A concrete durability detection method based on the Internet of Things

Abstract

The present invention relates to the technical field of concrete durability testing, and is a concrete durability testing method based on the Internet of Things. The method comprises: receiving a durability testing instruction, confirming concrete to be tested according to the durability testing instruction and activating a pre-built concrete durability testing unit; using a data acquisition unit to collect environmental data; obtaining test data corresponding to the item to be tested based on the environmental data; using a pre-built fuzzy database to confirm the fuzzy interval of the extracted item to be tested; calling the corresponding membership function; calculating the membership degree based on the membership function and the test data; summarizing the membership degree of the concrete to be tested to obtain membership data; using a data analysis unit to construct a fuzzy rule base; calculating a durability risk value using the fuzzy rule base; judging the durability of the concrete according to the pre-built durability assessment standard and the durability risk value; and completing the concrete durability testing based on the Internet of Things. The present invention can improve the efficiency and accuracy of concrete durability testing.

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. Regular testing can promptly detect potential durability issues such as cracks, spalling, carbonization, and chloride ion corrosion, allowing appropriate maintenance and repair measures to be taken to avoid safety accidents caused by structural failure.

[0003] Current concrete durability testing methods typically rely on visual inspection and chemical analysis. Professionals visually inspect the concrete structure, observing surface defects such as cracks, spalling, and discoloration. Concrete durability testing also involves collecting concrete samples and measuring the chloride ion concentration within the concrete through dissolution and chemical reaction.

[0004] Although the above method can achieve the purpose of concrete durability testing, it is highly subjective and 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 objectives, the present invention provides a concrete durability detection method based on the Internet of Things, comprising:

[0007] receiving a durability test instruction, confirming the concrete to be tested according to the durability test instruction and starting a pre-built concrete durability test unit, wherein the concrete durability test 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;

[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] A plurality of items to be detected are acquired based on a data acquisition unit, items to be detected are sequentially extracted from the plurality of items to be detected, 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] Utilize 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 IoT-based concrete durability test 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] Where 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 ion concentration.

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

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

[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] Where 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 for 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] The surface chloride ion concentration of the concrete to be tested is measured based on a 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] Where 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 confirming the fuzzy interval of the extracted item to be detected using the pre-built fuzzy database, the method further includes:

[0037] Based on the fuzzy database, 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 hierarchical 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 hierarchical 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 hierarchical 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] Performing a classification operation on the carbonization depth interval to obtain a high carbonization set, a medium carbonization set, and a low carbonization set, and summarizing the high carbonization set, the medium carbonization set, and the low carbonization set to obtain a fuzzy interval of the carbonization depth;

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

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

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

[0046] Eliminating the extracted items to be detected 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 detected, 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 calculation of the durable risk value 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 a failure risk value, a first item, and a second item corresponding to the extracted fuzzy rule, identifying first and second membership data in the membership data based on the first and second items, and calculating an activation strength of the extracted fuzzy rule based on the first and second membership 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 for 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 a pre-established durability assessment standard and durability risk value includes:

[0057] Obtain high durability risk interval, medium durability risk interval and low durability risk interval based on durability assessment standards;

[0058] 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;

[0059] If the durability risk value is less than or equal to the upper limit of the low durability risk interval, the concrete 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-mentioned object, the present invention further provides a concrete durability detection system using the Internet of Things, comprising:

[0062] an environmental data acquisition module, configured to receive a durability test instruction, confirm the concrete to be tested according to the durability test instruction, and activate a pre-built concrete durability test unit, wherein the concrete durability test 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, configured to acquire a plurality of items to be detected based on a data acquisition unit, sequentially extract items to be detected from the plurality of items to be detected, and perform the following operations on the extracted items to be detected: acquiring detection data corresponding to the items to be detected based on environmental data, confirming a fuzzy interval of the extracted items to be detected using a pre-built fuzzy database, calling a corresponding membership function based on the fuzzy interval, calculating a membership degree based on the membership function and the detection data, summarizing the membership degree of the concrete to be detected, and obtaining 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 detected, and transmitting the membership data to a 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 based on pre-built durability assessment standards and durability risk values, and complete concrete durability testing based on the Internet of Things.

[0066] In order to solve the above problem, the present invention further provides an 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, which stores at least one instruction. 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 problems 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 in sequence from the multiple items to be detected. 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 a 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 systematic and orderly nature of the detection process is ensured, data omission and confusion are avoided, and detection efficiency is improved. The fuzzy database and membership function can be used to handle the uncertainty and ambiguity in the detection data, providing more accurate and reliable detection results. This fuzzy logic analysis method is more suitable for complex concrete durability assessment than traditional deterministic analysis. The membership degree of the concrete to be detected is summarized to obtain 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 the membership data is transmitted to the data analysis unit. By summarizing and transmitting the membership data, the systematic and complete nature of the data is ensured, and comprehensive and accurate basic data is 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 concrete durability based on experience and rules, thereby improving the scientific nature 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, completing the concrete durability test based on the Internet of Things. Finally, the judgment is made based on the durability assessment standard and the risk value, ensuring the accuracy of the test results. Therefore, the present invention can improve the efficiency and accuracy of concrete durability testing. 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 one embodiment of the present invention;

[0072] Figure 2 This is a functional module diagram of a concrete durability detection system using the Internet of Things provided by one 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 one embodiment of the present invention.

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

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

[0076] The present invention provides an IoT-based concrete durability testing method. The method can be executed by at least one of a server, a terminal, or other electronic device capable of executing the method provided by the present invention. In other words, the method can be executed by software or hardware installed on a terminal or server device, where the software can be a blockchain platform. The server can include, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

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

[0078] S1. Receive a durability test instruction, confirm the concrete to be tested according to the durability test instruction, and start a pre-built concrete durability test unit, wherein the concrete durability test 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.

[0079] It should be explained that the durability test instruction is issued by a concrete durability tester. For example, Xiao Zhang is a tester at a concrete production plant. The plant has just produced a batch of concrete. In order to test the durability of the concrete under the influence of various factors, he issues the durability test instruction. These 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 for detecting 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 ambient vibration frequency and the vibration sensor sensitivity coefficient. The above sensors are all existing technologies and will not be described in detail 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 sensor is used to obtain the carbonization depth of the concrete to be tested; 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] Where 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 load conditions refer to the forces to which the concrete under test is subjected during use. Strain is the degree of deformation of concrete under external forces, obtained using strain sensors. For example, in the calculation of concrete load conditions, strain sensors are embedded within the concrete. When the concrete is subjected to force, the strain sensors measure the strain of the concrete. Ambient vibration frequency refers to the vibration frequency present in the surrounding environment, obtained using vibration sensors. Ambient vibration frequency can affect the calculation of concrete load conditions. For example, when calculating concrete load conditions, heavy machinery operating nearby can generate periodic vibrations. These vibrations can cause the concrete structure to respond at a certain frequency, thus affecting the concrete load conditions. Therefore, incorporating the ambient vibration frequency into the calculation of concrete load conditions can yield more accurate results. It should be explained that phase offset refers to the initial phase of the ambient vibration frequency. Real-time temperature refers to the ambient temperature at the time of concrete load condition calculation. Baseline temperature refers to the temperature used as a reference in the calculation of concrete load conditions, 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;

[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] Where 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 for 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] The carbonation depth of concrete refers to the depth to which carbonation, caused by chemical reactions, penetrates into the concrete structure. This process reduces the durability of concrete. The parameters for the nonlinear characteristics of the carbonation reaction reflect the nonlinear nature of the carbonation reaction, indicating that carbonation is nonlinear. Exposure time refers to the time between the concrete being tested leaving the factory and the completion of data acquisition by the data acquisition unit. Real-time humidity refers to the ambient humidity measured at the current moment. The carbon dioxide concentration on the surface of the concrete being tested refers to the concentration of carbon dioxide in the portion of the concrete surface 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 a chloride ion concentration sensor includes:

[0101] The surface chloride ion concentration of the concrete to be tested is measured based on a 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] Where 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] To explain, the surface chloride ion concentration of the concrete being tested refers to the chloride ion concentration in the portion of the concrete surface that is exposed to the atmosphere. The internal chloride ion concentration of the concrete being tested refers to the chloride ion concentration within the pores within the concrete. The measurement depth is the distance from the surface of the concrete being tested to the point within the concrete where the measurement is taken. The chloride ion diffusion coefficient in concrete is a physical parameter that measures the diffusion rate of chloride ions within the concrete.

[0106] S3. Based on the data acquisition unit, multiple items to be detected are obtained, and items to be detected are extracted from the multiple items to be detected in sequence. The following operations are performed on the extracted items to be detected: detection data corresponding to the items to be detected are obtained based on the environmental data, and the fuzzy interval of the extracted items to be detected is confirmed using a 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.

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

[0108] Based on the fuzzy database, 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 hierarchical 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 hierarchical 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 hierarchical 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] Performing a classification operation on the carbonization depth interval to obtain a high carbonization set, a medium carbonization set, and a low carbonization set, and summarizing the high carbonization set, the medium carbonization set, and the low carbonization set to obtain a fuzzy interval of the carbonization depth;

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

[0115] Explainably, the grading operation refers to dividing the data range of the detection data. For example, 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 that calculates the membership of the test data. The membership degree is a value that indicates the degree of membership of the test data in the corresponding set of the fuzzy interval. Through the membership function, the membership degree 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 the test data does not belong to the corresponding set in the fuzzy interval at all, a value of 1 indicates that the test data completely belongs to the corresponding set in the fuzzy interval, and a value between 0 and 1 indicates that the test data partially belongs to the corresponding set in the fuzzy interval.

[0117] For example, 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 degrees of the concrete to be tested to obtain membership data, wherein the membership data includes the 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 construct a fuzzy rule base.

[0126] Interpretable,membership data refers to a data set obtained by integrating,the membership degree obtained by calculating the detection data based on,the membership function.,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 items to be detected, items to be detected are extracted from the test set in sequence, and the following operations are performed on each of the extracted items to be detected:

[0129] Eliminating the extracted items to be detected 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 detected, 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 various elemental data of the concrete to be tested, where each elemental data includes: real-time temperature, real-time humidity, load conditions, internal chloride ion concentration, and carbonation depth. The set to be tested is the collection of various elemental data of the concrete to be tested. The combination set is the collection of elemental data remaining in the set after one elemental data is removed from the set to be tested. For example, if the real-time temperature is removed from the set to be tested, the remaining real-time humidity, load conditions, internal chloride ion concentration, and carbonation depth constitute the combination set. The combination item is the item extracted from the combination set. For example, if the real-time temperature is removed from the set to be tested, the remaining items in the set to be tested are collectively referred to as the combination set. The real-time humidity is then extracted from the combination set, and the real-time humidity is the extracted combination item. The real-time temperature and 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 to determine the impact 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 assess the risk of concrete durability failure under specific conditions.

[0132] For example, a fuzzy rule is constructed for internal chloride ion concentration and real-time humidity: 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 affiliation 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 a failure risk value, a first item, and a second item corresponding to the extracted fuzzy rule, identifying first and second membership data in the membership data based on the first and second items, and calculating an activation strength of the extracted fuzzy rule based on the first and second membership 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] As you can understand, the fuzzy rule base is the collection of all fuzzy rules. The failure risk value is a pre-established numerical value corresponding to failure risk, used to describe the likelihood of concrete durability failure under given conditions. The durability risk value assesses the durability risk level of the concrete under test 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 a key value for assessing concrete durability.

[0139] It can be explained that 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 internal chloride ion concentration and the detection data is in the low humidity set of 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 obtained by calculating the first item using the corresponding membership function, and the second membership data is the data obtained by calculating the second item using the corresponding membership function. 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 completely not satisfied 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] Specifically, calculating the durable risk value according to the activation intensity set includes:

[0141] The calculation formula for 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] Interpretably, the activation strength set is the set of activation strengths of the fuzzy rules.

[0145] For example, 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 test data is transformed: the internal chloride ion concentration has a membership of 0.8 in the medium concentration set, a membership of 0.2 in the high concentration set, and a membership of 0.0 in the low concentration set. The real-time temperature has a membership of 0.5 in the high temperature set, a membership of 0.5 in the medium temperature set, and a membership of 0.0 in the low temperature set. The activation strength of the fuzzy rules 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. Substituting the activation strength and failure risk values ​​of the fuzzy rules into the durability risk value calculation formula, the durability risk value of the concrete under test is obtained.

[0146] S6. Determine the durability of concrete based on the pre-established durability assessment standards and durability risk values, and complete the IoT-based concrete durability test.

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

[0148] Obtain high durability risk interval, medium durability risk interval and low durability risk interval based on durability assessment standards;

[0149] 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;

[0150] If the durability risk value is less than or equal to the upper limit of the low durability risk interval, the concrete 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] The durability assessment standard is used to evaluate the durability of concrete. 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. 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. The fuzzy interval is hierarchically operated to obtain three sets. Each set has a corresponding membership function. The environmental data is calculated by the membership function in each set to obtain the membership of each set. 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 durability risk value to obtain the durability risk value. The durability risk value is evaluated based on the durability evaluation interval to obtain the durability of the concrete to be tested. The concrete durability detection based on the Internet of Things is completed.

[0154] The present invention is to solve the problems 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 in sequence from the multiple items to be detected. 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 a 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 systematic and orderly nature of the detection process is ensured, data omission and confusion are avoided, and detection efficiency is improved. The fuzzy database and membership function can be used to handle the uncertainty and ambiguity in the detection data, providing more accurate and reliable detection results. This fuzzy logic analysis method is more suitable for complex concrete durability assessment than traditional deterministic analysis. The membership degree of the concrete to be detected is summarized to obtain 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 the membership data is transmitted to the data analysis unit. By summarizing and transmitting the membership data, the systematic and complete nature of the data is ensured, and comprehensive and accurate basic data is 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 concrete durability based on experience and rules, thereby improving the scientific nature 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, completing the concrete durability test based on the Internet of Things. Finally, the judgment is made based on the durability assessment standard and the risk value, ensuring the accuracy of the test results. Therefore, the present invention can improve the efficiency and accuracy of concrete durability testing.

[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 IoT-based concrete durability testing system 100 described in the present invention can be installed in an electronic device. Depending on the functionality implemented, the IoT-based concrete durability testing system 100 can include an environmental data acquisition module 101, a subordinate data acquisition module 102, a durability risk value acquisition module 103, and a durability assessment module 104. A module, also referred to as a unit, refers to a series of computer program segments that can be executed by an electronic device processor and perform a fixed function. These modules are stored in the electronic device's memory.

[0157] The environmental data acquisition module 101 is used to receive a durability test instruction, confirm the concrete to be tested according to the durability test instruction, and start a pre-built concrete durability test unit, wherein the concrete durability test 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 membership data acquisition module 102 is configured to acquire a plurality of items to be detected based on a data acquisition unit, sequentially extract items to be detected from the plurality of items to be detected, and perform the following operations on the extracted items to be detected: acquiring detection data corresponding to the items to be detected based on environmental data, confirming a fuzzy interval of the extracted items to be detected using a pre-built fuzzy database, calling a corresponding membership function based on the fuzzy interval, calculating a membership degree based on the membership function and the detection data, summarizing the membership degree of the concrete to be detected, and obtaining 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 detected, and transmitting the membership data to a data analysis unit;

[0159] The durable risk value acquisition module 103 is used to construct a fuzzy rule base using the 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, the modules in the concrete durability detection system 100 using the Internet of Things in the embodiment of the present invention are used in the same manner as above. Figure 1The same technical means are used as the concrete durability detection method based on the Internet of Things described in the previous section and can produce the same technical effects, so I will not go into details here.

[0162] like Figure 3 , which is a structural diagram of an electronic device for implementing a concrete durability detection method based on the Internet of Things provided by one 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, including flash memory, mobile hard disk, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 1. Furthermore, 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 can also be used to temporarily store data that has been output or is to be output.

[0165] In some embodiments, the processor 10 may be comprised of an integrated circuit, such as 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. The processor 10 is the control core (Control Unit) of the electronic device, connecting the various components of the entire electronic device using various interfaces and circuits. It executes or runs programs or modules stored in the memory 11 (e.g., a program for a concrete durability testing method based on the Internet of Things), 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. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to enable communication between the memory 11 and at least one processor 10.

[0167] Figure 3 Only the electronic device with components is shown, and it can be understood by those skilled in the art 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 further include a power source (such as a battery) for powering the various components. Preferably, the power source may be logically connected to the at least one processor 10 via a power management device, thereby implementing functions such as charging management, discharging management, and power consumption management through the power management device. The power source may further include any components such as one or more DC or AC power sources, a recharging device, a power failure detection circuit, a power converter or inverter, a power status indicator, etc. The electronic device 1 may further include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail 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 or an input unit (such as a keyboard). Optionally, the user interface may also be 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, or 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 the following:

[0173] receiving a durability test instruction, confirming the concrete to be tested according to the durability test instruction and starting a pre-built concrete durability test unit, wherein the concrete durability test 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;

[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] A plurality of items to be detected are acquired based on a data acquisition unit, items to be detected are sequentially extracted from the plurality of items to be detected, 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] Utilize 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 IoT-based concrete durability test 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 modules / units integrated into the electronic device 1 are implemented as software functional units and sold or used as independent products, they 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 drive, a magnetic disk, an optical disk, a computer memory, or 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. When the computer program is executed by a processor of an electronic device, the computer program can implement:

[0184] receiving a durability test instruction, confirming the concrete to be tested according to the durability test instruction and starting a pre-built concrete durability test unit, wherein the concrete durability test 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;

[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] A plurality of items to be detected are acquired based on a data acquisition unit, items to be detected are sequentially extracted from the plurality of items to be detected, 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] Utilize the data analysis unit to build a 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 IoT-based concrete durability test 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 exemplary, and actual implementations may have other division methods.

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

[0194] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional modules.

[0195] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, 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] Furthermore, 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 recited in a system claim may also be implemented by a single unit or device through software or hardware. Second-order terms are used to indicate names and do not imply any particular order.

[0197] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. 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 solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions 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 test instruction, confirming the concrete to be tested according to the durability test instruction and starting a pre-built concrete durability test unit, wherein the concrete durability test 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; 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; A plurality of items to be detected are acquired based on a data acquisition unit, items to be detected are sequentially extracted from the plurality of items to be detected, 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; Utilize the data analysis unit to build a fuzzy rule base; Wherein, the use of the data analysis unit to construct a fuzzy rule base includes: A test set is obtained based on multiple items to be detected, items to be detected are extracted from the test set in sequence, and the following operations are performed on each of the extracted items to be detected: Eliminating the extracted items to be detected 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 detected, and sending the fuzzy rules to a pre-constructed initial fuzzy rule base; After confirming that the combination set is a 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; 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 IoT-based concrete durability test is completed.

2. The method for detecting concrete durability based on the Internet of Things according to claim 1, wherein: 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(t)=k·∈(t)·(1+sin(ωt+φ))·exp(T(t)-T0) Where 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 ion concentration.

3. The method for detecting concrete durability based on the Internet of Things according to claim 2, wherein: 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; 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: Where 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 for 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, wherein: The method of obtaining the internal chloride ion concentration of the concrete to be tested by using a chloride ion concentration sensor includes: The surface chloride ion concentration of the concrete to be tested is measured based on a 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: Where 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, wherein: Before confirming the fuzzy interval of the extracted item to be detected using the pre-built fuzzy database, the method further includes: Based on the fuzzy database, internal chloride ion concentration interval, real-time temperature interval, real-time humidity interval, load condition interval and carbonization depth interval are obtained; Performing a hierarchical 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 hierarchical 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 hierarchical 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; Performing a classification operation on the carbonization depth interval to obtain a high carbonization set, a medium carbonization set, and a low carbonization set, and summarizing the high carbonization set, the medium carbonization set, and the low carbonization set to obtain a fuzzy interval of the carbonization depth; A fuzzy database is constructed based on the fuzzy intervals of internal chloride ion concentration, real-time temperature, real-time humidity, load conditions, and carbonization depth.

6. The method for detecting concrete durability based on the Internet of Things according to claim 5, wherein: The calculation of the durable risk value using the fuzzy rule base and the membership 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 a failure risk value, a first item, and a second item corresponding to the extracted fuzzy rule, identifying first and second membership data in the membership data based on the first and second items, and calculating an activation strength of the extracted fuzzy rule based on the first and second membership 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.

7. The method for detecting concrete durability based on the Internet of Things according to claim 6, wherein: Calculating the durable risk value according to the activation intensity set includes: The calculation formula for 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.

8. The method for detecting concrete durability based on the Internet of Things according to claim 7, wherein: The determination of the durability of concrete according to the pre-established durability assessment standard and durability risk value includes: Obtain high durability risk interval, medium durability risk interval and low durability risk interval based on durability assessment standards; 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 concrete 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.

9. A concrete durability detection system based on the Internet of Things, characterized in that: The system comprises: an environmental data acquisition module, configured to receive a durability test instruction, confirm the concrete to be tested according to the durability test instruction, and activate a pre-built concrete durability test unit, wherein the concrete durability test 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, configured to acquire a plurality of items to be detected based on a data acquisition unit, sequentially extract items to be detected from the plurality of items to be detected, and perform the following operations on the extracted items to be detected: acquiring detection data corresponding to the items to be detected based on environmental data, confirming a fuzzy interval of the extracted items to be detected using a pre-built fuzzy database, calling a corresponding membership function based on the fuzzy interval, calculating a membership degree based on the membership function and the detection data, summarizing the membership degree of the concrete to be detected, and obtaining 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 detected, and transmitting the membership data to a 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; Wherein, the use of the data analysis unit to construct a fuzzy rule base includes: A test set is obtained based on multiple items to be detected, items to be detected are extracted from the test set in sequence, and the following operations are performed on each of the extracted items to be detected: Eliminating the extracted items to be detected 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 detected, and sending the fuzzy rules to a pre-constructed initial fuzzy rule base; After confirming that the combination set is a 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; The durability judgment module is used to judge the durability of concrete based on pre-built durability assessment standards and durability risk values, and complete concrete durability testing based on the Internet of Things.

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