Disinfection effect detection method based on big data

Abstract

The invention discloses a disinfection effect detection method based on big data, and the method comprises the steps: analyzing the disinfection matching degree of a disinfectant and virus microorganisms according to the condition of the virus microorganisms and the type of the disinfectant to obtain a disinfection matching influence weight ratio P; detecting environmental parameter information when the disinfectant is used in the to-be-detected area, judging the influence of the environment on the disinfection effect, and obtaining a disinfection environment influence coefficient ART; according to the first virus microorganism type information and the area capacity of the to-be-detected area, the appropriate disinfectant use dosage BX is obtained, according to the disinfectant concentration information and the disinfection duration information of the to-be-detected area, the actual disinfectant use dosage information BS is obtained, a disinfectant dosage influence coefficient BJ is obtained through analysis, and according to the disinfection operation mode information, an operation influence coefficient BF is obtained; and integrating the disinfection dosage influence coefficient BJ and the disinfection mode influence coefficient BF to obtain a disinfection use mode influence coefficient BTH. The method has the effect of improving the disinfection effect detection accuracy based on the big data.

Description

Technical Field

[0001] This application relates to the field of big data technology, and in particular, to a method for detecting disinfection effect based on big data. Background Art

[0002] Currently, big data, or massive data, refers to the data volume involved is so huge that it cannot be captured, managed, processed, and organized into information that helps enterprises make more proactive business decisions through mainstream software tools within a reasonable time. With the rapid progress and development of modern social sciences, people pay more and more attention to health and safety. There are various disinfection technologies, so it is also crucial to detect the disinfection effect.

[0003] However, the existing disinfection effect detection method refers to evaluating the sterilization effect by culturing and counting the number of colonies before and after disinfection. If the number of colonies after disinfection is significantly reduced or completely disappeared, the disinfection effect is considered good. However, the existing disinfection effect detection method does not consider the environmental parameters during disinfection and the influence of the disinfectant usage method on the disinfection effect, and the accuracy of detecting the disinfection effect is relatively low, and there is room for improvement. Summary of the Invention

[0004] In order to improve the detection accuracy of the disinfection effect based on big data, this application provides a method for detecting the disinfection effect based on big data.

[0005] In a first aspect, a method for detecting the disinfection effect based on big data provided by this application adopts the following technical solution: A method for detecting the disinfection effect based on big data includes the following steps: According to the virus and microorganism situation before disinfection in the area to be tested and the type of disinfectant used for disinfection in the area to be tested, analyze the disinfection matching degree between the disinfectant and the virus and microorganism to obtain the disinfection matching influence weight ratio P; Based on big data technology, obtain the suitable environment of the disinfectant used in the area to be tested, and detect the environmental parameter information when the disinfectant is used in the area to be tested to judge the influence of the environment on the disinfection effect to obtain the disinfection environment influence coefficient ART; According to the first virus and microorganism type information and the area capacity of the area to be tested, obtain the suitable disinfectant usage dose BX. According to the disinfectant concentration information and the disinfection duration information when the disinfectant is used for disinfection treatment in the area to be tested, obtain the actual disinfectant usage dose information BS. Compare the actual disinfectant usage dose information BS with the suitable disinfectant usage dose BX, analyze to obtain the disinfection dose influence coefficient BJ, and obtain the operation influence coefficient BF according to the disinfection operation method information. Combine the disinfection dose influence coefficient BJ and the disinfection method influence coefficient BF to obtain the disinfection usage method influence coefficient BTH; The theoretical disinfection intensity coefficient W1 is obtained by synthesizing and disinfecting and matching the influence weight ratio P, the disinfection environment influence coefficient ART, and the disinfection usage method influence coefficient BTH; Obtain a biological indicator and place it in the area to be tested. The actual disinfection intensity coefficient W2 is obtained according to the changes in the microorganisms cultured in the biological indicator before and after the disinfection operation; Compare the actual disinfection intensity coefficient W2 with the theoretical disinfection intensity coefficient W1 to calculate the difference to obtain the disinfection intensity difference. Compare the disinfection intensity difference with the preset disinfection intensity difference threshold, and analyze the influence weight ratio QS of the theoretical and actual differences; Detect the virus and microorganism situation after disinfecting the area to be tested to obtain the second virus and microorganism information, and determine the influence weight ratio QR of the virus and microorganism residue based on the second virus and microorganism information; According to the influence weight ratio QS of the theoretical and actual differences and the influence weight ratio QR of the virus and microorganism residue, obtain the final disinfection effect coefficient T of the area to be tested, and evaluate the disinfection effect of the area to be tested according to the final disinfection effect coefficient T.

[0006] Preferably, detect the virus and microorganism situation before disinfecting the area to be tested to obtain the first virus and microorganism information, and the first virus and microorganism information includes the first virus and microorganism type information and the first virus and microorganism quantity information of different first virus and microorganism types; Obtain the disinfectant type information for disinfecting the area to be tested; Obtain the disinfectant and microorganism matching degree table based on big data technology. The disinfectant and microorganism matching degree table includes the virus and microorganism types that different disinfectants can eliminate and the targeted elimination intensity for different virus and microorganism types; Search in the disinfectant and microorganism matching degree table based on the first virus and microorganism type information and the disinfectant type information to obtain multiple targeted elimination intensity information K1, K2, K3,..., K for different first virus and microorganism type information in the area to be tested n , where n refers to the number of virus and microorganism types of the first virus and microorganism type information; According to multiple targeted elimination intensity information K1, K2, K3,..., K n , based on the disinfection and matching relationship function Calculate to obtain the disinfection and matching influence weight ratio P, where is a proportionality factor and is greater than 0.

[0007] Preferably, detect the disinfection environment when disinfecting the area to be tested with a disinfectant to obtain the environmental parameter information, and the environmental parameter information includes temperature information, humidity information, and light information; Obtain the appropriate temperature of the disinfectant used in the area to be measured to obtain the appropriate temperature information, and obtain the appropriate humidity of the disinfectant used in the area to be measured to obtain the appropriate humidity information; Compare the temperature information in the environmental parameters with the appropriate temperature information to obtain a temperature difference, and obtain a temperature influence coefficient AW based on the temperature difference. The larger the temperature difference, the larger the temperature influence coefficient AW; Compare the humidity information in the environmental parameters with the appropriate humidity information to obtain a humidity difference, and obtain a humidity influence coefficient AS based on the humidity difference. The larger the humidity difference, the larger the humidity influence coefficient AS; Based on big data technology, check whether the disinfectant used in the area to be measured is sensitive to light. If not, the light influence coefficient AG is obtained as 0. If it is sensitive to light, the light influence coefficient AG is obtained based on the sensitivity degree of the disinfectant used in the area to be measured to light and the light intensity information. Among them, the higher the sensitivity degree of the disinfectant used in the area to be measured to light, the larger the light influence coefficient AG, and the stronger the light intensity information when the disinfectant is used in the area to be measured, the larger the light influence coefficient AG; Integrate the temperature influence coefficient AW, the humidity influence coefficient AS, and the light influence coefficient AG, and based on the environmental relationship function Perform calculations to obtain a disinfection environment influence coefficient ART, where a1, a2, and a3 are proportionality factors and are all greater than 0.

[0008] Preferably, obtain the first virus and microorganism concentration information BN according to the first virus and microorganism type information and the first virus and microorganism quantity information of different first virus and microorganism types. The more the first virus and microorganism type information, the higher the first virus and microorganism concentration information BN, and the more the first virus and microorganism quantity information of different first virus and microorganism types, the higher the first virus and microorganism concentration information BN; Obtain the area capacity of the area to be measured to obtain the area capacity information BQ; According to the first virus and microorganism concentration information BN and the area capacity information BQ, based on the disinfectant concentration relationship function Perform calculations to obtain the appropriate disinfectant usage dose BX, where b1 and b2 are proportionality factors and are all greater than 0; Obtain the usage method parameter information when using the disinfectant for disinfection treatment in the area to be measured. The usage method parameter information includes disinfectant concentration information, disinfection duration information, and disinfection operation method information. The disinfection operation method information includes spraying disinfection, wiping disinfection, and soaking disinfection; Based on the disinfectant concentration information and the disinfection duration information, calculate the disinfectant usage dose when using the disinfectant for disinfection in the area to be measured to obtain the actual disinfectant usage dose information BS; Compare the actual disinfectant dosage information BS with the appropriate disinfectant dosage BX. If the actual disinfectant dosage information BS is greater than or equal to the appropriate disinfectant dosage BX, output the disinfection dosage influence coefficient BJ as 0. If the actual disinfectant dosage information BS is less than the appropriate disinfectant dosage BX, calculate the difference between the actual disinfectant dosage information BS and the appropriate disinfectant dosage BX to obtain the disinfection dosage difference. Based on the disinfection dosage difference, obtain the disinfection dosage influence coefficient BJ. Among them, the greater the disinfection dosage difference, the greater the disinfection dosage influence coefficient BJ; Obtain the operation influence coefficient BF according to the disinfection operation method information, where the operation influence coefficient BF for spraying disinfection, the operation influence coefficient BF for wiping disinfection, and the operation influence coefficient BF for immersion disinfection decrease in turn; According to the disinfection dosage influence coefficient BJ and the disinfection method influence coefficient BF, based on the operation relationship function Calculate to obtain the disinfection usage method influence coefficient BTH, where b3 and b4 are proportionality factors and are both greater than 0.

[0009] Preferably, comprehensively consider the disinfection matching influence weight ratio P, the disinfection environment influence coefficient ART, and the disinfection usage method influence coefficient BTH, and calculate the theoretical disinfection strength coefficient W1 based on the theoretical disinfection strength function where 、 are proportionality factors and are both greater than 0.

[0010] Preferably, obtain a biological indicator, and place the biological indicator in the area to be tested before the disinfection operation in the area to be tested; Obtain the microbial situation of the biological indicator cultured before the disinfection operation in the area to be tested to obtain the pre-operation microbial information, where the pre-operation microbial information includes the pre-operation microbial type and the pre-operation microbial quantity; Detect the microbial situation of the biological indicator after the disinfection treatment in the area to be tested to obtain the post-operation microbial information, where the post-operation microbial information includes the post-operation microbial type and the post-operation microbial quantity; Compare the post-operation microbial type with the pre-operation microbial type. If the post-operation microbial type is inconsistent with the pre-operation microbial type, obtain the microbial type influence coefficient CLT based on the difference between the post-operation microbial type and the pre-operation microbial type. Among them, the greater the difference between the post-operation microbial type and the pre-operation microbial type, the greater the microbial type influence coefficient CLT; Compare the number of microorganisms after the operation with the number of microorganisms before the operation. If the number of microorganisms after the operation is inconsistent with the number of microorganisms before the operation, then obtain the microorganism quantity influence coefficient DSL based on the difference between the number of microorganisms after the operation and the number of microorganisms before the operation, where the greater the difference between the number of microorganisms after the operation and the number of microorganisms before the operation, the greater the microorganism quantity influence coefficient DSL; According to the microorganism type influence coefficient CLT and the microorganism quantity influence coefficient DSL, based on the actual disinfection intensity function calculate to obtain the actual disinfection intensity coefficient W2, where, 、 are proportionality factors and are both greater than 0.

[0011] Preferably, compare the actual disinfection intensity coefficient W2 with the theoretical disinfection intensity coefficient W1 to obtain the theoretical-actual comparison result; Based on the theoretical-actual comparison result, if the actual disinfection intensity coefficient W2 is less than the theoretical disinfection intensity coefficient W1, then calculate the difference between the actual disinfection intensity coefficient W2 and the theoretical disinfection intensity coefficient W1 to obtain the disinfection intensity difference; Compare the disinfection intensity difference with the preset disinfection intensity difference threshold. If the disinfection intensity difference is greater than the preset disinfection intensity difference threshold, then calculate the gap value between the disinfection intensity difference and the preset disinfection intensity difference threshold to obtain the theoretical-actual gap value; Obtain the theoretical-actual difference influence weight ratio QS based on the theoretical-actual gap value, where the greater the theoretical-actual gap value, the greater the theoretical-actual difference influence weight ratio QS.

[0012] Preferably, detect the virus microorganism situation before disinfecting the area to be tested to obtain the second virus microorganism information, where the second virus microorganism information includes the second virus microorganism type information and the second virus microorganism quantity information of different second virus microorganism types; Determine the virus microorganism residue influence weight ratio QR according to the second virus microorganism type information and the second virus microorganism quantity information of different second virus microorganism types, where the more the second virus microorganism type information, the greater the virus microorganism residue influence weight ratio QR, and the more the second virus microorganism quantity information, the greater the virus microorganism residue influence weight ratio QR.

[0013] Preferably, synthesize the theoretical-actual difference influence weight ratio QS and the virus microorganism residue influence weight ratio QR, and calculate based on the disinfection effect relationship function to obtain the final disinfection effect coefficient T, where, 、 are proportionality factors and are both greater than 0; Evaluate the disinfection effect of the area to be tested according to the final disinfection effect coefficient T. The greater the final disinfection effect coefficient T, the better the disinfection effect of the area to be tested.

[0014] In a second aspect, the present application provides a disinfection effect detection system based on big data, adopting the following technical solution: A disinfection effect detection system based on big data, comprising: A disinfection matching degree analysis module, configured to analyze the disinfection matching degree between the disinfectant and the virus microorganisms according to the virus microorganism situation before disinfection in the area to be tested and the type of disinfectant used for disinfection in the area to be tested, and obtain the disinfection matching influence weight ratio P; A disinfection environment influence analysis module, configured to obtain the suitable environment for the disinfectant used in the area to be tested based on big data technology, and detect the environmental parameter information when the disinfectant is used in the area to be tested to judge the influence of the environment on the disinfection effect, and obtain the disinfection environment influence coefficient ART; A disinfection usage mode influence analysis module, configured to obtain the suitable disinfectant usage dose BX according to the first virus microorganism type information and the area capacity of the area to be tested, obtain the actual disinfectant usage dose information BS according to the disinfectant concentration information and the disinfection duration information when the disinfectant is used for disinfection treatment in the area to be tested, compare the actual disinfectant usage dose information BS with the suitable disinfectant usage dose BX, analyze and obtain the disinfection dose influence coefficient BJ, obtain the operation influence coefficient BF according to the disinfection operation mode information, and comprehensively obtain the disinfection usage mode influence coefficient BTH based on the disinfection dose influence coefficient BJ and the disinfection mode influence coefficient BF; A theoretical disinfection intensity analysis module, configured to comprehensively obtain the theoretical disinfection intensity coefficient W1 based on the disinfection matching influence weight ratio P, the disinfection environment influence coefficient ART, and the disinfection usage mode influence coefficient BTH; An actual disinfection intensity analysis module, configured to obtain a biological indicator and place it in the area to be tested, and obtain the actual disinfection intensity coefficient W2 according to the changes in the microorganisms cultured in the biological indicator before and after the disinfection operation; A theoretical-actual gap analysis module, configured to compare the actual disinfection intensity coefficient W2 with the theoretical disinfection intensity coefficient W1 to calculate the difference to obtain the disinfection intensity difference, compare the disinfection intensity difference with the preset disinfection intensity difference threshold, and analyze the theoretical-actual difference influence weight ratio QS; A virus microorganism residue analysis module, configured to detect the virus microorganism situation after disinfection in the area to be tested to obtain the second virus microorganism information, and determine the virus microorganism residue influence weight ratio QR based on the second virus microorganism information; The disinfection effect evaluation module is configured to obtain the final disinfection effect coefficient T of the area to be tested according to the theoretical-actual difference influence weight ratio QS and the virus and microorganism residue influence weight ratio QR, and evaluate the disinfection effect of the area to be tested according to the final disinfection effect coefficient T.

[0015] In summary, the present application includes at least one of the following beneficial technical effects: 1. By judging the disinfection environment when disinfecting the area to be tested with a disinfectant, namely temperature, humidity, and light, the influence of the disinfection environment on the disinfection effect when disinfecting the area to be tested with a disinfectant is obtained, and the detection accuracy of the disinfection environment influence coefficient ART is improved, thereby improving the detection accuracy of the disinfection effect of the area to be tested; 2. The first virus and microorganism concentration information BN is obtained by means of the first virus and microorganism type information and the first virus and microorganism quantity information of different first virus and microorganism types. The appropriate disinfectant dosage BX is obtained according to the first virus and microorganism concentration information BN and the area capacity information BQ. The actual disinfectant dosage information BS is obtained according to the disinfectant concentration information and the disinfection duration information. The actual disinfectant dosage information BS is compared and analyzed with the appropriate disinfectant dosage BX to obtain the disinfection dosage influence coefficient BJ. The operation influence coefficient BF is obtained according to the disinfection operation mode information. The disinfection usage mode influence coefficient BTH is obtained according to the disinfection dosage influence coefficient BJ and the disinfection mode influence coefficient BF, improving the detection accuracy of the disinfection usage mode influence coefficient BTH, thereby improving the detection accuracy of the disinfection effect of the area to be tested; 3. The final disinfection effect coefficient T is calculated by means of the theoretical-actual difference influence weight ratio QS and the virus and microorganism residue influence weight ratio QR, and the disinfection effect of the area to be tested is evaluated according to the final disinfection effect coefficient T, improving the detection accuracy of the disinfection effect of the area to be tested. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic flowchart mainly showing the disinfection effect detection method based on big data in this embodiment; Figure 2 It is a schematic module diagram mainly showing the disinfection effect detection system based on big data in this embodiment.

[0017] Reference numerals: 1, disinfection matching degree analysis module; 2, disinfection environment influence analysis module; 3, disinfection usage mode influence analysis module; 4, theoretical disinfection intensity analysis module; 5, actual disinfection intensity analysis module; 6, theoretical-actual gap analysis module; 7, virus and microorganism residue analysis module; 8, disinfection effect evaluation module. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] The present application will be further described in detail below with reference to the accompanying drawings.

[0019] An embodiment of the present application discloses a disinfection effect detection method based on big data.

[0020] A disinfection effect detection method based on big data includes the following steps: Refer to Figure 1 , step S1, according to the virus and microorganism situation before disinfection in the area to be tested and the type of disinfectant used for disinfection in the area to be tested, analyze the disinfection matching degree between the disinfectant and the virus and microorganism to obtain the disinfection matching influence weight ratio P. Step S1 specifically includes the following sub-steps: Step S11, detect the virus and microorganism situation before the disinfection operation in the area to be tested to obtain the first virus and microorganism information, and the first virus and microorganism information includes the first virus and microorganism type information and the first virus and microorganism quantity information of different first virus and microorganism types.

[0021] Step S12, obtain the type information of the disinfectant used for disinfection in the area to be tested to obtain the disinfectant type information.

[0022] Step S13, obtain the disinfectant-microorganism matching degree table based on big data technology. The disinfectant-microorganism matching degree table includes the virus and microorganism types that different disinfectants can eliminate and the targeted elimination intensity for different virus and microorganism types.

[0023] Step S14, search in the disinfectant-microorganism matching degree table based on the first virus and microorganism type information and the disinfectant type information to obtain multiple targeted elimination intensity information K1, K2, K3,..., K n , where n refers to the number of virus and microorganism types of the first virus and microorganism type information.

[0024] Step S15, based on the multiple targeted elimination intensity information K1, K2, K3,..., K n , calculate the disinfection matching influence weight ratio P based on the disinfection matching relationship function , where is a proportionality factor and is greater than 0.

[0025] Refer to Figure 1 , step S2, obtain the suitable environment of the disinfectant used in the area to be tested based on big data technology, and detect the environmental parameter information when the disinfectant is used in the area to be tested to judge the influence of the environment on the disinfection effect to obtain the disinfection environment influence coefficient ART. Step S2 specifically includes the following sub-steps: Step S21, detect the disinfection environment when disinfecting the area to be tested with a disinfectant to obtain environmental parameter information, where the environmental parameter information includes temperature information, humidity information, and light information.

[0026] Step S22, obtain the suitable temperature of the disinfectant used in the area to be tested to get the suitable temperature information, and obtain the suitable humidity of the disinfectant used in the area to be tested to get the suitable humidity information.

[0027] Step S23, compare the temperature information in the environmental parameters with the suitable temperature information to obtain a temperature difference, and obtain a temperature influence coefficient AW based on the temperature difference, where the larger the temperature difference, the larger the temperature influence coefficient AW.

[0028] Step S24, compare the humidity information in the environmental parameters with the suitable humidity information to obtain a humidity difference, and obtain a humidity influence coefficient AS based on the humidity difference, where the larger the humidity difference, the larger the humidity influence coefficient AS.

[0029] Step S25, based on big data technology, check whether the disinfectant used in the area to be tested is sensitive to light. If it is not sensitive, the light influence coefficient AG is obtained as 0. If it is sensitive to light, the light influence coefficient AG is obtained based on the sensitivity of the disinfectant used in the area to be tested to light and the light information. Among them, the higher the sensitivity of the disinfectant used in the area to be tested to light, the larger the light influence coefficient AG, and the stronger the light intensity information when using the disinfectant in the area to be tested, the larger the light influence coefficient AG.

[0030] In practical applications, some disinfectants (such as hydrogen peroxide) are sensitive to light and are prone to decomposition and inactivation under strong light conditions.

[0031] Step S26, synthesize the temperature influence coefficient AW, the humidity influence coefficient AS, and the light influence coefficient AG, and calculate the disinfection environment influence coefficient ART based on the environmental relationship function where a1, a2, and a3 are proportionality factors and are all greater than 0.

[0032] In specific applications, through the disinfection environment when disinfecting the area to be tested with a disinfectant, that is, temperature, humidity, and light, judge the influence of the disinfection environment when disinfecting the area to be tested with a disinfectant on the disinfection effect, and obtain the disinfection environment influence coefficient ART, which improves the detection accuracy of the disinfection environment influence coefficient ART, and further improves the detection accuracy of the disinfection effect of the area to be tested.

[0033] Refer to Figure 1, Step S3, obtain the appropriate disinfectant dosage BX based on the first virus and microorganism type information and the regional capacity of the area to be tested. Obtain the actual disinfectant dosage information BS based on the disinfectant concentration information and the disinfection duration information when disinfecting the area to be tested with the disinfectant. Compare the actual disinfectant dosage information BS with the appropriate disinfectant dosage BX, analyze to obtain the disinfection dosage influence coefficient BJ, and obtain the operation influence coefficient BF based on the disinfection operation mode information. Synthesize the disinfection dosage influence coefficient BJ and the disinfection mode influence coefficient BF to obtain the disinfection usage mode influence coefficient BTH. Step S3 specifically includes the following sub-steps: Step S31, obtain the first virus and microorganism concentration information BN based on the first virus and microorganism type information and the first virus and microorganism quantity information of different first virus and microorganism types. The more the first virus and microorganism type information, the higher the first virus and microorganism concentration information BN, and the more the first virus and microorganism quantity information of different first virus and microorganism types, the higher the first virus and microorganism concentration information BN.

[0034] Step S32, obtain the regional capacity information BQ by acquiring the regional capacity of the area to be tested.

[0035] Step S33, based on the first virus and microorganism concentration information BN and the regional capacity information BQ, calculate the appropriate disinfectant dosage BX based on the disinfectant concentration relationship function where b1 and b2 are proportionality factors and are both greater than 0.

[0036] Step S34, obtain the usage mode parameter information when disinfecting the area to be tested with the disinfectant. The usage mode parameter information includes disinfectant concentration information, disinfection duration information, and disinfection operation mode information. The disinfection operation mode information includes spraying disinfection, wiping disinfection, and soaking disinfection.

[0037] Step S35, calculate the actual disinfectant dosage information BS for the disinfectant used to disinfect the area to be tested based on the disinfectant concentration information and the disinfection duration information.

[0038] Step S36, compare the actual disinfectant dosage information BS with the appropriate disinfectant dosage BX. If the actual disinfectant dosage information BS is greater than or equal to the appropriate disinfectant dosage BX, output the disinfection dosage influence coefficient BJ as 0. If the actual disinfectant dosage information BS is less than the appropriate disinfectant dosage BX, calculate the difference between the actual disinfectant dosage information BS and the appropriate disinfectant dosage BX to obtain the disinfection dosage difference, and obtain the disinfection dosage influence coefficient BJ based on the disinfection dosage difference. The greater the disinfection dosage difference, the greater the disinfection dosage influence coefficient BJ.

[0039] Step S37, obtain the operation influence coefficient BF according to the disinfection operation mode information, where the operation influence coefficient BF for spraying disinfection, the operation influence coefficient BF for wiping disinfection, and the operation influence coefficient BF for immersion disinfection decrease in sequence.

[0040] Step S38, based on the disinfection dose influence coefficient BJ and the disinfection mode influence coefficient BF, calculate the disinfection usage mode influence coefficient BTH according to the operation relationship function where b3 and b4 are proportionality factors and are both greater than 0.

[0041] In specific applications, obtain the first virus and microorganism concentration information BN through the first virus and microorganism type information and the first virus and microorganism quantity information of different first virus and microorganism types, obtain the appropriate disinfectant usage dose BX according to the first virus and microorganism concentration information BN and the regional capacity information BQ, obtain the actual disinfectant usage dose information BS according to the disinfectant concentration information and the disinfection duration information, compare and analyze the actual disinfectant usage dose information BS with the appropriate disinfectant usage dose BX to obtain the disinfection dose influence coefficient BJ, obtain the operation influence coefficient BF according to the disinfection operation mode information, and obtain the disinfection usage mode influence coefficient BTH according to the disinfection dose influence coefficient BJ and the disinfection mode influence coefficient BF, which improves the detection accuracy of the disinfection usage mode influence coefficient BTH, and further improves the detection accuracy of the disinfection effect of the area to be tested.

[0042] Refer to Figure 1 , in step S4, comprehensively obtain the theoretical disinfection strength coefficient W1 by the comprehensive disinfection matching influence weight ratio P, the disinfection environment influence coefficient ART, and the disinfection usage mode influence coefficient BTH. Step S4 specifically includes: Comprehensively consider the comprehensive disinfection matching influence weight ratio P, the disinfection environment influence coefficient ART, and the disinfection usage mode influence coefficient BTH, and calculate the theoretical disinfection strength coefficient W1 according to the theoretical disinfection strength function where , are proportionality factors and are both greater than 0.

[0043] Refer to Figure 1 , in step S5, obtain a biological indicator and place it in the area to be tested, and obtain the actual disinfection strength coefficient W2 according to the changes of the microorganisms cultured in the biological indicator before and after the disinfection operation. Step S5 specifically includes the following sub-steps: Step S51, obtain a biological indicator, which is placed in the area to be tested before the disinfection operation in the area to be tested, where specific microorganisms such as dehydrogenase are cultured in the biological indicator.

[0044] Step S52: Obtain the microbial situation of the biological indicator before the disinfection operation in the area to be tested to get the pre-operation microbial information, where the pre-operation microbial information includes the pre-operation microbial type and the pre-operation microbial quantity.

[0045] Step S53: Detect the microbial situation of the biological indicator after the disinfection treatment in the area to be tested to get the post-operation microbial information, where the post-operation microbial information includes the post-operation microbial type and the post-operation microbial quantity.

[0046] Step S54: Compare the post-operation microbial type with the pre-operation microbial type. If the post-operation microbial type is inconsistent with the pre-operation microbial type, then obtain the microbial type influence coefficient CLT based on the difference between the post-operation microbial type and the pre-operation microbial type. The larger the difference between the post-operation microbial type and the pre-operation microbial type, the larger the microbial type influence coefficient CLT.

[0047] Step S55: Compare the post-operation microbial quantity with the pre-operation microbial quantity. If the post-operation microbial quantity is inconsistent with the pre-operation microbial quantity, then obtain the microbial quantity influence coefficient DSL based on the difference between the post-operation microbial quantity and the pre-operation microbial quantity. The larger the difference between the post-operation microbial quantity and the pre-operation microbial quantity, the larger the microbial quantity influence coefficient DSL.

[0048] Step S56: According to the microbial type influence coefficient CLT and the microbial quantity influence coefficient DSL, calculate the actual disinfection intensity coefficient W2 based on the actual disinfection intensity function where, 、 are scale factors and are both greater than 0.

[0049] Refer to Figure 1 , Step S6: Compare the actual disinfection intensity coefficient W2 with the theoretical disinfection intensity coefficient W1 to calculate the difference to get the disinfection intensity difference, and compare the disinfection intensity difference with the preset disinfection intensity difference threshold to analyze the theoretical-actual difference influence weight ratio QS. Step S6 specifically includes the following sub-steps: Step S61: Compare the actual disinfection intensity coefficient W2 with the theoretical disinfection intensity coefficient W1 to get the theoretical-actual comparison result.

[0050] Step S62: Based on the theoretical-actual comparison result, if the actual disinfection intensity coefficient W2 is less than the theoretical disinfection intensity coefficient W1, then calculate the difference between the actual disinfection intensity coefficient W2 and the theoretical disinfection intensity coefficient W1 to get the disinfection intensity difference.

[0051] Step S63: Compare the disinfection intensity difference with a preset disinfection intensity difference threshold. If the disinfection intensity difference is greater than the preset disinfection intensity difference threshold, calculate the gap value between the disinfection intensity difference and the preset disinfection intensity difference threshold to obtain the theoretical actual gap value.

[0052] Step S64: Obtain the theoretical actual difference influence weight ratio QS based on the theoretical actual gap value, where the larger the theoretical actual gap value, the larger the theoretical actual difference influence weight ratio QS.

[0053] Refer to Figure 1 , Step S7: Detect the virus and microorganism situation after disinfecting the area to be measured to obtain the second virus and microorganism information, and determine the virus and microorganism residue influence weight ratio QR based on the second virus and microorganism information. Step S7 specifically includes the following sub-steps: Step S71: Detect the virus and microorganism situation before disinfecting the area to be measured to obtain the second virus and microorganism information, where the second virus and microorganism information includes the second virus and microorganism type information and the second virus and microorganism quantity information of different second virus and microorganism types.

[0054] Step S72: Determine the virus and microorganism residue influence weight ratio QR according to the second virus and microorganism type information and the second virus and microorganism quantity information of different second virus and microorganism types. Among them, the more the second virus and microorganism type information, the larger the virus and microorganism residue influence weight ratio QR, and the more the second virus and microorganism quantity information, the larger the virus and microorganism residue influence weight ratio QR.

[0055] Refer to Figure 1 , Step S8: Obtain the final disinfection effect coefficient T of the area to be measured according to the theoretical actual difference influence weight ratio QS and the virus and microorganism residue influence weight ratio QR, and evaluate the disinfection effect of the area to be measured according to the final disinfection effect coefficient T. Step S8 specifically includes the following sub-steps: Step S81: Integrate the theoretical actual difference influence weight ratio QS and the virus and microorganism residue influence weight ratio QR, and calculate to obtain the final disinfection effect coefficient T based on the disinfection effect relationship function where 、 are scale factors and are both greater than 0.

[0056] Step S82: Evaluate the disinfection effect of the area to be measured according to the final disinfection effect coefficient T, where the larger the final disinfection effect coefficient T, the better the disinfection effect of the area to be measured.

[0057] In specific applications, if the actual disinfection intensity of the area to be tested is significantly different from the theoretical disinfection intensity, it indicates a poor disinfection effect. If the content of virus microorganisms in the area to be tested is still relatively high after the disinfection operation, it also indicates a poor disinfection effect. The final disinfection effect coefficient T is calculated through the weight ratio QS affected by the theoretical-actual difference and the weight ratio QR affected by the residual virus microorganisms. The disinfection effect of the area to be tested is evaluated based on the final disinfection effect coefficient T, which improves the detection accuracy of the disinfection effect of the area to be tested.

[0058] The embodiment of the present application also discloses a disinfection effect detection system based on big data.

[0059] Referring to Figure 2 , a disinfection effect detection system based on big data includes the following steps: A disinfection matching degree analysis module configured to analyze the disinfection matching degree between the disinfectant and virus microorganisms according to the virus microorganism situation before disinfection in the area to be tested and the type of disinfectant used for disinfection in the area to be tested, and obtain the weight ratio P affected by the disinfection matching; A disinfection environment impact analysis module configured to obtain the suitable environment of the disinfectant used in the area to be tested based on big data technology, and detect the environmental parameter information when the disinfectant is used in the area to be tested to judge the impact of the environment on the disinfection effect and obtain the disinfection environment impact coefficient ART; A disinfection usage mode impact analysis module configured to obtain the appropriate disinfectant usage dose BX according to the first virus microorganism type information and the area capacity of the area to be tested, obtain the actual disinfectant usage dose information BS according to the disinfectant concentration information and the disinfection duration information when the disinfectant is used for disinfection treatment in the area to be tested, compare the actual disinfectant usage dose information BS with the appropriate disinfectant usage dose BX, analyze and obtain the disinfection dose impact coefficient BJ, obtain the operation impact coefficient BF according to the disinfection operation mode information, and comprehensively obtain the disinfection usage mode impact coefficient BTH based on the disinfection dose impact coefficient BJ and the disinfection mode impact coefficient BF; A theoretical disinfection intensity analysis module configured to comprehensively obtain the theoretical disinfection intensity coefficient W1 based on the weight ratio P affected by the disinfection matching, the disinfection environment impact coefficient ART, and the disinfection usage mode impact coefficient BTH; An actual disinfection intensity analysis module configured to obtain a biological indicator and place it in the area to be tested, and obtain the actual disinfection intensity coefficient W2 according to the changes in the microorganisms cultured in the biological indicator before and after the disinfection operation; A theoretical-actual difference analysis module configured to compare and calculate the difference between the actual disinfection intensity coefficient W2 and the theoretical disinfection intensity coefficient W1 to obtain the disinfection intensity difference, compare the disinfection intensity difference with a preset disinfection intensity difference threshold, and analyze the weight ratio QS affected by the theoretical-actual difference; The virus and microorganism residue analysis module is configured to detect the virus and microorganism situation in the area to be tested after disinfection to obtain the second virus and microorganism information, and determine the virus and microorganism residue influence weight ratio QR based on the second virus and microorganism information; The disinfection effect evaluation module is configured to obtain the final disinfection effect coefficient T of the area to be tested according to the theoretical and actual difference influence weight ratio QS and the virus and microorganism residue influence weight ratio QR, and evaluate the disinfection effect of the area to be tested according to the final disinfection effect coefficient T.

[0060] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited accordingly. Therefore, all equivalent changes made according to the structure, shape, and principle of the present application should be covered within the protection scope of the present application.

Claims

1. A disinfection effect detection method based on big data, characterized in that: The following steps are involved: According to the virus and microorganism conditions of the tested area before disinfection and the type of disinfectant used in the tested area, the disinfection matching degree between the disinfectant and the virus and microorganism is analyzed to obtain the disinfection matching influence weight ratio P; Based on big data technology, the appropriate environment for the disinfectant used in the tested area is obtained, and the environmental parameter information when the disinfectant is used in the tested area is detected to determine the impact of the environment on the disinfection effect and obtain the disinfection environment impact coefficient ART; Obtain appropriate disinfectant dosage BX according to the first virus microorganism type information and the regional capacity of the area to be tested, obtain actual disinfectant dosage information BS according to the disinfectant concentration information and disinfection duration information when the area to be tested is disinfected with the disinfectant, compare the actual disinfectant dosage information BS with the appropriate disinfectant dosage BX, analyze and obtain the disinfection dosage influence coefficient BJ, obtain the operation influence coefficient BF according to the disinfection operation mode information, and obtain the disinfection use mode influence coefficient BTH by combining the disinfection dosage influence coefficient BJ and the disinfection mode influence coefficient BF; The theoretical disinfection intensity coefficient W1 is obtained by comprehensively considering the disinfection matching influence weight ratio P, the disinfection environment influence coefficient ART, and the disinfection use method influence coefficient BTH; Obtain a biological indicator and place it in the area to be tested, and obtain the actual disinfection intensity coefficient W2 based on the changes of the microorganisms cultured in the biological indicator before and after the disinfection operation; The actual disinfection strength coefficient W2 is compared with the theoretical disinfection strength coefficient W1 to calculate the difference to obtain the disinfection strength difference, the disinfection strength difference is compared with the preset disinfection strength difference threshold, and the theoretical actual difference influence weight ratio QS is analyzed; Detect the virus and microorganism situation of the tested area after disinfection to obtain second virus and microorganism information and determine the virus and microorganism residual influence weight ratio QR based on the second virus and microorganism information; According to the theoretical actual difference influence weight ratio QS and the virus microorganism residue influence weight ratio QR, the final disinfection effect coefficient T of the area to be tested is obtained, and the disinfection effect of the area to be tested is evaluated according to the final disinfection effect coefficient T.

2. A disinfection effect detection method based on big data according to claim 1, characterized in that: According to the virus and microorganism conditions of the tested area before disinfection and the type of disinfectant used in the tested area, the steps of analyzing the disinfection matching degree between the disinfectant and the virus and microorganism to obtain the disinfection matching influence weight ratio P specifically include: Detect the virus and microorganism conditions of the tested area before the disinfection operation to obtain first virus and microorganism information, wherein the first virus and microorganism information includes first virus and microorganism type information and first virus and microorganism quantity information of different first virus and microorganism types; Obtain the type of disinfectant used to disinfect the area to be tested to obtain disinfectant type information; Obtaining a disinfectant microbial matching degree table based on big data technology, wherein the disinfectant microbial matching degree table includes the types of viruses and microorganisms that can be eliminated by different disinfectants and the intensity of elimination for different types of viruses and microorganisms; Based on the first virus microorganism type information and the disinfectant type information, a search is performed in the disinfectant microorganism matching degree table to obtain a plurality of elimination strength information K1, K2, K3, ..., K4, K5, K6, K7, K8, K9, K10, K110, K120, K130, K140, K150, K160, K170, K180, K190, K210, K310, n , wherein n refers to the number of virus microorganism types of the first virus microorganism type information; According to the plurality of elimination strength information K1, K2, K3, ..., K n , based on the elimination matching relationship function The elimination and matching influence weight ratio P is calculated, where: is the scaling factor and is greater than 0.

3. A disinfection effect detection method based on big data according to claim 2, characterized in that: The steps of obtaining a suitable environment for the disinfectant used in the tested area based on big data technology, detecting the environmental parameter information when the disinfectant is used in the tested area, judging the influence of the environment on the disinfection effect, and obtaining the disinfection environment influence coefficient ART specifically include: Detecting the disinfection environment of the area to be tested when the disinfectant is used to disinfect the area to be tested to obtain environmental parameter information, wherein the environmental parameter information includes temperature information, humidity information and light information; Obtaining the appropriate temperature of the disinfectant used in the area to be tested to obtain appropriate temperature information, and obtaining the appropriate humidity of the disinfectant used in the area to be tested to obtain appropriate humidity information; The temperature information in the environmental parameters is compared with the suitable temperature information to obtain a temperature difference, and a temperature influence coefficient AW is obtained based on the temperature difference, wherein the greater the temperature difference, the greater the temperature influence coefficient AW; The humidity information in the environmental parameters is compared with the appropriate humidity information to obtain a humidity difference, and a humidity influence coefficient AS is obtained based on the humidity difference, wherein the greater the humidity difference, the greater the humidity influence coefficient AS; Based on big data technology, find out whether the disinfectant used in the test area is sensitive to light. If it is not sensitive, the light impact coefficient AG is 0. If it is sensitive to light, the light impact coefficient AG is obtained based on the sensitivity of the disinfectant used in the test area to light and the light information. The higher the sensitivity of the disinfectant used in the test area to light, the greater the light impact coefficient AG, and the stronger the light intensity information when the disinfectant is used in the test area, the greater the light impact coefficient AG; Comprehensive temperature influence coefficient AW, humidity influence coefficient AS and light influence coefficient AG, based on the environmental relationship function The disinfection environmental impact coefficient ART is calculated, where a1, a2, and a3 are proportional factors and are all greater than 0.

4. A disinfection effect detection method based on big data according to claim 3, characterized in that: The steps of obtaining an appropriate disinfectant dosage BX according to the first virus microorganism type information and the regional capacity of the area to be tested, obtaining actual disinfectant dosage information BS according to the disinfectant concentration information and disinfection duration information when the area to be tested is disinfected with the disinfectant, comparing the actual disinfectant dosage information BS with the appropriate disinfectant dosage BX, analyzing and obtaining a disinfection dosage influence coefficient BJ, obtaining an operation influence coefficient BF according to the disinfection operation mode information, and obtaining a disinfection usage mode influence coefficient BTH by integrating the disinfection dosage influence coefficient BJ and the disinfection mode influence coefficient BF, specifically include: The first virus microorganism concentration information BN is obtained according to the first virus microorganism type information and the first virus microorganism quantity information of different first virus microorganism types, wherein the more the first virus microorganism type information is, the higher the first virus microorganism concentration information BN is, and the more the first virus microorganism quantity information of different first virus microorganism types is, the higher the first virus microorganism concentration information BN is; Acquire the area capacity of the area to be measured to obtain the area capacity information BQ; According to the first virus microorganism concentration information BN and the regional capacity information BQ, based on the disinfectant concentration relationship function The appropriate disinfectant dosage BX is calculated, where b1 and b2 are proportional factors and are both greater than 0; Obtaining usage parameter information when the area to be tested is disinfected with a disinfectant, wherein the usage parameter information includes disinfectant concentration information, disinfection duration information, and disinfection operation mode information, wherein the disinfection operation mode information includes spraying disinfection, wiping disinfection, and immersion disinfection; Based on the disinfectant concentration information and the disinfection time information, the disinfectant dosage used when the disinfectant is used to disinfect the tested area is calculated to obtain the actual disinfectant dosage information BS; Compare the actual disinfectant dosage information BS with the appropriate disinfectant dosage BX. If the actual disinfectant dosage information BS is greater than or equal to the appropriate disinfectant dosage BX, the disinfection dosage influence coefficient BJ is output as 0. If the actual disinfectant dosage information BS is less than the appropriate disinfectant dosage BX, the difference between the actual disinfectant dosage information BS and the appropriate disinfectant dosage BX is calculated to obtain the disinfection dosage difference. Based on the disinfection dosage difference, the disinfection dosage influence coefficient BJ is obtained. The larger the disinfection dosage difference, the larger the disinfection dosage influence coefficient BJ. The operation influence coefficient BF is obtained according to the disinfection operation mode information, wherein the operation influence coefficient BF of the spraying mode disinfection, the operation influence coefficient BF of the wiping mode disinfection, and the operation influence coefficient BF of the immersion mode disinfection decrease in sequence; According to the disinfection dose influence coefficient BJ and the disinfection method influence coefficient BF, based on the operation relationship function The disinfection usage mode influence coefficient BTH is calculated, where b3 and b4 are proportional factors and are both greater than 0.

5. A disinfection effect detection method based on big data according to claim 4, characterized in that: The steps of obtaining the theoretical disinfection intensity coefficient W1 by comprehensively matching the influence weight ratio P of disinfection and sterilization, the influence coefficient ART of disinfection environment, and the influence coefficient BTH of disinfection usage method include: Comprehensive disinfection matching influence weight ratio P, disinfection environment influence coefficient ART and disinfection usage influence coefficient BTH, based on the theoretical disinfection intensity function The theoretical disinfection intensity coefficient W1 is calculated, where: , are proportional factors and are all greater than 0.

6. A disinfection effect detection method based on big data according to claim 5, characterized in that: The steps of obtaining a biological indicator and placing it in the test area, and obtaining an actual disinfection intensity coefficient W2 according to the changes of the microorganisms cultured in the biological indicator before and after the disinfection operation, specifically include: Obtaining a biological indicator, which is placed in the area to be tested before the area to be tested is disinfected; Obtaining the microorganisms cultured by the biological indicator before the disinfection operation in the test area to obtain pre-operation microbial information, wherein the pre-operation microbial information includes the pre-operation microbial type and the pre-operation microbial quantity; Detecting the microbial conditions of the biological indicator after disinfection treatment in the test area to obtain post-operation microbial information, wherein the post-operation microbial information includes post-operation microbial types and post-operation microbial quantities; The microbial type after the operation is compared with the microbial type before the operation. If the microbial type after the operation is inconsistent with the microbial type before the operation, the microbial type influence coefficient CLT is obtained based on the difference between the microbial type after the operation and the microbial type before the operation. The greater the difference between the microbial type after the operation and the microbial type before the operation, the greater the microbial type influence coefficient CLT; The number of microorganisms after the operation is compared with the number of microorganisms before the operation. If the number of microorganisms after the operation is inconsistent with the number of microorganisms before the operation, the microorganism number influence coefficient DSL is obtained based on the difference between the number of microorganisms after the operation and the number of microorganisms before the operation. The larger the difference between the number of microorganisms after the operation and the number of microorganisms before the operation, the larger the microorganism number influence coefficient DSL is. According to the microbial type influence coefficient CLT and microbial number influence coefficient DSL, based on the actual disinfection intensity function The actual disinfection intensity coefficient W2 is calculated, where: , are proportional factors and are all greater than 0.

7. A disinfection effect detection method based on big data according to claim 6, characterized in that: The steps of comparing the actual disinfection intensity coefficient W2 with the theoretical disinfection intensity coefficient W1 to calculate the difference to obtain the disinfection intensity difference, comparing the disinfection intensity difference with a preset disinfection intensity difference threshold, and analyzing the influence of the theoretical and actual difference on the weight ratio QS specifically include: Compare the actual disinfection intensity coefficient W2 with the theoretical disinfection intensity coefficient W1 to obtain a theoretical-actual comparison result; Based on the theoretical and actual comparison results, if the actual disinfection strength coefficient W2 is less than the theoretical disinfection strength coefficient W1, the difference between the actual disinfection strength coefficient W2 and the theoretical disinfection strength coefficient W1 is calculated to obtain the disinfection strength difference; The disinfection strength difference is compared with a preset disinfection strength difference threshold. If the disinfection strength difference is greater than the preset disinfection strength difference threshold, the difference between the disinfection strength difference and the preset disinfection strength difference threshold is calculated to obtain a theoretical actual difference value. Based on the theoretical-actual gap value, the theoretical-actual difference impact weight ratio QS is obtained, wherein the larger the theoretical-actual gap value is, the larger the theoretical-actual difference impact weight ratio QS is.

8. A disinfection effect detection method based on big data according to claim 7, characterized in that: The steps of detecting the virus and microorganism situation of the tested area after disinfection to obtain second virus and microorganism information and determining the virus and microorganism residual influence weight ratio QR based on the second virus and microorganism information specifically include: Detect the virus and microorganism conditions of the tested area before the disinfection operation to obtain second virus and microorganism information, wherein the second virus and microorganism information includes second virus and microorganism type information and second virus and microorganism quantity information of different second virus and microorganism types; The virus microorganism residue impact weight ratio QR is determined based on the second virus microorganism type information and the second virus microorganism quantity information of different second virus microorganism types, wherein the more second virus microorganism type information the greater the virus microorganism residue impact weight ratio QR, and the more second virus microorganism quantity information the greater the virus microorganism residue impact weight ratio QR.

9. A disinfection effect detection method based on big data according to claim 8, characterized in that: According to the theoretical actual difference influence weight ratio QS and the virus microorganism residue influence weight ratio QR, the final disinfection effect coefficient T of the area to be tested is obtained. The steps of evaluating the disinfection effect of the area to be tested according to the final disinfection effect coefficient T specifically include: Comprehensive theoretical actual difference impact weight ratio QS and virus microbial residue impact weight ratio QR, based on the disinfection effect relationship function The final disinfection effect coefficient T is calculated, where: , is the proportional factor and is greater than 0; The disinfection effect of the area to be tested is evaluated according to the final disinfection effect coefficient T, wherein the larger the final disinfection effect coefficient T is, the better the disinfection effect of the area to be tested is.

10. A disinfection effect detection system based on big data, characterized in that: The disinfection effect detection system based on big data is used to implement the disinfection effect detection method based on big data described in any one of claims 1 to 9, comprising: The disinfection matching degree analysis module is configured to analyze the disinfection matching degree between the disinfectant and the virus microorganisms to obtain the disinfection matching influence weight ratio P according to the virus microorganism situation of the tested area before disinfection and the type of disinfectant used to disinfect the tested area; The disinfection environment impact analysis module is configured to obtain the suitable environment of the disinfectant used in the test area based on big data technology, detect the environmental parameter information when the disinfectant is used in the test area, judge the impact of the environment on the disinfection effect, and obtain the disinfection environment impact coefficient ART; The disinfection usage mode impact analysis module is configured to obtain an appropriate disinfectant usage dose BX according to the first virus microorganism type information and the regional capacity of the area to be tested, obtain actual disinfectant usage dose information BS according to the disinfectant concentration information and disinfection duration information when the area to be tested is disinfected with the disinfectant, compare the actual disinfectant usage dose information BS with the appropriate disinfectant usage dose BX, analyze and obtain a disinfection dose influence coefficient BJ, obtain an operation influence coefficient BF according to the disinfection operation mode information, and obtain a disinfection usage mode influence coefficient BTH by combining the disinfection dose influence coefficient BJ and the disinfection mode influence coefficient BF; The theoretical disinfection intensity analysis module is configured to obtain the theoretical disinfection intensity coefficient W1 by comprehensively matching the impact weight ratio P of disinfection and killing, the disinfection environment impact coefficient ART, and the disinfection usage mode impact coefficient BTH; The actual disinfection intensity analysis module is configured to obtain a biological indicator and place it in the test area, and obtain an actual disinfection intensity coefficient W2 according to the changes of the microorganisms cultured in the biological indicator before and after the disinfection operation; Theoretical-actual gap analysis module, configured to compare the actual disinfection intensity coefficient W2 with the theoretical disinfection intensity coefficient W1 to calculate the difference to obtain the disinfection intensity difference, compare the disinfection intensity difference with a preset disinfection intensity difference threshold, and analyze the theoretical-actual difference impact weight ratio QS; A virus microorganism residue analysis module is configured to detect the virus microorganism situation of the tested area after disinfection to obtain second virus microorganism information and determine the virus microorganism residue influence weight ratio QR based on the second virus microorganism information; The disinfection effect evaluation module is configured to obtain the final disinfection effect coefficient T of the area to be tested based on the theoretical actual difference influence weight ratio QS and the virus microorganism residue influence weight ratio QR, and evaluate the disinfection effect of the area to be tested based on the final disinfection effect coefficient T.

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