Concrete durability detection method based on Internet of Things

Abstract

The invention relates to the technical field of concrete durability detection, in particular to a concrete durability detection method based on the Internet of Things, which comprises the following steps: receiving a durability detection instruction, confirming to-be-detected concrete according to the durability detection instruction, starting a pre-constructed concrete durability detection unit, acquiring environmental data by using a data acquisition unit, and determining the durability of the concrete according to the environmental data. The method comprises the following steps: acquiring detection data corresponding to a to-be-detected item based on environmental data, determining a fuzzy interval of the extracted to-be-detected item by utilizing a pre-constructed fuzzy database, calling a corresponding membership function, calculating membership degrees according to the membership function and the detection data, summarizing the membership degrees of to-be-detected concrete to obtain membership data, and performing data processing on the membership data. And constructing a fuzzy rule base by using the data analysis unit, calculating a durability risk value by using the fuzzy rule base, and judging concrete durability according to a pre-constructed durability evaluation standard and the durability risk value, thereby completing concrete durability detection based on the Internet of Things. According to the invention, the concrete durability detection efficiency and accuracy can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of durability detection, and in particular to a concrete durability detection method, system, electronic device and computer-readable storage medium based on the Internet of Things. Background Art

[0002] The durability of concrete directly affects the safety of infrastructure. Through regular testing, potential durability problems such as cracks, spalling, carbonization and chloride ion corrosion can be discovered in time, so that corresponding maintenance and repair measures can be taken to avoid safety accidents caused by structural failure.

[0003] The current concrete durability testing methods usually use visual inspection and chemical analysis. Professionals conduct visual inspections of concrete structures to observe surface defects such as cracks, spalling and discoloration to test the durability of concrete, and collect concrete samples to measure the chloride ion concentration inside the concrete through dissolution and chemical reagent reactions to test the durability of concrete.

[0004] Although the above method can achieve the purpose of concrete durability testing, it is highly subjective, relies on the experience and skills of the inspectors, the process is complicated and time-consuming, and the testing efficiency is low and not accurate enough. Summary of the invention

[0005] The present invention provides a concrete durability detection method based on the Internet of Things and a computer-readable storage medium, the main purpose of which is to improve the efficiency and accuracy of concrete durability detection.

[0006] To achieve the above purpose, the present invention provides a concrete durability detection method based on the Internet of Things, comprising:

[0007] receiving a durability detection instruction, confirming the concrete to be tested according to the durability detection instruction and starting a pre-built concrete durability detection unit, wherein the concrete durability detection unit is composed of a data acquisition unit and a data analysis unit, wherein the data acquisition unit includes a chloride ion concentration sensor, a temperature sensor, a humidity sensor, a strain sensor, a carbonization depth sensor and a vibration sensor;

[0008] Using a data acquisition unit to collect environmental data, wherein the environmental data includes real-time temperature, real-time humidity, load conditions, internal chloride ion concentration and carbonization depth;

[0009] Based on the data acquisition unit, a plurality of items to be detected are obtained, items to be detected are extracted from the plurality of items to be detected in sequence, and the following operations are performed on the extracted items to be detected:

[0010] Acquire detection data corresponding to the item to be detected based on the environmental data, confirm the fuzzy interval of the extracted item to be detected using a pre-built fuzzy database, call the corresponding membership function based on the fuzzy interval, and calculate the membership degree according to the membership function and the detection data;

[0011] Summarizing the membership degree of the concrete to be tested to obtain membership data, wherein the membership data includes internal chloride ion concentration, real-time temperature, real-time humidity, load conditions and carbonation depth of the concrete to be tested, and transmitting the membership data to a data analysis unit;

[0012] Use the data analysis unit to build a fuzzy rule base;

[0013] Calculate the durable risk value using the fuzzy rule base and membership data;

[0014] The durability of concrete is judged according to the pre-built durability assessment standards and durability risk values, and the concrete durability detection based on the Internet of Things is completed.

[0015] Optionally, the data acquisition unit is used to collect environmental data, wherein the environmental data includes real-time temperature, real-time humidity, load conditions, internal chloride ion concentration and carbonization depth, including:

[0016] Setting a reference temperature of the temperature sensor;

[0017] Using a chloride ion concentration sensor to obtain the internal chloride ion concentration of the concrete to be tested;

[0018] The carbonization depth of the concrete to be tested is obtained by using a carbonization depth sensor;

[0019] Measuring real-time humidity based on the humidity sensor;

[0020] Using a temperature sensor to obtain the real-time temperature and using a strain sensor to measure the real-time strain of the concrete to be tested;

[0021] The vibration sensor is used to obtain the phase offset of the concrete to be tested, the ambient vibration frequency and the sensitivity coefficient of the vibration sensor;

[0022] The load condition is calculated based on the reference temperature, real-time temperature, real-time strain, phase offset, ambient vibration frequency and vibration sensor sensitivity coefficient. The calculation formula is as follows:

[0023] F(t)=k·∈(t)·(1+sin(ωt+φ))·exp(T(t)-T0)

[0024] Among them, F(t) is the load condition at time t, t is the time of measurement, k is the sensitivity coefficient of the vibration sensor, ∈(t) is the real-time strain, ω is the ambient vibration frequency, φ is the phase offset, T(t) is the real-time temperature, and T0 is the reference temperature;

[0025] Build environmental data based on real-time humidity, load conditions, carbonation depth, real-time temperature, and internal chloride concentration.

[0026] Optionally, the obtaining the carbonization depth of the concrete to be tested by using the carbonization depth sensor includes:

[0027] The carbonation depth sensor is used to measure the carbon dioxide concentration on the concrete surface to be tested;

[0028] The carbonation depth is calculated based on the carbon dioxide concentration on the surface of the concrete to be tested. The calculation formula is as follows:

[0029]

[0030] Among them, d(t2) is the carbonation depth of the concrete to be tested, β is the parameter of the nonlinear characteristics of the carbonation reaction, t2 is the exposure time of the concrete to be tested, γ is the reference constant of the influence of environmental conditions on the carbonation rate, H(t) is the real-time humidity, and M represents the carbon dioxide concentration on the surface of the concrete to be tested.

[0031] Optionally, the step of obtaining the internal chloride ion concentration of the concrete to be tested by using a chloride ion concentration sensor includes:

[0032] Measuring the surface chloride ion concentration of the concrete to be tested based on the chloride ion concentration sensor;

[0033] The internal chloride ion concentration of the concrete to be tested is calculated based on the surface chloride ion concentration. The calculation formula is as follows:

[0034]

[0035] Wherein, C(x, t2) represents the internal chloride ion concentration of the concrete to be tested, x represents the measurement depth, C1 is the surface chloride ion concentration, and D is the diffusion coefficient of chloride ions in concrete.

[0036] Optionally, before using the pre-built fuzzy database to confirm the fuzzy interval of the extracted item to be detected, the method further includes:

[0037] Based on the fuzzy database, the internal chloride ion concentration interval, real-time temperature interval, real-time humidity interval, load condition interval and carbonization depth interval are obtained;

[0038] Performing a classification operation on the internal chloride ion concentration interval to obtain a high concentration set, a medium concentration set and a low concentration set, and summarizing the high concentration set, the medium concentration set and the low concentration set to obtain a fuzzy interval of the internal chloride ion concentration;

[0039] Performing a classification operation on the real-time temperature interval to obtain a high temperature set, a medium temperature set and a low temperature set, and summarizing the high temperature set, the medium temperature set and the low temperature set to obtain a fuzzy interval of the real-time temperature;

[0040] Performing a classification operation on the real-time humidity interval to obtain a high humidity set, a medium humidity set and a low humidity set, and summarizing the high humidity set, the medium humidity set and the low humidity set to obtain a fuzzy interval of the real-time humidity;

[0041] Performing a classification operation on the load condition interval to obtain a high load set, a medium load set and a low load set, and summarizing the high load set, the medium load set and the low load set to obtain a fuzzy interval of the load condition;

[0042] The carbonization depth interval is graded to obtain a high carbonization set, a medium carbonization set and a low carbonization set, and the high carbonization set, the medium carbonization set and the low carbonization set are summarized to obtain a fuzzy interval of the carbonization depth;

[0043] A fuzzy database is constructed based on the fuzzy interval of internal chloride ion concentration, the fuzzy interval of real-time temperature, the fuzzy interval of real-time humidity, the fuzzy interval of load conditions, and the fuzzy interval of carbonization depth.

[0044] Optionally, the using the data analysis unit to construct a fuzzy rule base includes:

[0045] A test set is obtained based on multiple test items, the test items are extracted from the test set in sequence, and the following operations are performed on the extracted test items:

[0046] Eliminating the extracted items to be tested from the set to be tested to obtain a combination set, extracting combination items from the combination set in sequence, constructing fuzzy rules based on the extracted combination items and the extracted items to be tested, and sending the fuzzy rules to a pre-constructed initial fuzzy rule base;

[0047] After confirming that the combination set is the set to be tested, returning to the step of sequentially extracting items to be tested from the set to be tested until the combination set is an empty set, and constructing the fuzzy rule base based on the initial fuzzy rule base.

[0048] Optionally, the calculating of the durable risk value by using the fuzzy rule base and the membership data includes:

[0049] A plurality of fuzzy rules are obtained based on a fuzzy rule base, fuzzy rules are extracted from the plurality of fuzzy rules in sequence, and the following operations are performed on the extracted fuzzy rules:

[0050] Obtaining the failure risk value, the first item, and the second item corresponding to the extracted fuzzy rule, confirming the first and second subordinate data in the subordinate data according to the first and second items, and calculating the activation strength of the extracted fuzzy rule according to the first and second subordinate data;

[0051] The activation intensities are aggregated to obtain an activation intensity set, and the durability risk value is calculated based on the activation intensity set.

[0052] Optionally, calculating the durable risk value according to the activation intensity set includes:

[0053] The calculation formula of the durable risk value is as follows:

[0054]

[0055] Among them, Z is the durability risk value of the concrete to be tested, i represents the i-th fuzzy rule, n represents the total number of fuzzy rules, V i is the failure risk value corresponding to the i-th fuzzy rule, J i is the activation intensity corresponding to the i-th fuzzy rule.

[0056] Optionally, judging the durability of concrete according to the pre-constructed durability assessment standard and durability risk value includes:

[0057] Based on the durability assessment standard, a high durability risk interval, a medium durability risk interval and a low durability risk interval are obtained;

[0058] Based on the low durability risk interval, obtain the upper limit of the low durability risk interval, and compare the durability risk value with the upper limit of the low durability risk interval;

[0059] If the durability risk value is less than or equal to the upper limit of the low durability risk interval, the durability of the concrete to be tested is confirmed to be high durability; otherwise, the upper limit of the medium durability risk interval of the medium durability risk interval is obtained, and the durability risk value is compared with the upper limit of the medium durability risk interval;

[0060] If the durability risk value is less than or equal to the upper limit of the medium durability risk interval, the concrete durability of the concrete to be tested is confirmed to be medium durability; otherwise, the concrete durability of the concrete to be tested is confirmed to be low-high durability.

[0061] To achieve the above object, the present invention also provides a concrete durability detection system using the Internet of Things, comprising:

[0062] An environmental data acquisition module is used to receive a durability detection instruction, confirm the concrete to be tested according to the durability detection instruction and start a pre-built concrete durability detection unit, wherein the concrete durability detection unit is composed of a data acquisition unit and a data analysis unit, wherein the data acquisition unit includes a chloride ion concentration sensor, a temperature sensor, a humidity sensor, a strain sensor, a carbonation depth sensor and a vibration sensor, and the data acquisition unit is used to collect environmental data, wherein the environmental data includes real-time temperature, real-time humidity, load conditions, internal chloride ion concentration and carbonation depth;

[0063] A membership data acquisition module is used to acquire multiple items to be detected based on a data acquisition unit, extract items to be detected from the multiple items to be detected in sequence, and perform the following operations on the extracted items to be detected: acquire detection data corresponding to the items to be detected based on environmental data, confirm the fuzzy interval of the extracted items to be detected using a pre-built fuzzy database, call the corresponding membership function based on the fuzzy interval, calculate the membership degree according to the membership function and the detection data, summarize the membership degree of the concrete to be detected, and obtain the membership data, wherein the membership data includes the internal chloride ion concentration, real-time temperature, real-time humidity, load conditions and carbonation depth of the concrete to be detected, and transmit the membership data to the data analysis unit;

[0064] A durable risk value acquisition module is used to construct a fuzzy rule base using a data analysis unit, and calculate the durable risk value using the fuzzy rule base and subordinate data;

[0065] The durability judgment module is used to judge the durability of concrete according to the pre-built durability assessment standards and durability risk values, and complete the concrete durability detection based on the Internet of Things.

[0066] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0067] A memory storing at least one instruction;

[0068] The processor executes the instructions stored in the memory to implement the above-mentioned concrete durability detection method based on the Internet of Things.

[0069] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned Internet of Things-based concrete durability detection method.

[0070] The present invention is to solve the problem described in the background technology. The present invention receives a durability detection instruction, confirms the concrete to be tested according to the durability detection instruction, and starts a pre-built concrete durability detection unit. The concrete durability detection unit is composed of a data acquisition unit and a data analysis unit, wherein the data acquisition unit includes a chloride ion concentration sensor, a temperature sensor, a humidity sensor, a strain sensor, a carbonation depth sensor and a vibration sensor. By integrating multiple sensors, various environmental data of the concrete to be tested can be collected comprehensively and in real time. The data acquisition unit is used to collect environmental data, wherein the environmental data includes real-time temperature, real-time humidity, load conditions, internal chloride ion concentration and carbonation depth. Real-time collection of environmental data helps to analyze the changes of concrete under different conditions, thereby providing more dynamic and real-time detection results, which is of great significance for timely discovery and early warning of potential problems. Based on the data acquisition unit, multiple items to be detected are obtained, and the items to be detected are extracted from the multiple items to be detected in sequence, and the following operations are performed on the extracted items to be detected: the detection data corresponding to the items to be detected are obtained based on the environmental data, the fuzzy interval of the extracted items to be detected is confirmed using the pre-built fuzzy database, the corresponding membership function is called based on the fuzzy interval, and the membership degree is calculated according to the membership function and the detection data. By processing multiple items to be detected in steps, the systematicness and orderliness of the detection process are ensured, data omission and confusion are avoided, and the detection efficiency is improved. The uncertainty and ambiguity in the detection data can be processed by using the fuzzy database and the membership function, and more accurate and reliable detection results are provided. This fuzzy logic analysis method is more suitable for complex concrete durability evaluation than traditional deterministic analysis. The membership degree of the concrete to be tested is summarized to obtain the membership data, wherein the membership data includes the internal chloride ion concentration, real-time temperature, real-time humidity, load conditions and carbonization depth of the concrete to be tested, and the membership data is transmitted to the data analysis unit. By summarizing and transmitting the membership data, the systematicness and integrity of the data are ensured, and comprehensive and accurate basic data are provided for subsequent analysis and evaluation. A fuzzy rule base is constructed using a data analysis unit. The construction of the fuzzy rule base enables the system to conduct a more intelligent and flexible assessment of the durability of concrete based on experience and rules, thereby improving the scientificity and rationality of the assessment. The durability risk value is calculated using the fuzzy rule base and the subordinate data. By combining the fuzzy rule base and the subordinate data to calculate the durability risk value, the durability risk of concrete can be quantified, providing a strong basis for decision-making and maintenance. This quantitative analysis method improves the objectivity and accuracy of the assessment. The durability of concrete is judged according to the pre-constructed durability assessment standard and the durability risk value, and the concrete durability detection based on the Internet of Things is completed. Finally, the judgment is made based on the durability assessment standard and the risk value, thereby ensuring the accuracy of the test results. Therefore, the present invention can improve the efficiency and accuracy of concrete durability detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 A schematic diagram of a flow chart of a concrete durability detection method based on the Internet of Things provided by an embodiment of the present invention;

[0072] Figure 2 A functional module diagram of a concrete durability detection system using the Internet of Things provided by an embodiment of the present invention;

[0073] Figure 3 A schematic structural diagram of an electronic device for implementing the Internet of Things-based concrete durability detection method provided by an embodiment of the present invention.

[0074] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0075] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0076] The embodiment of the present application provides a concrete durability detection method based on the Internet of Things. The execution subject of the concrete durability detection method based on the Internet of Things includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided in the embodiment of the present application. In other words, the concrete durability detection method based on the Internet of Things can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0077] Reference Figure 1 FIG. 1 is a flow chart of a concrete durability detection method based on the Internet of Things provided by an embodiment of the present invention. In this embodiment, the concrete durability detection method based on the Internet of Things includes:

[0078] S1. Receive a durability detection instruction, confirm the concrete to be tested according to the durability detection instruction and start a pre-built concrete durability detection unit, wherein the concrete durability detection unit is composed of a data acquisition unit and a data analysis unit, wherein the data acquisition unit includes a chloride ion concentration sensor, a temperature sensor, a humidity sensor, a strain sensor, a carbonization depth sensor and a vibration sensor.

[0079] It should be explained that the durability test instruction is issued by a concrete durability tester. For example, Xiao Zhang is a tester of a concrete production plant. The plant has produced a batch of concrete. In order to test the durability of concrete under the influence of various factors, the durability test instruction is initiated, wherein the various factors include: chloride ion concentration, temperature, humidity, load conditions and carbonation depth.

[0080] In the embodiment of the present invention, the durability detection unit is a unit used to detect the durability of concrete.

[0081] The key point is that the main function of the data acquisition unit is to collect various environmental data, among which the chloride ion concentration sensor is used to measure the chloride ion concentration on the surface of the concrete to be tested, the temperature sensor is used to sense the real-time temperature, the humidity sensor is used to sense the real-time humidity, the strain sensor is used to obtain the real-time strain, the carbonization depth sensor is used to detect the carbon dioxide concentration on the surface of the concrete to be tested, and the vibration sensor is used to obtain the phase offset, the environmental vibration frequency and the vibration sensor sensitivity coefficient. The above sensors are all existing technologies and will not be repeated here.

[0082] S2. Collect environmental data using a data acquisition unit, wherein the environmental data includes real-time temperature, real-time humidity, load conditions, internal chloride ion concentration, and carbonization depth.

[0083] In detail, the data acquisition unit is used to collect environmental data, wherein the environmental data includes real-time temperature, real-time humidity, load conditions, internal chloride ion concentration and carbonization depth, including:

[0084] Setting a reference temperature of the temperature sensor;

[0085] Using a chloride ion concentration sensor to obtain the internal chloride ion concentration of the concrete to be tested;

[0086] The carbonization depth of the concrete to be tested is obtained by using the carbonization depth sensor; the real-time humidity of the environmental data is measured based on the humidity sensor;

[0087] The temperature sensor is used to obtain the real-time temperature of the environmental data and the strain sensor is used to measure the real-time strain of the concrete to be tested;

[0088] The vibration sensor is used to obtain the phase offset of the concrete to be tested, the ambient vibration frequency and the sensitivity coefficient of the vibration sensor;

[0089] The load condition is calculated based on the reference temperature, real-time temperature, real-time strain, phase offset, ambient vibration frequency and vibration sensor sensitivity coefficient. The calculation formula is as follows:

[0090] F(t)=k·∈(t)·(1+sin(ωt+φ))·exp(T(t)-T0)

[0091] Among them, F(t) is the load condition at time t, t is the time of measurement, k is the sensitivity coefficient of the vibration sensor, ∈(t) is the real-time strain, ω is the ambient vibration frequency, φ is the phase offset, T(t) is the real-time temperature, and T0 is the reference temperature;

[0092] Build environmental data based on real-time humidity, load conditions, and internal chloride concentration.

[0093] It should be explained that the load condition refers to the force that the concrete to be tested bears during use. Strain is the degree of deformation of concrete under the action of external force, which is obtained by a strain sensor. For example, in the calculation of concrete load conditions, the strain sensor is buried in the concrete. When the concrete is subjected to force, the strain sensor can measure the strain of the concrete. The environmental vibration frequency refers to the vibration frequency existing in the surrounding environment, which is obtained by a vibration sensor. The environmental vibration frequency will affect the calculation of the load condition of the concrete. For example, when calculating the concrete load condition, heavy machinery and equipment running nearby will produce periodic vibrations. These vibrations will cause the concrete structure to respond at a certain frequency, thereby affecting the concrete load condition. Therefore, the environmental vibration frequency is taken into account in the calculation of the concrete load condition to obtain a more accurate result. It can be explained that the phase offset refers to the initial phase of the environmental vibration frequency. The real-time temperature refers to the ambient temperature when the concrete load condition is calculated. The reference temperature refers to the temperature value used as a reference in the calculation of the concrete load condition, which is used to measure the impact of temperature changes on material properties and load conditions.

[0094] In detail, the method of obtaining the carbonization depth of the concrete to be tested by using the carbonization depth sensor includes:

[0095] The carbonation depth sensor is used to measure the carbon dioxide concentration on the concrete surface to be tested;

[0096] The carbonation depth is calculated based on the carbon dioxide concentration on the surface of the concrete to be tested. The calculation formula is as follows:

[0097]

[0098] Among them, d(t2) is the carbonation depth of the concrete to be tested, β is the parameter of the nonlinear characteristics of the carbonation reaction, t2 is the exposure time of the concrete to be tested, γ is the reference constant of the influence of environmental conditions on the carbonation rate, H(t) is the real-time humidity, and M represents the carbon dioxide concentration on the surface of the concrete to be tested.

[0099] It can be explained that the carbonation depth of concrete refers to the depth of penetration of carbonation caused by chemical reaction into the concrete structure. The carbonation process will reduce the durability of concrete. The parameters of the nonlinear characteristics of the carbonation reaction reflect the nonlinear characteristics of the carbonation reaction process, that is, carbonation is nonlinear. The exposure time refers to the time between the concrete to be tested leaving the factory and the completion of data collection by the data acquisition unit. Real-time humidity refers to the humidity in the environment measured at the current moment. The carbon dioxide concentration on the surface of the concrete to be tested refers to the carbon dioxide concentration of the part of the concrete surface to be tested that is in contact with the atmosphere.

[0100] In detail, the method of obtaining the internal chloride ion concentration of the concrete to be tested by using the chloride ion concentration sensor includes:

[0101] Measuring the surface chloride ion concentration of the concrete to be tested based on the chloride ion concentration sensor;

[0102] The internal chloride ion concentration of the concrete to be tested is calculated based on the surface chloride ion concentration. The calculation formula is as follows:

[0103]

[0104] Wherein, C(x, t2) represents the internal chloride ion concentration of the concrete to be tested, x represents the measurement depth, C1 is the surface chloride ion concentration, and D is the diffusion coefficient of chloride ions in concrete.

[0105] It can be explained that the surface chloride ion concentration of the concrete to be tested refers to the chloride ion concentration of the part of the concrete surface that is in contact with the atmosphere. The internal chloride ion concentration of the concrete to be tested refers to the chloride ion concentration existing in the pores inside the concrete. The measurement depth refers to the distance from the surface of the concrete to be tested to the position where the measurement is made inside the concrete to be tested. The diffusion coefficient of chloride ions in concrete is a physical parameter that measures the diffusion rate of chloride ions inside concrete.

[0106] S3. Acquire multiple items to be detected based on the data acquisition unit, extract items to be detected from the multiple items to be detected in sequence, and perform the following operations on the extracted items to be detected: acquire detection data corresponding to the items to be detected based on the environmental data, use a pre-constructed fuzzy database to confirm the fuzzy interval of the extracted items to be detected, call the corresponding membership function based on the fuzzy interval, and calculate the membership degree according to the membership function and the detection data.

[0107] In detail, before using the pre-built fuzzy database to confirm the fuzzy interval of the extracted item to be detected, it also includes:

[0108] Based on the fuzzy database, the internal chloride ion concentration interval, real-time temperature interval, real-time humidity interval, load condition interval and carbonization depth interval are obtained;

[0109] Performing a classification operation on the internal chloride ion concentration interval to obtain a high concentration set, a medium concentration set and a low concentration set, and summarizing the high concentration set, the medium concentration set and the low concentration set to obtain a fuzzy interval of the internal chloride ion concentration;

[0110] Performing a classification operation on the real-time temperature interval to obtain a high temperature set, a medium temperature set and a low temperature set, and summarizing the high temperature set, the medium temperature set and the low temperature set to obtain a fuzzy interval of the real-time temperature;

[0111] Performing a classification operation on the real-time humidity interval to obtain a high humidity set, a medium humidity set and a low humidity set, and summarizing the high humidity set, the medium humidity set and the low humidity set to obtain a fuzzy interval of the real-time humidity;

[0112] Performing a classification operation on the load condition interval to obtain a high load set, a medium load set and a low load set, and summarizing the high load set, the medium load set and the low load set to obtain a fuzzy interval of the load condition;

[0113] The carbonization depth interval is graded to obtain a high carbonization set, a medium carbonization set and a low carbonization set, and the high carbonization set, the medium carbonization set and the low carbonization set are summarized to obtain a fuzzy interval of the carbonization depth;

[0114] A fuzzy database is constructed based on the fuzzy interval of internal chloride ion concentration, the fuzzy interval of real-time temperature, the fuzzy interval of real-time humidity, the fuzzy interval of load conditions, and the fuzzy interval of carbonization depth.

[0115] Explainably, the grading operation refers to dividing the data range of the detection data. Exemplarily, the range of the internal chloride ion concentration interval is 0% to 1%, the low concentration set is 0% to 0.2%, the medium concentration set is 0.2% to 0.5%, and the high concentration set is 0.5% to 1%. The range of the carbonization depth interval is 0mm to 50mm, the low carbonization set is 0mm to 20mm, the medium carbonization set is 15mm to 35mm, and the high carbonization set is 30mm to 50mm. The fuzzy interval is the interval obtained by summarizing the three sets after the division is completed, and each set in the fuzzy interval corresponds to a membership function.

[0116] It should be explained that the membership function is a function for calculating the membership of the test data. The membership is a value, which indicates the degree of membership of the test data in the corresponding set of the fuzzy interval. Through the membership function, the membership of each test data can be calculated. The membership degree indicates the degree to which a test data belongs to a certain set in the fuzzy interval. The membership degree is represented by a numerical value ranging from 0 to 1. A value of 0 indicates that it does not belong to the corresponding set in the fuzzy interval at all, a value of 1 indicates that it completely belongs to the corresponding set in the fuzzy interval, and a value between 0 and 1 indicates that it partially belongs to the corresponding set in the fuzzy interval.

[0117] Exemplarily, the membership function corresponding to the high carbonization set of carbonization depth is:

[0118]

[0119] The membership function corresponding to the carbonization set is:

[0120]

[0121] The membership function corresponding to the low-carbonization set is:

[0122]

[0123] Where d(t2) is the carbonization depth.

[0124] For example, assuming that the carbonization depth is 20 mm, the membership function corresponding to the low carbonization set is substituted into the membership function to obtain a membership degree of 0.099, the membership function corresponding to the medium carbonization set is substituted into the membership function to obtain a membership degree of 0.092, and the membership function corresponding to the high carbonization set is substituted into the membership function to obtain a membership degree of 0.0003.

[0125] S4. Summarize the membership of the concrete to be tested to obtain membership data, wherein the membership data includes chloride ion concentration, temperature, humidity, load conditions and carbonation depth of the concrete to be tested, and transmit the membership data to a data analysis unit, and use the data analysis unit to build a fuzzy rule base.

[0126] Interpretable, membership data refers to a data set obtained by integrating the membership calculated based on the membership function for the detection data. The data analysis unit is a unit for analyzing the detection data.

[0127] In detail, the method of constructing a fuzzy rule base using a data analysis unit includes:

[0128] A test set is obtained based on multiple test items, the test items are extracted from the test set in sequence, and the following operations are performed on the extracted test items:

[0129] Eliminating the extracted items to be tested from the set to be tested to obtain a combination set, extracting combination items from the combination set in sequence, constructing fuzzy rules based on the extracted combination items and the extracted items to be tested, and sending the fuzzy rules to a pre-constructed initial fuzzy rule base;

[0130] After confirming that the combination set is the set to be tested, returning to the step of sequentially extracting items to be tested from the set to be tested until the combination set is an empty set, and constructing the fuzzy rule base based on the initial fuzzy rule base.

[0131] It should be explained that the items to be tested are the data of various elements of the concrete to be tested, wherein the data of various elements include: real-time temperature, real-time humidity, load conditions, internal chloride ion concentration and carbonation depth. The set to be tested is the set of data of various elements of the concrete to be tested. The combination set is the set of element data remaining in the set to be tested after taking out one element data from the set to be tested. For example, the real-time temperature is taken out from the set to be tested, and the remaining real-time humidity, load conditions, internal chloride ion concentration and carbonation depth are the combination set. The combination item is the item extracted from the combination set. For example, the real-time temperature is taken out from the set to be tested, and the remaining items in the set to be tested are summarized as the combination set. Then the real-time humidity is extracted from the combination set, and the real-time humidity is the extracted combination item. The real-time temperature and the real-time humidity are combined to construct a fuzzy rule. Fuzzy rules are rules constructed based on the items to be tested and the combination items for judging the influence of the items to be tested and the combination items on the durability of concrete. Failure risk refers to the use of fuzzy logic to evaluate the risk of failure of concrete durability under specific conditions.

[0132] Exemplarily, the fuzzy rules of internal chloride ion concentration and real-time humidity are constructed: if the internal chloride ion concentration is in the high concentration set and the real-time humidity is in the high humidity set, then the failure risk is high, so the corresponding failure risk value is 0.8. If the internal chloride ion concentration is in the high concentration set and the real-time humidity is in the medium humidity set, then the failure risk is high, so the corresponding failure risk value is 0.8. If the internal chloride ion concentration is in the high concentration set and the real-time humidity is in the low humidity set, then the failure risk is medium, so the corresponding failure risk value is 0.5. If the internal chloride ion concentration is in the medium concentration set and the real-time humidity is in the high humidity set, then the failure risk is high, so the corresponding failure risk value is 0.8. If the internal chloride ion concentration is in the medium concentration set and the real-time humidity is in the medium humidity set, then the failure risk is medium, so the corresponding failure risk value is 0.5. If the internal chloride ion concentration is in the medium concentration set and the real-time humidity is in the low humidity set, then the failure risk is low, so the corresponding failure risk value is 0.2. If the internal chloride ion concentration is in the low concentration set and the real-time humidity is in the high humidity set, then the failure risk is medium, so the corresponding failure risk value is 0.5. If the internal chloride ion concentration is in the low concentration set and the real-time humidity is in the medium humidity set, then the failure risk is low, so the corresponding failure risk value is 0.2. If the internal chloride ion concentration is in the low concentration set and the real-time humidity is in the low humidity set, then the failure risk is low, so the corresponding failure risk value is 0.2.

[0133] S5. Calculate the durability risk value using the fuzzy rule base and membership data.

[0134] In detail, the calculation of the durable risk value using the fuzzy rule base and the membership data includes:

[0135] A plurality of fuzzy rules are obtained based on a fuzzy rule base, fuzzy rules are extracted from the plurality of fuzzy rules in sequence, and the following operations are performed on the extracted fuzzy rules:

[0136] Obtaining the failure risk value, the first item, and the second item corresponding to the extracted fuzzy rule, confirming the first and second subordinate data in the subordinate data according to the first and second items, and calculating the activation strength of the extracted fuzzy rule according to the first and second subordinate data;

[0137] The activation intensities are aggregated to obtain an activation intensity set, and the durability risk value is calculated based on the activation intensity set.

[0138] It can be understood that the fuzzy rule base is a collection of all fuzzy rules. The failure risk value is a pre-constructed value corresponding to the failure risk, which is used to describe the possibility of durability failure of concrete under given conditions. The durability risk value is used to evaluate the risk level of durability of the concrete under various influencing factors, including internal chloride ion concentration, real-time temperature, real-time humidity, load conditions and carbonation depth. The durability risk value is an important value for detecting the durability of concrete.

[0139] Explainably, the first item is the first element in the fuzzy rule, and the second item is the second element in the fuzzy rule. For example, the fuzzy rule is: if the detection data is in the low concentration set of the internal chloride ion concentration and the detection data is in the low humidity set of the real-time humidity, then the corresponding failure risk value of this fuzzy rule is 0.2. The internal chloride ion concentration is the first item, and the real-time humidity is the second item. The first membership data is the data calculated by the corresponding membership function for the first item, and the second membership data is the data calculated by the corresponding membership function for the second item. The activation strength of the fuzzy rule is the degree of satisfaction of each fuzzy rule under given input conditions. The activation strength of the fuzzy rule is represented by a value between 0 and 1, where 0 means that the rule is not satisfied at all and 1 means that the rule is completely satisfied. Exemplarily, the fuzzy rule is: if the internal chloride ion concentration is in the low concentration set and the real-time humidity is in the medium humidity set, then the failure risk value is 0.2. Assuming that the internal chloride ion concentration of the concrete to be tested has a membership of 0.12 in the low concentration set and the real-time humidity has a membership of 0.4 in the medium humidity set, the activation degree of this fuzzy rule is min{0.12,0.4}=0.12.

[0140] In detail, the calculation of the durable risk value according to the activation intensity set includes:

[0141] The calculation formula of the durable risk value is as follows:

[0142]

[0143] Among them, Z is the durability risk value of the concrete to be tested, i represents the i-th fuzzy rule, n represents the total number of fuzzy rules, V i is the failure risk value corresponding to the i-th fuzzy rule, J i is the activation intensity corresponding to the i-th fuzzy rule.

[0144] Interpretable, the activation strength set is the set of activation strengths of the fuzzy rules.

[0145] Exemplarily, assume fuzzy rule one: if the internal chloride ion concentration is in the high concentration set and the real-time temperature is in the high temperature set, then the failure risk value is 0.8. Fuzzy rule two: if the internal chloride ion concentration is in the medium concentration set and the real-time temperature is in the medium temperature set, then the failure risk value is 0.5. Fuzzy rule three: if the internal chloride ion concentration is in the low concentration set and the real-time temperature is in the low temperature set, then the failure risk value is 0.2. The internal chloride ion concentration of the concrete to be tested is 0.4%, and the real-time temperature is 30°C. Based on the membership function, the data to be tested is transformed, and the membership of the internal chloride ion concentration in the medium concentration set is 0.8, the membership of the internal chloride ion concentration in the high concentration set is 0.2, and the membership of the internal chloride ion concentration in the low concentration set is 0.0. The membership of the real-time temperature in the high temperature set is 0.5, the membership of the real-time temperature in the medium temperature set is 0.5, and the membership of the real-time temperature in the low temperature set is 0.0. The activation strength of the fuzzy rule is calculated based on the membership. The activation strength of fuzzy rule 1 is min{0.2, 0.5} = 0.2, the activation strength of fuzzy rule 2 is min{0.8, 0.5} = 0.5, and the activation strength of fuzzy rule 3 is min{0.0, 0.0} = 0. Substitute the activation strength and failure risk value of the fuzzy rules into the durable risk value calculation formula to obtain the durable risk value of the concrete to be tested.

[0146] S6. Determine the durability of concrete according to the pre-built durability assessment standard and durability risk value, and complete the concrete durability detection based on the Internet of Things.

[0147] In detail, judging the durability of concrete according to the pre-constructed durability assessment standard and durability risk value includes:

[0148] Based on the durability assessment standard, a high durability risk interval, a medium durability risk interval and a low durability risk interval are obtained;

[0149] Based on the low durability risk interval, obtain the upper limit of the low durability risk interval, and compare the durability risk value with the upper limit of the low durability risk interval;

[0150] If the durability risk value is less than or equal to the upper limit of the low durability risk interval, the durability of the concrete to be tested is confirmed to be high durability; otherwise, the upper limit of the medium durability risk interval of the medium durability risk interval is obtained, and the durability risk value is compared with the upper limit of the medium durability risk interval;

[0151] If the durability risk value is less than or equal to the upper limit of the medium durability risk interval, the concrete durability of the concrete to be tested is confirmed to be medium durability; otherwise, the concrete durability of the concrete to be tested is confirmed to be low-high durability.

[0152] Explainably, the durability assessment standard is a standard used to assess the durability of concrete, and the durability assessment standard is divided into three durability assessment intervals. For example, the low durability risk interval is: [0, 0.2], the medium durability risk interval is: (0.2, 0.5], and the high durability risk interval is: (0.5, 1.0].

[0153] Specifically, in an embodiment of the invention, the environmental data of the concrete to be tested is collected by a data acquisition unit, and the environmental data includes: real-time temperature, real-time humidity, load conditions, internal chloride ion concentration and carbonation depth. The environmental data is transmitted to the corresponding fuzzy interval, and the fuzzy interval is obtained through a hierarchical operation to obtain three sets, each set has a corresponding membership function, and the environmental data is calculated by the membership function in each set to obtain the membership of each set, and the activation strength of the fuzzy rules is calculated based on the membership to obtain the activation strength of each fuzzy rule. The activation strength of each fuzzy rule is calculated by the calculation formula of the durable risk value to obtain the durable risk value. The durable risk value is evaluated based on the durability evaluation interval to obtain the durability of the concrete to be tested. The durability detection of concrete based on the Internet of Things is completed.

[0154] The present invention is to solve the problem described in the background technology. The present invention receives a durability detection instruction, confirms the concrete to be tested according to the durability detection instruction, and starts a pre-built concrete durability detection unit. The concrete durability detection unit is composed of a data acquisition unit and a data analysis unit, wherein the data acquisition unit includes a chloride ion concentration sensor, a temperature sensor, a humidity sensor, a strain sensor, a carbonation depth sensor and a vibration sensor. By integrating multiple sensors, various environmental data of the concrete to be tested can be collected comprehensively and in real time. The data acquisition unit is used to collect environmental data, wherein the environmental data includes real-time temperature, real-time humidity, load conditions, internal chloride ion concentration and carbonation depth. Real-time collection of environmental data helps to analyze the changes of concrete under different conditions, thereby providing more dynamic and real-time detection results, which is of great significance for timely discovery and early warning of potential problems. Based on the data acquisition unit, multiple items to be detected are obtained, and the items to be detected are extracted from the multiple items to be detected in sequence, and the following operations are performed on the extracted items to be detected: the detection data corresponding to the items to be detected are obtained based on the environmental data, the fuzzy interval of the extracted items to be detected is confirmed using the pre-built fuzzy database, the corresponding membership function is called based on the fuzzy interval, and the membership degree is calculated according to the membership function and the detection data. By processing multiple items to be detected in steps, the systematicness and orderliness of the detection process are ensured, data omission and confusion are avoided, and the detection efficiency is improved. The uncertainty and ambiguity in the detection data can be processed by using the fuzzy database and the membership function, and more accurate and reliable detection results are provided. This fuzzy logic analysis method is more suitable for complex concrete durability evaluation than traditional deterministic analysis. The membership degree of the concrete to be tested is summarized to obtain the membership data, wherein the membership data includes the internal chloride ion concentration, real-time temperature, real-time humidity, load conditions and carbonization depth of the concrete to be tested, and the membership data is transmitted to the data analysis unit. By summarizing and transmitting the membership data, the systematicness and integrity of the data are ensured, and comprehensive and accurate basic data are provided for subsequent analysis and evaluation. A fuzzy rule base is constructed using a data analysis unit. The construction of the fuzzy rule base enables the system to conduct a more intelligent and flexible assessment of the durability of concrete based on experience and rules, thereby improving the scientificity and rationality of the assessment. The durability risk value is calculated using the fuzzy rule base and the subordinate data. By combining the fuzzy rule base and the subordinate data to calculate the durability risk value, the durability risk of concrete can be quantified, providing a strong basis for decision-making and maintenance. This quantitative analysis method improves the objectivity and accuracy of the assessment. The durability of concrete is judged according to the pre-constructed durability assessment standard and the durability risk value, and the concrete durability detection based on the Internet of Things is completed. Finally, the judgment is made based on the durability assessment standard and the risk value, thereby ensuring the accuracy of the test results. Therefore, the present invention can improve the efficiency and accuracy of concrete durability detection.

[0155] like Figure 2, which is a functional module diagram of a concrete durability detection system using the Internet of Things provided by one embodiment of the present invention.

[0156] The concrete durability detection system 100 using the Internet of Things of the present invention can be installed in an electronic device. According to the functions to be implemented, the concrete durability detection system 100 using the Internet of Things can include an environmental data acquisition module 101, a subordinate data acquisition module 102, a durability risk value acquisition module 103 and a durability judgment module 104. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0157] The environmental data acquisition module 101 is used to receive a durability detection instruction, confirm the concrete to be tested according to the durability detection instruction and start the pre-built concrete durability detection unit, wherein the concrete durability detection unit is composed of a data acquisition unit and a data analysis unit, wherein the data acquisition unit includes a chloride ion concentration sensor, a temperature sensor, a humidity sensor, a strain sensor, a carbonation depth sensor and a vibration sensor, and the data acquisition unit is used to collect environmental data, wherein the environmental data includes real-time temperature, real-time humidity, load conditions, internal chloride ion concentration and carbonation depth;

[0158] The subject data acquisition module 102 is used to acquire multiple items to be detected based on the data acquisition unit, extract items to be detected from the multiple items to be detected in sequence, and perform the following operations on the extracted items to be detected: acquire detection data corresponding to the items to be detected based on the environmental data, confirm the fuzzy interval of the extracted items to be detected using a pre-built fuzzy database, call the corresponding membership function based on the fuzzy interval, calculate the membership degree according to the membership function and the detection data, summarize the membership degree of the concrete to be tested, and obtain the membership data, wherein the membership data includes the internal chloride ion concentration, real-time temperature, real-time humidity, load conditions and carbonization depth of the concrete to be tested, and transmit the membership data to the data analysis unit;

[0159] The durable risk value acquisition module 103 is used to construct a fuzzy rule base using a data analysis unit, and calculate the durable risk value using the fuzzy rule base and the subordinate data;

[0160] The durability judgment module 104 is used to judge the durability of concrete according to the pre-built durability evaluation standard and durability risk value, and complete the concrete durability detection based on the Internet of Things.

[0161] In detail, each module in the concrete durability detection system 100 using the Internet of Things in the embodiment of the present invention is used in the same manner as described above. Figure 1The same technical means are used as the concrete durability detection method based on the Internet of Things described in the text, and can produce the same technical effects, so I will not go into details here.

[0162] like Figure 3 , which is a schematic diagram of the structure of an electronic device for implementing a concrete durability detection method based on the Internet of Things provided by an embodiment of the present invention.

[0163] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a concrete durability detection method program based on the Internet of Things.

[0164] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. The memory 11 may be an internal storage unit of the electronic device 1 in some embodiments, such as a mobile hard disk of the electronic device 1. The memory 11 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Further, the memory 11 also includes an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the concrete durability detection method program based on the Internet of Things, but also be used to temporarily store data that has been output or is to be output.

[0165] The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules (such as a concrete durability detection method program based on the Internet of Things, etc.) stored in the memory 11, and calls data stored in the memory 11 to execute various functions of the electronic device 1 and process data.

[0166] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0167] Figure 3 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 3 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0168] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, etc. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0169] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0170] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0171] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0172] The program of the concrete durability detection method based on the Internet of Things stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve:

[0173] receiving a durability detection instruction, confirming the concrete to be tested according to the durability detection instruction and starting a pre-built concrete durability detection unit, wherein the concrete durability detection unit is composed of a data acquisition unit and a data analysis unit, wherein the data acquisition unit includes a chloride ion concentration sensor, a temperature sensor, a humidity sensor, a strain sensor, a carbonization depth sensor and a vibration sensor;

[0174] Using a data acquisition unit to collect environmental data, wherein the environmental data includes real-time temperature, real-time humidity, load conditions, internal chloride ion concentration and carbonization depth;

[0175] Based on the data acquisition unit, a plurality of items to be detected are obtained, items to be detected are extracted from the plurality of items to be detected in sequence, and the following operations are performed on the extracted items to be detected:

[0176] Acquire detection data corresponding to the item to be detected based on the environmental data, confirm the fuzzy interval of the extracted item to be detected using a pre-built fuzzy database, call the corresponding membership function based on the fuzzy interval, and calculate the membership degree according to the membership function and the detection data;

[0177] Summarizing the membership degree of the concrete to be tested to obtain membership data, wherein the membership data includes internal chloride ion concentration, real-time temperature, real-time humidity, load conditions and carbonation depth of the concrete to be tested, and transmitting the membership data to a data analysis unit;

[0178] Use the data analysis unit to build a fuzzy rule base;

[0179] Calculate the durable risk value using the fuzzy rule base and membership data;

[0180] The durability of concrete is judged according to the pre-built durability assessment standards and durability risk values, and the concrete durability detection based on the Internet of Things is completed.

[0181] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0182] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0183] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement:

[0184] receiving a durability detection instruction, confirming the concrete to be tested according to the durability detection instruction and starting a pre-built concrete durability detection unit, wherein the concrete durability detection unit is composed of a data acquisition unit and a data analysis unit, wherein the data acquisition unit includes a chloride ion concentration sensor, a temperature sensor, a humidity sensor, a strain sensor, a carbonization depth sensor and a vibration sensor;

[0185] Using a data acquisition unit to collect environmental data, wherein the environmental data includes real-time temperature, real-time humidity, load conditions, internal chloride ion concentration and carbonization depth;

[0186] Based on the data acquisition unit, a plurality of items to be detected are obtained, items to be detected are extracted from the plurality of items to be detected in sequence, and the following operations are performed on the extracted items to be detected:

[0187] Acquire detection data corresponding to the item to be detected based on the environmental data, confirm the fuzzy interval of the extracted item to be detected using a pre-built fuzzy database, call the corresponding membership function based on the fuzzy interval, and calculate the membership degree according to the membership function and the detection data;

[0188] Summarizing the membership degree of the concrete to be tested to obtain membership data, wherein the membership data includes internal chloride ion concentration, real-time temperature, real-time humidity, load conditions and carbonation depth of the concrete to be tested, and transmitting the membership data to a data analysis unit;

[0189] Using data analysis unit to build fuzzy rule base;

[0190] Calculate the durable risk value using the fuzzy rule base and membership data;

[0191] The durability of concrete is judged according to the pre-built durability assessment standards and durability risk values, and the concrete durability detection based on the Internet of Things is completed.

[0192] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative, and actual implementation may have other division methods.

[0193] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0194] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0195] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0196] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the system claim can also be implemented by one unit or device through software or hardware. The second and other words are used to indicate names, but not to indicate any particular order.

[0197] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A concrete durability detection method based on the Internet of Things, characterized in that: The method comprises: receiving a durability detection instruction, confirming the concrete to be tested according to the durability detection instruction and starting a pre-built concrete durability detection unit, wherein the concrete durability detection unit is composed of a data acquisition unit and a data analysis unit, wherein the data acquisition unit includes a chloride ion concentration sensor, a temperature sensor, a humidity sensor, a strain sensor, a carbonization depth sensor and a vibration sensor; Using a data acquisition unit to collect environmental data, wherein the environmental data includes real-time temperature, real-time humidity, load conditions, internal chloride ion concentration and carbonization depth; Based on the data acquisition unit, a plurality of items to be detected are obtained, items to be detected are extracted from the plurality of items to be detected in sequence, and the following operations are performed on the extracted items to be detected: Acquire detection data corresponding to the item to be detected based on the environmental data, confirm the fuzzy interval of the extracted item to be detected using a pre-built fuzzy database, call the corresponding membership function based on the fuzzy interval, and calculate the membership degree according to the membership function and the detection data; Summarizing the membership degree of the concrete to be tested to obtain membership data, wherein the membership data includes internal chloride ion concentration, real-time temperature, real-time humidity, load conditions and carbonation depth of the concrete to be tested, and transmitting the membership data to a data analysis unit; Use the data analysis unit to build a fuzzy rule base; Calculate the durable risk value using the fuzzy rule base and membership data; The durability of concrete is judged according to the pre-built durability assessment standards and durability risk values, and the concrete durability detection based on the Internet of Things is completed.

2. The method for detecting concrete durability based on the Internet of Things according to claim 1, characterized in that: The data acquisition unit is used to collect environmental data, wherein the environmental data includes real-time temperature, real-time humidity, load conditions, internal chloride ion concentration and carbonization depth, including: Setting a reference temperature of the temperature sensor; Using a chloride ion concentration sensor to obtain the internal chloride ion concentration of the concrete to be tested; The carbonization depth of the concrete to be tested is obtained by using a carbonization depth sensor; Measuring real-time humidity based on the humidity sensor; Using a temperature sensor to obtain the real-time temperature and using a strain sensor to measure the real-time strain of the concrete to be tested; The vibration sensor is used to obtain the phase offset of the concrete to be tested, the ambient vibration frequency and the sensitivity coefficient of the vibration sensor; The load condition is calculated based on the reference temperature, real-time temperature, real-time strain, phase offset, ambient vibration frequency and vibration sensor sensitivity coefficient. The calculation formula is as follows: F(y)=k·∈(t)·(1+sin(ωt+φ))·exp(T(t)-T0) Among them, F(t) is the load condition at time t, t is the time of measurement, k is the sensitivity coefficient of the vibration sensor, ∈(t) is the real-time strain, ω is the ambient vibration frequency, φ is the phase offset, T(t) is the real-time temperature, and T0 is the reference temperature; Build environmental data based on real-time humidity, load conditions, carbonation depth, real-time temperature, and internal chloride concentration.

3. The method for detecting concrete durability based on the Internet of Things according to claim 2, characterized in that: The method of obtaining the carbonization depth of the concrete to be tested by using the carbonization depth sensor includes: The carbonation depth sensor is used to measure the carbon dioxide concentration on the concrete surface to be tested; The carbonation depth is calculated based on the carbon dioxide concentration on the surface of the concrete to be tested. The calculation formula is as follows: Among them, d(t2) is the carbonation depth of the concrete to be tested, β is the parameter of the nonlinear characteristics of the carbonation reaction, t2 is the exposure time of the concrete to be tested, γ is the reference constant of the influence of environmental conditions on the carbonation rate, H(t) is the real-time humidity, and M represents the carbon dioxide concentration on the surface of the concrete to be tested.

4. The method for detecting concrete durability based on the Internet of Things according to claim 3, characterized in that: The method of using a chloride ion concentration sensor to obtain the internal chloride ion concentration of the concrete to be tested includes: Measuring the surface chloride ion concentration of the concrete to be tested based on the chloride ion concentration sensor; The internal chloride ion concentration of the concrete to be tested is calculated based on the surface chloride ion concentration. The calculation formula is as follows: Among them, C(x,t2) represents the internal chloride ion concentration of the concrete to be tested, x represents the measurement depth, C1 is the surface chloride ion concentration, and D is the diffusion coefficient of chloride ions in concrete.

5. The method for detecting concrete durability based on the Internet of Things according to claim 4, characterized in that: Before the fuzzy interval of the extracted item to be detected is confirmed by using the pre-built fuzzy database, the method further includes: Based on the fuzzy database, the internal chloride ion concentration interval, real-time temperature interval, real-time humidity interval, load condition interval and carbonization depth interval are obtained; Performing a classification operation on the internal chloride ion concentration interval to obtain a high concentration set, a medium concentration set and a low concentration set, and summarizing the high concentration set, the medium concentration set and the low concentration set to obtain a fuzzy interval of the internal chloride ion concentration; Performing a classification operation on the real-time temperature interval to obtain a high temperature set, a medium temperature set and a low temperature set, and summarizing the high temperature set, the medium temperature set and the low temperature set to obtain a fuzzy interval of the real-time temperature; Performing a classification operation on the real-time humidity interval to obtain a high humidity set, a medium humidity set and a low humidity set, and summarizing the high humidity set, the medium humidity set and the low humidity set to obtain a fuzzy interval of the real-time humidity; Performing a classification operation on the load condition interval to obtain a high load set, a medium load set and a low load set, and summarizing the high load set, the medium load set and the low load set to obtain a fuzzy interval of the load condition; The carbonization depth interval is graded to obtain a high carbonization set, a medium carbonization set and a low carbonization set, and the high carbonization set, the medium carbonization set and the low carbonization set are summarized to obtain a fuzzy interval of the carbonization depth; A fuzzy database is constructed based on the fuzzy interval of internal chloride ion concentration, the fuzzy interval of real-time temperature, the fuzzy interval of real-time humidity, the fuzzy interval of load conditions, and the fuzzy interval of carbonization depth.

6. The method for detecting concrete durability based on the Internet of Things according to claim 5, characterized in that: The method of using the data analysis unit to construct a fuzzy rule base includes: A test set is obtained based on multiple test items, the test items are extracted from the test set in sequence, and the following operations are performed on the extracted test items: Eliminating the extracted items to be tested from the set to be tested to obtain a combination set, extracting combination items from the combination set in sequence, constructing fuzzy rules based on the extracted combination items and the extracted items to be tested, and sending the fuzzy rules to a pre-constructed initial fuzzy rule base; After confirming that the combination set is the set to be tested, returning to the step of sequentially extracting items to be tested from the set to be tested until the combination set is an empty set, and constructing the fuzzy rule base based on the initial fuzzy rule base.

7. The method for detecting concrete durability based on the Internet of Things according to claim 6, characterized in that: The method of calculating the durable risk value by using the fuzzy rule base and the subordinate data includes: A plurality of fuzzy rules are obtained based on a fuzzy rule base, fuzzy rules are extracted from the plurality of fuzzy rules in sequence, and the following operations are performed on the extracted fuzzy rules: Obtaining the failure risk value, the first item, and the second item corresponding to the extracted fuzzy rule, confirming the first and second subordinate data in the subordinate data according to the first and second items, and calculating the activation strength of the extracted fuzzy rule according to the first and second subordinate data; The activation intensities are aggregated to obtain an activation intensity set, and the durability risk value is calculated based on the activation intensity set.

8. The method for detecting concrete durability based on the Internet of Things according to claim 7, characterized in that: The calculating of the durability risk value according to the activation intensity set includes: The calculation formula of the durable risk value is as follows: Among them, Z is the durability risk value of the concrete to be tested, i represents the i-th fuzzy rule, n represents the total number of fuzzy rules, V i is the failure risk value corresponding to the i-th fuzzy rule, J i is the activation intensity corresponding to the i-th fuzzy rule.

9. The method for detecting concrete durability based on the Internet of Things according to claim 8, characterized in that: The determining of the durability of concrete according to the pre-constructed durability assessment standard and durability risk value includes: Based on the durability assessment standard, a high durability risk interval, a medium durability risk interval and a low durability risk interval are obtained; Obtain the upper limit of the low durability risk interval based on the low durability risk interval, and compare the durability risk value with the upper limit of the low durability risk interval; If the durability risk value is less than or equal to the upper limit of the low durability risk interval, the durability of the concrete to be tested is confirmed to be high durability; otherwise, the upper limit of the medium durability risk interval of the medium durability risk interval is obtained, and the durability risk value is compared with the upper limit of the medium durability risk interval; If the durability risk value is less than or equal to the upper limit of the medium durability risk interval, the concrete durability of the concrete to be tested is confirmed to be medium durability; otherwise, the concrete durability of the concrete to be tested is confirmed to be low-high durability.

10. A concrete durability detection system based on the Internet of Things, characterized in that: The system comprises: An environmental data acquisition module is used to receive a durability detection instruction, confirm the concrete to be tested according to the durability detection instruction and start a pre-built concrete durability detection unit, wherein the concrete durability detection unit is composed of a data acquisition unit and a data analysis unit, wherein the data acquisition unit includes a chloride ion concentration sensor, a temperature sensor, a humidity sensor, a strain sensor, a carbonation depth sensor and a vibration sensor, and the data acquisition unit is used to collect environmental data, wherein the environmental data includes real-time temperature, real-time humidity, load conditions, internal chloride ion concentration and carbonation depth; A membership data acquisition module is used to acquire multiple items to be detected based on a data acquisition unit, extract items to be detected from the multiple items to be detected in sequence, and perform the following operations on the extracted items to be detected: acquire detection data corresponding to the items to be detected based on environmental data, confirm the fuzzy interval of the extracted items to be detected using a pre-built fuzzy database, call the corresponding membership function based on the fuzzy interval, calculate the membership degree according to the membership function and the detection data, summarize the membership degree of the concrete to be detected, and obtain the membership data, wherein the membership data includes the internal chloride ion concentration, real-time temperature, real-time humidity, load conditions and carbonation depth of the concrete to be detected, and transmit the membership data to the data analysis unit; A durable risk value acquisition module is used to construct a fuzzy rule base using a data analysis unit, and calculate the durable risk value using the fuzzy rule base and subordinate data; The durability judgment module is used to judge the durability of concrete according to the pre-built durability assessment standards and durability risk values, and complete the concrete durability detection based on the Internet of Things.

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