio particle set and the first test log set, to obtain a third dressing component ratio particle set, and return to step four to perform cyclic performance detection.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] By obtaining basic information of hydrocolloid dressings, wherein the basic information of hydrocolloid dressings includes a list of dressing component types; based on the list of dressing component types and protection performance indicators, a historical test database is retrieved to obtain a historical test log set; single population component ratio optimization is performed according to the historical test log set to generate a first dressing component ratio particle set, wherein any one particle stores a set of component ratio optimization values; the first dressing component ratio particle set is traversed to prepare hydrocolloid dressing samples and perform tests to obtain a first test log set; when any one test log of the first test log set meets the expected protection performance indicator, the dressing component target ratio information is output ; When any test log of the first test log set does not meet the expected protection performance indicators, a second dressing component ratio particle set and a second test log set are received through the user end; based on the second dressing component ratio particle set and the second test log set, the first dressing component ratio particle set and the first test log set are combined to perform dual-population component ratio optimization to obtain a third dressing component ratio particle set, and return to perform cyclic performance testing. In other words, by optimizing the possible optimal ratio for testing, testing resources are saved, and ultimately the technical goal of improving detection efficiency is achieved, and the technical effect of improving the quality of hydrocolloid dressing preparation solutions is achieved.

[0011] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0013] Figure 1 A schematic diagram of a process for testing the wound protection performance of a hydrocolloid dressing for this application;

[0014] Figure 2 This is a schematic structural diagram of a system for testing the wound protection performance of a hydrocolloid dressing according to the present application.

[0015] Description of reference numerals:

[0016] A hydrocolloid dressing basic information acquisition module 11, a historical test log set acquisition module 12, a first dressing component ratio particle set generation module 13, a first test log set acquisition module 14, a dressing component target ratio information output module 15, a second dressing component ratio particle set receiving module 16, and a third dressing component ratio particle set acquisition module 17. DETAILED DESCRIPTION

[0017] This application provides a method and system for detecting the wound protection performance of hydrocolloid dressings, which solves the technical problem in the prior art that the detection process becomes lengthy and the detection efficiency is reduced due to the large number of traditional test samples, resulting in waste of test resources and further affecting the preparation scheme of hydrocolloid dressings. The technical goal of improving the detection efficiency is achieved, and the technical effect of improving the quality of the preparation scheme of hydrocolloid dressings is achieved.

[0018] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0019] Embodiment 1

[0020] Please refer to the attached Figure 1 The present application provides a method for detecting the wound protection performance of a hydrocolloid dressing, wherein the method is applied to a system for detecting the wound protection performance of a hydrocolloid dressing, and the method specifically comprises the following steps:

[0021] Step 1: obtaining basic information of hydrocolloid dressings, wherein the basic information of hydrocolloid dressings includes a list of dressing component types;

[0022] Specifically, hydrocolloid dressings are dressings made by mixing elastic polymerized hydrogels with synthetic rubber and adhesives, which provide a moist healing environment for wounds and help promote the healing process of wounds. Obtain basic information about hydrocolloid dressings. For example, basic information about hydrocolloid dressings is obtained by searching big data. Among them, the basic information about hydrocolloid dressings includes a list of dressing component types. For example, the list of dressing component types includes polymerized hydrogels, synthetic rubbers, and adhesives.

[0023] Step 2: searching a historical test database based on the dressing component type list and the protection performance index to obtain a historical test log set;

[0024] Specifically, based on the dressing component type list and the protection performance index, data related to the dressing component type list and the protection performance index are retrieved from a historical test database of the hydrocolloid dressing at a historical time to obtain a historical test log set.

[0025] Step 3: Optimizing the proportion of a single population component according to the historical test log set to generate a first dressing component proportion particle set, wherein any particle stores a set of component proportion optimization values;

[0026] Specifically, the dressing component ratio information related to the protection performance index is obtained from the historical test log set. The component ratio combination is randomly generated as the initial particles of the single population. Each particle represents a set of possible component ratio optimization values. With the improvement of the protection performance index as the optimization goal, the performance of each particle in the single population is evaluated by calculating the protection performance index corresponding to each particle. According to the performance evaluation results, the individuals with excellent performance in the single population are selected as the first dressing component ratio particle set.

[0027] Step 4: traverse the first dressing component ratio particle set to prepare a hydrocolloid dressing sample and perform a test to obtain a first test log set;

[0028] Specifically, according to the component ratio represented by each particle in the first dressing component ratio particle set, prepare the corresponding raw materials. According to the process flow of preparing hydrocolloid dressings, mix and process the raw materials to prepare hydrocolloid dressing samples. Perform a protective performance test on each prepared hydrocolloid dressing sample. Record the test data of each sample, including test conditions, test process and test results, to form a first test log. Arrange all first test logs into a collection to obtain a first test log set. The first test log set contains the test data of the sample corresponding to each first dressing component ratio particle.

[0029] Step 5: When any test log in the first test log set meets the expected index of protection performance, output the target ratio information of dressing components;

[0030] Specifically, the test data in the first test log set is compared with the expected index of protection performance. It is determined whether any of the first test logs meets the expected index of protection performance. The dressing component ratio corresponding to the first test log that meets the expected index is extracted, and the dressing component ratio information corresponding to the first test log is output, and the dressing component target ratio information is output.

[0031] Step 6: When any test log of the first test log set does not meet the expected index of protection performance, a second dressing component ratio particle set and a second test log set are received through the user end;

[0032] Specifically, when any test log of the first test log set does not meet the expected protection performance index, the user terminal is started to interact with several data sharing parties. According to the credit identification of the data sharing party, the number of particles is allocated to obtain the second dressing component ratio particle set and the second test log set.

[0033] Step seven: perform dual-population component ratio optimization based on the second dressing component ratio particle set and the second test log set in combination with the first dressing component ratio particle set and the first test log set to obtain a third dressing component ratio particle set, and return to step four to perform cyclic performance testing.

[0034] Specifically, the received second dressing component ratio particle set and the second test log set are combined with the original first dressing component ratio particle set and the first test log set to form a dual-population data set. Based on the dual-population data set, the component ratio is optimized to find the dressing component ratio that meets or exceeds the expected protection performance index. Through multiple iterations and optimization operations, a third dressing component ratio particle set is obtained. The third dressing component ratio particle set is used as a new input, and the process of preparing hydrocolloid dressings is returned to step four to perform cyclic performance testing. According to the process of preparing hydrocolloid dressings, hydrocolloid dressing samples are prepared, tests are performed, and test results are analyzed, and the dressing component ratios are continued to be adjusted and optimized according to the results.

[0035] The method for detecting the wound protection performance of a hydrocolloid dressing is applied to a system for detecting the wound protection performance of a hydrocolloid dressing, which can achieve the technical goal of improving the detection efficiency and the technical effect of improving the quality of the hydrocolloid dressing preparation plan.

[0036] Furthermore, the present application also includes the following steps:

[0037] The protection performance index at least includes: a gel time factor, a compression modulus factor and a mass residual rate factor.

[0038] Specifically, the gel time factor refers to the time required for a hydrocolloid dressing to form a stable gel under specific conditions. A shorter gel time means that the dressing can work quickly and provide a moist and stable healing environment for the wound. Conversely, the effect is worse. The compression modulus factor is used to evaluate the degree of deformation of the hydrocolloid dressing when it is subjected to pressure. A higher compression modulus means that the dressing can maintain a better shape and stability when it is subjected to pressure, thereby more effectively protecting the wound from external pressure and friction. Conversely, the effect is worse. The mass residual rate factor refers to the ratio of the remaining mass to the original mass after being in water for a preset period of time. It is used to evaluate the ability of a hydrocolloid dressing to maintain its original mass after a period of use. A higher mass residual rate means that the dressing is not easy to decompose or degrade during use, and can continue to provide protection for the wound, which helps to ensure the durability and reliability of the dressing, reduce the frequency of dressing changes, and thus reduce the burden on patients. Conversely, the effect is worse.

[0039] By obtaining protection performance indicators, support can be provided for subsequent protection performance testing.

[0040] Furthermore, the present application also includes the following steps:

[0041] Obtain a historical component ratio particle set of the historical test log set;

[0042] According to the historical test log set and based on the protection performance expected index, a historical component ratio particle coarseness set is calculated, wherein any historical component ratio particle coarseness represents the proportion of the number of indicators in the historical test log that meet the protection performance expected index;

[0043] The coarsest particles and the finest particles are sorted from the historical component ratio particle coarseness set, and a random search is performed using the coarsest particles to guide the finest particles, so as to generate the first dressing component ratio particle set.

[0044] Specifically, the ratio data of dressing components in each historical test log is extracted from the historical test log set to obtain a historical component ratio particle set. The historical component ratio particle set may include percentages or specific values ​​of components such as polymerized hydrogel, synthetic rubber, and adhesive.

[0045] Then, the historical test log set is evaluated according to the protection performance expectation index. For each historical component ratio granule in the historical test log set, the protection performance index value of the historical component ratio granule is compared with the protection performance expectation index. The proportion of the number of indicators that meet the protection performance expectation index for each historical component ratio granule is calculated. For example, this is achieved by counting the number of performance indicators that meet the expected index in each historical component ratio granule and dividing it by the total number of performance indicators, which is used to reflect the degree of compliance of the historical component ratio granule in performance. Each historical component ratio granule is combined with the proportion of the number of indicators corresponding to it to form a historical component ratio granule coarseness set. Each granule in the historical component ratio granule coarseness set contains not only the component ratio information, but also the degree to which its performance meets the expected index.

[0046] Next, after obtaining the historical component ratio particle coarseness set, the coarsest particles and the finest particles are sorted out from the historical component ratio particle coarseness set. For example, this is achieved by comparing the proportion of the number of indicators of each historical component ratio particle. The particles with the highest proportion of the number of indicators are selected as the coarsest particles, which represent the combination with the performance that best meets the expected indicators in the historical test log. The particles with the lowest proportion of the number of indicators are selected as the finest particles, which represent the combination with the largest difference between the performance and the expected indicators in the historical test log. According to the component ratio range of the coarsest particles and the finest particles, the upper and lower bounds of the search space are determined. Randomly generate component ratio combinations in the search space to form a candidate particle set. Perform a performance evaluation on each particle in the candidate particle set, and screen out particles that meet the expected indicators of protection performance to form a first dressing component ratio particle set.

[0047] By obtaining dressing component ratio information related to protection performance indicators from historical test logs, data support can be provided for subsequent dressing optimization and design.

[0048] Furthermore, the present application also includes the following steps:

[0049] The first coordinate is set according to the proportion information of the finest particle component, and the second coordinate is set according to the proportion information of the coarsest particle component;

[0050] Calculate the Euclidean distance between the first coordinate and the second coordinate, and set it as a first spatial distance;

[0051] Search A: obtain a first random number through the RAND (0, 1) function, and multiply the first random number by the first spatial distance to obtain a first search distance;

[0052] Search B: starting a search based on the first coordinate according to the first search distance to obtain particles of a first dressing component ratio, wherein the Euclidean distance between the particles of the first dressing component ratio and the first coordinate is equal to the first search distance, and the Euclidean distance between the particles of the first dressing component ratio and the second coordinate is less than the first spatial distance;

[0053] The search A and the search B are cycled for a preset number of times to generate the first dressing component ratio particle set.

[0054] Specifically, a point in a multidimensional space is set as a first coordinate according to the component ratio information of the finest particles, and a point in another multidimensional space is set as a second coordinate according to the component ratio information of the coarsest particles.

[0055] Then, the Euclidean distance between the first coordinate and the second coordinate is calculated, which represents the straight-line distance between the first coordinate and the second coordinate in the multidimensional space and is set as the first spatial distance.

[0056] Next, the RAND (0, 1) function is a random number generation function, and a random number between 0 and 1 is randomly generated by the RAND (0, 1) function to obtain a first random number. The first random number is multiplied by the first spatial distance to obtain a first search distance.

[0057] Next, starting from the first coordinate, searching for points in the space within the range of the first search distance to obtain particles of the first dressing component ratio, wherein the Euclidean distance between the particles of the first dressing component ratio and the first coordinate is equal to the first search distance, and the Euclidean distance between the particles of the first dressing component ratio and the second coordinate is less than the first spatial distance.

[0058] Then, the search A and search B are repeatedly performed for a preset number of times. For example, the preset number of searches A and B is obtained by the size and diversity of the generated first dressing component ratio particle set. For example, the preset number of searches A and B is 1000 times or more. New first dressing component ratio particles are obtained in each cycle, and finally a first dressing component ratio particle set containing a plurality of first dressing component ratio particles is generated.

[0059] By searching and generating dressing particles with different component ratios between the finest and coarsest particles, more options are provided for subsequent dressing optimization and design.

[0060] Furthermore, the present application also includes the following steps:

[0061] According to the first test log set, based on the protection performance expected index, obtaining a first dressing component ratio particle coarseness set of the first dressing component ratio particle set;

[0062] According to the second test log set, based on the protection performance expected index, obtaining a second dressing component ratio particle coarseness set of the second dressing component ratio particle set;

[0063] According to the first dressing component ratio particle coarseness set, sorting the first dressing component ratio particle set from coarse to fine to generate a first dressing component ratio particle sorting result;

[0064] According to the coarseness set of the second dressing component ratio particles, sorting the second dressing component ratio particle set from coarse to fine to generate a second dressing component ratio particle sorting result;

[0065] Based on the first dressing component ratio particle sorting result and the second dressing component ratio particle sorting result, dual-population component ratio optimization is performed to obtain the third dressing component ratio particle set.

[0066] Specifically, the performance of each first dressing component ratio particle is evaluated according to the first test log set and the protection performance expectation index. According to the performance evaluation result, a coarseness value is assigned to each particle to obtain a first dressing component ratio particle coarseness set of the first dressing component ratio particle set. For example, the coarseness value can be determined based on the gap between the performance index and the expected index. The smaller the gap, the higher the coarseness value, indicating that the performance of the particle is more ideal, and vice versa.

[0067] Then, the performance of each second dressing component ratio particle is evaluated according to the second test log set and the protection performance expectation index. According to the performance evaluation result, a coarseness value is assigned to each particle to obtain a second dressing component ratio particle coarseness set of the second dressing component ratio particle set.

[0068] Next, according to the first dressing component ratio particle coarseness set, the first dressing component ratio particle set is sorted from coarse to fine, that is, particles with high coarseness values ​​are arranged in front and particles with low coarseness values ​​are arranged in the back, to generate the first dressing component ratio particle sorting result.

[0069] Next, according to the second dressing component ratio particle coarseness set, the second dressing component ratio particle set is sorted from coarse to fine, that is, particles with high coarseness values ​​are arranged in front and particles with low coarseness values ​​are arranged in the back, to generate the second dressing component ratio particle sorting result.

[0070] Then, based on the first dressing component ratio particle sorting result and the second dressing component ratio particle sorting result, a dual population component ratio optimization is performed. By combining information from two different data sources, a better dressing component ratio is found. After the dual population component ratio optimization, the obtained dressing component ratio particle set with better performance is used as the third dressing component ratio particle set.

[0071] Improve the efficiency and accuracy of optimizing dressing ingredient ratios by leveraging information from different data sources.

[0072] Furthermore, the present application also includes the following steps:

[0073] sorting a first number of reference dressing component ratio particle sets according to the first dressing component ratio particle sorting result;

[0074] sorting a first number of comparison dressing component ratio particle sets according to the second dressing component ratio particle sorting result;

[0075] A random dimensional crossover is performed on the first baseline dressing component ratio particle of the baseline dressing component ratio particle set and the first comparison dressing component ratio particle of the comparison dressing component ratio particle set to generate the third dressing component ratio particle set, wherein the dimension is less than the number of component types.

[0076] Specifically, according to the sorting result of the first dressing component ratio particles, the first first number of first dressing component ratio particles are selected as the reference dressing component ratio particle set. The reference dressing component ratio particle set has relatively good performance and has a high reference value. The first number is obtained by a technician in this field according to actual conditions.

[0077] Then, according to the sorting result of the second dressing component ratio particles, the first number of second dressing component ratio particles are selected as the comparison dressing component ratio particle set. The comparison dressing component ratio particle set comes from different data sources, and comparing and crossing with the benchmark dressing component ratio particle set is helpful to discover new optimization directions.

[0078] Next, a dimension smaller than the number of component types is randomly selected as a crossover dimension, which means that all component types are not crossovered, but some of them are selected for crossover operation to increase the randomness and diversity of the crossover. For the first reference dressing component ratio particle of the reference dressing component ratio particle set and the first comparison dressing component ratio particle of the comparison dressing component ratio particle set, a crossover operation is performed according to the selected crossover dimension. The component ratios of the two particles on the crossover dimension are interchanged or combined to generate a new dressing component ratio particle. The above crossover operation is repeated until a sufficient number of new particles are generated to form a third dressing component ratio particle set.

[0079] By crossing random dimensions and combining information from different data sources, new combinations of dressing ingredient ratios can be explored and dressing formulas with better performance can be found. The selection of random dimensions increases the diversity and flexibility of crossover operations and helps to discover potential correlations and optimization spaces between different dimensions.

[0080] Furthermore, the present application also includes the following steps:

[0081] When any test log of the first test log set does not meet the protection performance expectation indicator, interact with several data sharing parties through the user end, wherein the several data sharing parties include a historical credit identifier, and the historical credit identifier is a historical shared data accuracy label;

[0082] Calculating a number of data sharing weights according to the historical credit identifier;

[0083] According to the number of particle constraints, the plurality of data sharing weights are combined to perform allocation to obtain a plurality of allocated particle numbers;

[0084] The second dressing component ratio particle set and the second test log set are received from the plurality of data sharing parties according to the plurality of allocated particle numbers.

[0085] Specifically, when any test log of the first test log set does not meet the protection performance expectation indicator, the interaction process between the user end and several data sharing parties is initiated. Among them, the data sharing parties are other companies in the industrial alliance. Several data sharing parties include historical credit identification, that is, historical shared data accuracy labels.

[0086] Then, the data accuracy of each data sharer is evaluated based on the historical credit identification. For example, the historical credit identification can be derived based on factors such as the accuracy, completeness, and timeliness of previous data sharing. Based on the evaluation results, the data sharing weight is calculated for each data sharer to reflect the credibility and value of the data of the data sharer.

[0087] Next, determine the number of particle constraints, that is, the maximum number of particles that you want to obtain from each data sharing party in this interaction. Combined with the data sharing weight, the number of particles is allocated to obtain several allocated numbers of particles. Among them, the data sharing party with a higher weight will obtain more particles and thus can obtain more data resources, and vice versa.

[0088] Next, according to the number of allocated particles, a request is sent to each data sharing party to obtain the corresponding second dressing component ratio particle set and the second test log set. After receiving the request, the data sharing party provides the corresponding data resources according to the number of particles.

[0089] Through interaction and utilization of external data resources, the deficiencies of its own data can be made up, providing more possibilities for optimizing the proportion of dressing ingredients. Through historical credit identification and data sharing weights, the credibility and value of the acquired data can be ensured, thereby improving the efficiency of data utilization.

[0090] In summary, the wound protection performance testing method of a hydrocolloid dressing provided in this application has the following technical effects:

[0091] By obtaining basic information of hydrocolloid dressings, wherein the basic information of hydrocolloid dressings includes a list of dressing component types; based on the list of dressing component types and protection performance indicators, a historical test database is retrieved to obtain a historical test log set; single population component ratio optimization is performed according to the historical test log set to generate a first dressing component ratio particle set, wherein any one particle stores a set of component ratio optimization values; the first dressing component ratio particle set is traversed to prepare hydrocolloid dressing samples and perform tests to obtain a first test log set; when any one test log of the first test log set meets the expected protection performance indicator, the dressing component target ratio information is output ; When any test log of the first test log set does not meet the expected protection performance indicators, a second dressing component ratio particle set and a second test log set are received through the user end; based on the second dressing component ratio particle set and the second test log set, the first dressing component ratio particle set and the first test log set are combined to perform dual-population component ratio optimization to obtain a third dressing component ratio particle set, and return to perform cyclic performance testing. In other words, by optimizing the possible optimal ratio for testing, testing resources are saved, and ultimately the technical goal of improving detection efficiency is achieved, and the technical effect of improving the quality of hydrocolloid dressing preparation solutions is achieved.

[0092] Embodiment 2

[0093] Based on the wound protection performance detection method of a hydrocolloid dressing in the aforementioned embodiment, the present application also provides a wound protection performance detection system for a hydrocolloid dressing, please refer to the attached Figure 2 , the system comprising:

[0094] A hydrocolloid dressing basic information obtaining module 11, the hydrocolloid dressing basic information obtaining module 11 is used to obtain hydrocolloid dressing basic information, wherein the hydrocolloid dressing basic information includes a dressing component type list;

[0095] A historical test log set acquisition module 12, the historical test log set acquisition module 12 is used to retrieve a historical test database based on the dressing component type list and the protection performance index to obtain a historical test log set;

[0096] The first dressing component ratio particle set generation module 13 is used to perform single population component ratio optimization according to the historical test log set to generate a first dressing component ratio particle set, wherein any particle stores a set of component ratio optimization values;

[0097] A first test log set acquisition module 14, the first test log set acquisition module 14 is used to traverse the first dressing component ratio particle set to prepare a hydrocolloid dressing sample and perform a test to obtain a first test log set;

[0098] A dressing component target ratio information output module 15, wherein the dressing component target ratio information output module 15 is used to output dressing component target ratio information when any test log of the first test log set meets the expected index of protection performance;

[0099] The second dressing component ratio particle set receiving module 16 is used to receive the second dressing component ratio particle set and the second test log set through the user terminal when any test log of the first test log set does not meet the expected protection performance index;

[0100] The third dressing component ratio particle set acquisition module 17 is used to perform dual-population component ratio optimization based on the second dressing component ratio particle set and the second test log set in combination with the first dressing component ratio particle set and the first test log set to obtain the third dressing component ratio particle set, and return to step four to perform cyclic performance detection.

[0101] Further, the historical test log set obtaining module 12 in the system is also used for:

[0102] The protection performance index at least includes: a gel time factor, a compression modulus factor and a mass residual rate factor.

[0103] Furthermore, the first dressing component ratio particle set generating module 13 in the system is also used for:

[0104] Obtain a historical component ratio particle set of the historical test log set;

[0105] According to the historical test log set and based on the protection performance expected index, a historical component ratio particle coarseness set is calculated, wherein any historical component ratio particle coarseness represents the proportion of the number of indicators in the historical test log that meet the protection performance expected index;

[0106] The coarsest particles and the finest particles are sorted from the historical component ratio particle coarseness set, and a random search is performed using the coarsest particles to guide the finest particles, so as to generate the first dressing component ratio particle set.

[0107] Furthermore, the first dressing component ratio particle set generating module 13 in the system is also used for:

[0108] The first coordinate is set according to the proportion information of the finest particle component, and the second coordinate is set according to the proportion information of the coarsest particle component;

[0109] Calculate the Euclidean distance between the first coordinate and the second coordinate, and set it as a first spatial distance;

[0110] Search A: obtain a first random number through the RAND (0, 1) function, and multiply the first random number by the first spatial distance to obtain a first search distance;

[0111] Search B: starting a search based on the first coordinate according to the first search distance to obtain particles of a first dressing component ratio, wherein the Euclidean distance between the particles of the first dressing component ratio and the first coordinate is equal to the first search distance, and the Euclidean distance between the particles of the first dressing component ratio and the second coordinate is less than the first spatial distance;

[0112] The search A and the search B are cycled for a preset number of times to generate the first dressing component ratio particle set.

[0113] Furthermore, the third dressing component ratio particle set obtaining module 17 in the system is also used for:

[0114] According to the first test log set, based on the protection performance expected index, obtaining a first dressing component ratio particle coarseness set of the first dressing component ratio particle set;

[0115] According to the second test log set, based on the protection performance expected index, obtaining a second dressing component ratio particle coarseness set of the second dressing component ratio particle set;

[0116] According to the first dressing component ratio particle coarseness set, sorting the first dressing component ratio particle set from coarse to fine to generate a first dressing component ratio particle sorting result;

[0117] According to the coarseness set of the second dressing component ratio particles, sorting the second dressing component ratio particle set from coarse to fine to generate a second dressing component ratio particle sorting result;

[0118] Based on the first dressing component ratio particle sorting result and the second dressing component ratio particle sorting result, dual-population component ratio optimization is performed to obtain the third dressing component ratio particle set.

[0119] Furthermore, the third dressing component ratio particle set obtaining module 17 in the system is also used for:

[0120] sorting a first number of reference dressing component ratio particle sets according to the first dressing component ratio particle sorting result;

[0121] sorting a first number of comparison dressing component ratio particle sets according to the second dressing component ratio particle sorting result;

[0122] A random dimensional crossover is performed on the first baseline dressing component ratio particle of the baseline dressing component ratio particle set and the first comparison dressing component ratio particle of the comparison dressing component ratio particle set to generate the third dressing component ratio particle set, wherein the dimension is less than the number of component types.

[0123] Furthermore, the second dressing component ratio particle set receiving module 16 in the system is also used for:

[0124] When any test log of the first test log set does not meet the protection performance expectation indicator, interact with several data sharing parties through the user end, wherein the several data sharing parties include a historical credit identifier, and the historical credit identifier is a historical shared data accuracy label;

[0125] Calculating a number of data sharing weights according to the historical credit identifier;

[0126] According to the number of particle constraints, the plurality of data sharing weights are combined to perform allocation to obtain a plurality of allocated particle numbers;

[0127] The second dressing component ratio particle set and the second test log set are received from the plurality of data sharing parties according to the plurality of allocated particle numbers.

[0128] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The wound protection performance detection method and specific examples of a hydrocolloid dressing in the aforementioned embodiment 1 are also applicable to a wound protection performance detection system of a hydrocolloid dressing in this embodiment. Through the aforementioned detailed description of the wound protection performance detection method of a hydrocolloid dressing, those skilled in the art can clearly know the wound protection performance detection system of a hydrocolloid dressing in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0129] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0130] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A method for detecting the wound protection performance of a hydrocolloid dressing, characterized in that: include: Step 1: obtaining basic information of hydrocolloid dressings, wherein the basic information of hydrocolloid dressings includes a list of dressing component types; Step 2: searching a historical test database based on the dressing component type list and the protection performance index to obtain a historical test log set; Step 3: Optimizing the proportion of a single population component according to the historical test log set to generate a first dressing component proportion particle set, wherein any particle stores a set of component proportion optimization values; Step 4: traverse the first dressing component ratio particle set to prepare a hydrocolloid dressing sample and perform a test to obtain a first test log set; Step 5: When any test log in the first test log set meets the expected index of protection performance, output the target ratio information of dressing components; Step 6: When any test log of the first test log set does not meet the expected index of protection performance, a second dressing component ratio particle set and a second test log set are received through the user end; Step 7: performing dual-population component ratio optimization based on the second dressing component ratio particle set and the second test log set in combination with the first dressing component ratio particle set and the first test log set to obtain a third dressing component ratio particle set, and returning to step 4 to perform cyclic performance testing; The protection performance index at least includes: gel time factor, compression modulus factor and mass residual rate factor; Optimizing the proportion of a single population component according to the historical test log set to generate a first dressing component proportion particle set, including: Obtain a historical component ratio particle set of the historical test log set; According to the historical test log set and based on the protection performance expected index, a historical component ratio particle coarseness set is calculated, wherein any historical component ratio particle coarseness represents the proportion of the number of indicators in the historical test log that meet the protection performance expected index; sorting the coarsest particles and the finest particles from the historical component ratio particle coarseness set, using the coarsest particles to guide the finest particles to perform a random search, and generating the first dressing component ratio particle set; The method comprises: selecting the coarsest particles and the finest particles from the historical component ratio particle coarseness set, and using the coarsest particles to guide the finest particles to perform random search to generate the first dressing component ratio particle set, including: The first coordinate is set according to the proportion information of the finest particle component, and the second coordinate is set according to the proportion information of the coarsest particle component; Calculate the Euclidean distance between the first coordinate and the second coordinate, and set it as a first spatial distance; Search A: obtain a first random number through the RAND (0, 1) function, and multiply the first random number by the first spatial distance to obtain a first search distance; Search B: starting a search based on the first coordinate according to the first search distance to obtain particles of a first dressing component ratio, wherein the Euclidean distance between the particles of the first dressing component ratio and the first coordinate is equal to the first search distance, and the Euclidean distance between the particles of the first dressing component ratio and the second coordinate is less than the first spatial distance; The search A and the search B are cycled for a preset number of times to generate the first dressing component ratio particle set.

2. The method according to claim 1, characterized in that According to the second dressing component ratio particle set and the second test log set, the first dressing component ratio particle set and the first test log set are combined to perform dual-population component ratio optimization to obtain a third dressing component ratio particle set, including: According to the first test log set, based on the protection performance expected index, obtaining a first dressing component ratio particle coarseness set of the first dressing component ratio particle set; According to the second test log set, based on the protection performance expected index, obtaining a second dressing component ratio particle coarseness set of the second dressing component ratio particle set; According to the first dressing component ratio particle coarseness set, sorting the first dressing component ratio particle set from coarse to fine to generate a first dressing component ratio particle sorting result; According to the coarseness set of the second dressing component ratio particles, sorting the second dressing component ratio particle set from coarse to fine to generate a second dressing component ratio particle sorting result; Based on the first dressing component ratio particle sorting result and the second dressing component ratio particle sorting result, a dual-population component ratio optimization is performed to obtain the third dressing component ratio particle set.

3. The method according to claim 2, characterized in that Based on the first dressing component ratio particle sorting result and the second dressing component ratio particle sorting result, dual-population component ratio optimization is performed to obtain the third dressing component ratio particle set, including: sorting a first number of reference dressing component ratio particle sets according to the first dressing component ratio particle sorting result; sorting a first number of comparison dressing component ratio particle sets according to the second dressing component ratio particle sorting result; A random dimensional crossover is performed on the first baseline dressing component ratio particle of the baseline dressing component ratio particle set and the first comparison dressing component ratio particle of the comparison dressing component ratio particle set to generate the third dressing component ratio particle set, wherein the dimension is less than the number of component types.

4. The method according to claim 1, characterized in that When any test log of the first test log set does not meet the expected index of protection performance, a second dressing component ratio particle set and a second test log set are received through the user end, including: When any test log of the first test log set does not meet the protection performance expectation indicator, interact with several data sharing parties through the user end, wherein the several data sharing parties include a historical credit identifier, and the historical credit identifier is a historical shared data accuracy label; Calculating a number of data sharing weights according to the historical credit identifier; According to the number of particle constraints, the plurality of data sharing weights are combined to perform allocation to obtain a plurality of allocated particle numbers; The second dressing component ratio particle set and the second test log set are received from the plurality of data sharing parties according to the plurality of allocated particle numbers.

5. A hydrocolloid dressing wound protection performance detection system, characterized in that: For implementing the steps of the method according to any one of claims 1 to 4, the system comprises: A hydrocolloid dressing basic information acquisition module, the hydrocolloid dressing basic information acquisition module is used to obtain hydrocolloid dressing basic information, wherein the hydrocolloid dressing basic information includes a dressing component type list; A historical test log set acquisition module, the historical test log set acquisition module is used to retrieve a historical test database based on the dressing component type list and the protection performance index to obtain a historical test log set; A first dressing component ratio particle set generation module, the first dressing component ratio particle set generation module is used to perform single population component ratio optimization according to the historical test log set to generate a first dressing component ratio particle set, wherein any particle stores a set of component ratio optimization values; A first test log set acquisition module, the first test log set acquisition module is used to traverse the first dressing component ratio particle set to prepare a hydrocolloid dressing sample and perform a test to obtain a first test log set; A dressing component target ratio information output module, the dressing component target ratio information output module is used to output dressing component target ratio information when any test log of the first test log set meets the expected index of protection performance; A second dressing component ratio particle set receiving module, the second dressing component ratio particle set receiving module is used to receive a second dressing component ratio particle set and a second test log set through a user terminal when any test log of the first test log set does not meet the expected protection performance index; A third dressing component ratio particle set acquisition module is used to perform dual-population component ratio optimization based on the second dressing component ratio particle set and the second test log set in combination with the first dressing component ratio particle set and the first test log set to obtain the third dressing component ratio particle set, and return to step four to perform cyclic performance detection.

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