Building material quality supervision system based on blockchain technology

Abstract

The present invention relates to the field of data analysis, and in particular to a building material quality supervision system based on blockchain technology. The present invention extracts the storage characteristics of each batch of steel and combines the static load time of the corresponding backlog margin to analyze the corresponding storage backlog characterization value to divide the storage backlog category, and then adaptively monitors the quality of the corresponding batch of steel, including determining the storage rack area corresponding to the contact between the batch of steel and the storage rack, analyzing the force loss characterization parameter based on the force characteristics of the storage rack area to evaluate whether the batch of steel meets the storage quality standard, calling the deformation of the storage rack area and the contact surface rust extension area of ​​the corresponding bottom steel to determine whether to issue an early warning prompt signal; adjusting the quality monitoring frequency of the batch of steel according to the storage backlog characterization value; generating data tags for the corresponding batch of steel, and storing the data tags through blockchain. The present invention accurately evaluates the storage quality of steel while improving monitoring efficiency.

Description

Technical Field

[0001] The present invention relates to the field of data analysis, and in particular to a building material quality supervision system based on blockchain technology. Background Art

[0002] With the rapid development of modern industry, steel, as a basic material, is widely used in many fields such as construction and machinery manufacturing. These fields have increasingly higher requirements for the quality of steel. For example, the construction industry requires steel to have high strength, good toughness and corrosion resistance to ensure the safety and durability of buildings. In order to meet the industry's strict requirements on steel quality;

[0003] However, traditional steel storage quality inspection methods mainly rely on manual sampling. This method has certain limitations due to the subjectivity of manual inspection and the randomness of sampling. It is difficult to ensure the comprehensiveness and accuracy of the inspection, and it is easy to miss potential quality problems.

[0004] At the same time, the steel production industry itself is also constantly developing towards scale, automation, and intelligence. During the storage process of large-scale steel production, effective quality monitoring methods are needed to ensure the stability of steel quality. Intelligent quality monitoring systems can better adapt to industry development trends and enhance the competitiveness of enterprises.

[0005] Therefore, multiple technologies are integrated and applied in the development process. For example, sensor technology can provide a rich source of data for steel quality monitoring; data acquisition and transmission technology provides flexibility and scalability for steel quality monitoring systems; data analysis and processing technology can achieve accurate prediction and diagnosis of steel quality, jointly promoting the development of accurate and efficient steel quality monitoring systems.

[0006] Chinese patent application publication number: CN116563273A, discloses a detection and early warning method and system for steel defects. The application calls the target monitoring record and splits it into process nodes, determines the node monitoring image set, builds a defect detection model, extracts the source data to be inspected based on the node monitoring image set, inputs it into the defect detection model, obtains the defect identification set, and the defect alarm information is additional output information. The defect identification set is repaired and evaluated, and a defect assessment list is generated. The defect assessment list is used as the defect detection result. The invention solves the technical problems of insufficient accuracy and completeness of steel defect detection in the existing technology, and deviations from the actual state of the steel. Dual detection channels are constructed for multiple detection dimensions, production detection images are segmented, and processing channel matching detection is performed node by node to ensure the rigor of detection and maximize the accuracy and completeness of defect detection.

[0007] However, the prior art still has the following problems:

[0008] When monitoring a large amount of steel, there are many batches of steel involved. It is not taken into account that steel is a commodity that is produced and sold at the same time, and the turnover situation is different. The storage and stacking situation of steel is dynamic and not fixed. Due to the differences in stacking conditions, the impact on steel quality is different. It is difficult to ensure the accuracy of quality monitoring by using a single monitoring method. Summary of the Invention

[0009] To this end, the present invention provides a construction material quality supervision system based on blockchain technology to overcome the problem in the existing technology that when monitoring a large amount of steel, the impact on the quality of the steel is different due to differences in stacking conditions, and it is difficult to ensure the accuracy of quality monitoring by using a single monitoring method.

[0010] To achieve the above objectives, the present invention provides a construction material quality supervision system based on blockchain technology, which includes:

[0011] A data acquisition module, comprising a data storage unit for storing steel data corresponding to a plurality of batches of steel and an image acquisition unit for acquiring storage image data of each of the batches of steel;

[0012] a storage analysis module connected to the data acquisition module, configured to extract the storage characteristics of each batch of steel and analyze the storage backlog representation value of each batch of steel in combination with the static load duration of the corresponding backlog margin, so as to classify the storage backlog category of each batch of steel;

[0013] a depth monitoring module connected to the data acquisition module, configured to determine the storage rack area corresponding to the contact between the batch of steel and the storage rack, and to analyze a force loss characterization parameter of the storage rack area based on the force characteristics of the storage rack area;

[0014] A monitoring and early warning module, connected to the depth monitoring module, is used to evaluate whether the batch of steel meets the storage quality standard based on the force loss characterization parameter, call the deformation of the storage rack area and the rust extension area of ​​the contact surface of the corresponding underlying steel to determine whether to issue an early warning signal, generate a data tag for the batch of steel, and store the data tag via blockchain;

[0015] a normal monitoring module, connected to the warehouse analysis module, adjusting the quality monitoring frequency of the batch of steel according to the warehouse backlog representation value, generating a data tag for the batch of steel, and storing the data tag via blockchain;

[0016] A selection and calling module is connected to the storage analysis module and is used to select and call the depth monitoring module and the normal monitoring module according to the storage backlog category to perform quality monitoring on the corresponding batch of steel;

[0017] The storage characteristics include the turnover rate and the backlog within a predetermined time period, and the force characteristics include the force value and the vibration frequency.

[0018] Furthermore, the storage analysis module is used to extract the storage characteristics of each batch of steel and analyze the storage backlog representation value of each batch of steel in combination with the static load time of the corresponding backlog margin, including:

[0019] The sum of the ratio of the turnover rate threshold to the turnover rate within a predetermined time period and the ratio of the backlog margin to the backlog margin threshold is used as the first warehouse backlog feature;

[0020] The ratio of the static load duration of the backlog margin to the static load duration threshold is used as the second warehouse backlog feature;

[0021] The first warehouse backlog feature and the second warehouse backlog feature are weightedly summed as the warehouse backlog representation value of the batch of steel.

[0022] Furthermore, the storage analysis module is used to classify the storage backlog categories of each batch of steel, including:

[0023] If the storage backlog characterization value of any batch of steel is greater than or equal to the storage backlog characterization threshold, the batch of steel will be classified as severely overstocked;

[0024] If the warehouse backlog characterization value of any batch of steel is less than the warehouse backlog characterization threshold, the batch of steel will be classified as a slight warehouse backlog category.

[0025] Furthermore, the selection and calling module is used to select and call the depth monitoring module and the normal monitoring module according to the storage backlog category to perform quality monitoring on the corresponding batch of steel, including:

[0026] If the warehouse backlog category is a serious warehouse backlog category, choose to call the deep monitoring module;

[0027] If the warehouse backlog category is a slight warehouse backlog category, choose to call the normal monitoring module.

[0028] Furthermore, the depth monitoring module is used to determine the storage rack area corresponding to the contact between the batch of steel and the storage rack, including:

[0029] Used to call the storage image data of the batch of steel;

[0030] The area where the storage rack contacts and overlaps with the bottom steel materials of the batch of steel materials is determined as the storage rack area.

[0031] Furthermore, the depth monitoring module is used to analyze the stress loss characterization parameters of the storage rack area based on the stress characteristics of the storage rack area, including:

[0032] Used to call steel data to obtain standard vibration frequency;

[0033] used to determine the vibration frequency deviation value between the standard vibration frequency and the vibration frequency;

[0034] using a ratio of the vibration frequency deviation value to a vibration frequency deviation threshold as a first force loss feature;

[0035] The ratio of the force value to the force threshold is used as the second force loss feature;

[0036] The sum of the first stress loss characteristic and the second stress loss characteristic is used as a stress loss characterization parameter of the storage rack area.

[0037] Furthermore, the monitoring and early warning module is used to evaluate whether the batch of steel meets the storage quality standard based on the stress loss characterization parameter, call the deformation of the storage rack area and the rust extension area of ​​the contact surface of the corresponding bottom steel, including:

[0038] If the stress loss characterization parameter of the storage rack area corresponding to the batch of steel is greater than or equal to the stress loss characterization parameter threshold, the batch of steel is evaluated as not meeting the storage quality standards;

[0039] If the batch of steel is assessed as not meeting the storage quality standards, the deformation of the storage rack area and the rust extension area of ​​the corresponding steel contact surface are retrieved.

[0040] Furthermore, the monitoring and early warning module is used to determine whether to issue an early warning signal, including:

[0041] If the batch of steel does not meet the storage conditions, an early warning signal is issued;

[0042] Among them, the warehouse storage conditions include that the deformation of the storage rack area corresponding to the batch of steel is less than the deformation threshold and the rust extension area of ​​the contact surface of the corresponding bottom steel is less than the rust extension area threshold.

[0043] Furthermore, the normal monitoring module is used to adjust the quality monitoring frequency of the batch of steel according to the warehouse backlog characterization value, including:

[0044] The quality monitoring frequency is increased, and the increase in the quality monitoring frequency is positively correlated with the warehouse backlog characterization value.

[0045] Furthermore, the data tag includes the batch code of the batch of steel and the corresponding warehouse backlog category.

[0046] Compared with the prior art, the present invention sets up a data acquisition module, including a data storage unit for storing steel data corresponding to several batches of steel and an image acquisition unit for collecting storage image data of each batch of steel; a storage analysis module for extracting the storage characteristics of each batch of steel and analyzing the storage backlog characterization value of each batch of steel in combination with the static load time of the corresponding backlog margin, so as to classify the storage backlog category of each batch of steel; a depth monitoring module for determining the storage rack area corresponding to the contact between the batch of steel and the storage rack, and analyzing the force loss characterization parameter of the storage rack area based on the force characteristics of the storage rack area; a monitoring and early warning module for evaluating the storage backlog characterization parameter according to the force loss characterization parameter. Estimate whether a batch of steel meets the storage quality standards, call the deformation of the storage rack area and the rust extension area of ​​the corresponding bottom steel contact surface to determine whether to issue an early warning signal, generate data tags for the batch of steel, and store the data tags through the blockchain; the normal monitoring module adjusts the quality monitoring frequency of the batch of steel according to the storage backlog representation value, generates data tags for the batch of steel, and stores the data tags through the blockchain; the selection and calling module is used to select and call the deep monitoring module and the normal monitoring module according to the storage backlog category to monitor the quality of the corresponding batch of steel. The present invention accurately evaluates the storage quality of steel while improving the monitoring efficiency.

[0047] In particular, the present invention sets up a warehouse analysis module to consider the impact of the turnover rate and backlog margin of steel on the quality of steel and the quality status of storage rack support within a certain period of time, wherein the turnover rate reflects the length of time the steel is exposed to the storage environment and the stress state of the storage rack; the backlog margin reflects the impact of the mutual squeezing between steels during storage and the structural stability of the storage rack, and further considers the aggravating impact of the static load time of the steel backlog margin. Therefore, the present invention analyzes the warehouse backlog characterization value of each batch of steel through the warehouse characteristics combined with the corresponding static load time of the backlog margin to characterize the degree of influence of the warehouse circulation of steel on the quality of steel and the performance of the storage rack, and provides data support for the subsequent classification of the warehouse backlog categories of each batch of steel. The present invention accurately evaluates the storage quality of steel while improving monitoring efficiency.

[0048] In particular, the present invention is provided with a depth monitoring module, taking into account the situation where the steel turnover rate is low, the backlog is large and the static load time is too long, according to the stress conditions of the contact area between the storage rack and the steel and the adhesion between the steel and the storage rack, the storage quality of the steel may be affected. Uneven stress in the contact area or excessive stress may cause plastic deformation of the steel, and the degree of adhesion can reflect the scratching and wear between the steel and the storage rack, and the environmental conditions created by the adhesion will affect the degree of rusting of the steel in the contact area; therefore, the present invention analyzes the stress loss characteristics through the stress characteristics of the storage rack area to characterize the degree of deterioration of the steel quality, and provides data support for the subsequent evaluation of whether the batch of steel meets the storage quality standards. The present invention accurately evaluates the storage quality of steel on the premise of improving monitoring efficiency.

[0049] In particular, the present invention sets up a monitoring and early warning module, takes into account the situation where batches of steel do not meet the storage quality standards, conducts in-depth analysis of the rust degree of the steel and the deformation degree of the storage rack area, and evaluates whether the current storage environment of the batch of steel has a serious impact on the quality of the steel. The deformation amount reflects the severity of the pressure effect of the steel on the storage rack and the possible uneven stress situation of the steel itself. In addition to the presence of rust in the contact area between the steel and the storage rack, the area where the rust extends outside the contact area intuitively reflects the severity of corrosion on the contact surface of the steel. The storage quality of the steel is comprehensively monitored from multiple dimensions, making the quality assessment more accurate and reliable. The present invention accurately evaluates the storage quality of steel while improving the monitoring efficiency.

[0050] In particular, the present invention uses blockchain to store the data tags generated for each batch, ensuring the security and non-tamperability of the monitoring data, and providing a reliable historical data basis for the storage management and traceability of steel. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a functional module diagram of a building material quality supervision system based on blockchain technology according to an embodiment of the invention;

[0052] Figure 2 A logic decision diagram for classifying the storage backlog categories of each batch of steel according to an embodiment of the invention;

[0053] Figure 3 A logic decision diagram for selecting and calling a depth monitoring module and a normal monitoring module according to an embodiment of the present invention;

[0054] Figure 4 This is a logical decision diagram for evaluating whether a batch of steel meets storage quality standards in an embodiment of the invention. DETAILED DESCRIPTION

[0055] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0056] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0057] It should be noted that, in the description of the present invention, terms such as "upper", "inner" and "outer" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0058] Furthermore, it should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the term "connection" should be understood in a broad sense. For example, it can refer to a fixed connection, a detachable connection, or an integral connection; it can refer to a mechanical connection or an electrical connection. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0059] See also Figure 1 As shown in FIG, which is a functional module diagram of a construction material quality supervision system based on blockchain technology according to an embodiment of the present invention, the construction material quality supervision system based on blockchain technology according to an embodiment of the present invention includes:

[0060] A data acquisition module, comprising a data storage unit for storing steel data corresponding to a plurality of batches of steel and an image acquisition unit for acquiring storage image data of each of the batches of steel;

[0061] a storage analysis module connected to the data acquisition module, configured to extract the storage characteristics of each batch of steel and analyze the storage backlog representation value of each batch of steel in combination with the static load duration of the corresponding backlog margin, so as to classify the storage backlog category of each batch of steel;

[0062] a depth monitoring module connected to the data acquisition module, configured to determine the storage rack area corresponding to the contact between the batch of steel and the storage rack, and to analyze a force loss characterization parameter of the storage rack area based on the force characteristics of the storage rack area;

[0063] A monitoring and early warning module, connected to the depth monitoring module, is used to evaluate whether the batch of steel meets the storage quality standard based on the force loss characterization parameter, call the deformation of the storage rack area and the rust extension area of ​​the contact surface of the corresponding underlying steel to determine whether to issue an early warning signal, generate a data tag for the batch of steel, and store the data tag via blockchain;

[0064] a normal monitoring module, connected to the warehouse analysis module, adjusting the quality monitoring frequency of the batch of steel according to the warehouse backlog representation value, generating a data tag for the batch of steel, and storing the data tag via blockchain;

[0065] A selection and calling module is connected to the storage analysis module and is used to select and call the depth monitoring module and the normal monitoring module according to the storage backlog category to perform quality monitoring on the corresponding batch of steel;

[0066] The storage characteristics include the turnover rate and the backlog within a predetermined time period, and the force characteristics include the force value and the vibration frequency.

[0067] Specifically, there is no specific limitation on the structure of the data storage unit. It only needs to have the function of storing steel data corresponding to several batches of steel. It can be a component that uses blockchain technology to store steel data, wherein the steel data includes the turnover rate, backlog margin and static load time of the backlog margin corresponding to several batches of steel within a predetermined time period, etc., which will not be repeated here.

[0068] Specifically, there is no specific limitation on the structure of the image acquisition unit. It only needs to have the function of collecting storage images of each batch of steel. The rust on the contact surface of the steel is monitored by a mobile cart equipped with a magnetic thickness gauge, and the relevant image data of the bottom steel and the storage rack are captured by a movable camera. This will not be repeated here.

[0069] In this embodiment, the purpose of setting the predetermined time period is to determine the turnover rate and backlog of each batch of steel. Therefore, the predetermined time period is set to the last six months since the monitoring began to ensure the timeliness of the data and the accuracy of the steel-related data.

[0070] Specifically, the present invention uses blockchain to store the data tags generated for each batch, ensuring the security and non-tamperability of monitoring data, and providing a reliable historical data basis for the storage management and traceability of steel.

[0071] Specifically, there is no limitation on the specific structures of the warehouse analysis module, in-depth monitoring module, monitoring and early warning module, normal monitoring module and selection and scheduling module. They themselves or each unit therein can be composed of logic components or a combination of logic components, and the logic components include field programmable processors, computers or microprocessors in computers.

[0072] Specifically, the storage analysis module is used to extract the storage characteristics of each batch of steel and analyze the storage backlog representation value of each batch of steel in combination with the static load time of the corresponding backlog margin, including:

[0073] The sum of the ratio of the turnover rate threshold to the turnover rate within a predetermined time period and the ratio of the backlog margin to the backlog margin threshold is used as the first warehouse backlog feature;

[0074] The ratio of the static load duration of the backlog margin to the static load duration threshold is used as the second warehouse backlog feature;

[0075] The first warehouse backlog feature and the second warehouse backlog feature are weightedly summed as the warehouse backlog representation value of the batch of steel.

[0076] In actual warehousing, the turnover rate and backlog of each batch of steel can more directly reflect the degree of quality loss caused by the impact of the steel during the storage process. Therefore, in implementation, the warehousing characteristics, that is, the turnover rate and backlog within a predetermined time period, are given priority. Therefore, a slightly higher weight is given to the first warehousing backlog characteristic calculated based on the warehousing characteristics. Therefore, when performing the weighted summation, the weight of the first warehousing backlog characteristic is set to 0.6, and the weight of the second warehousing backlog characteristic is set to 0.4.

[0077] The purpose of setting the turnover rate threshold, the backlog margin threshold and the backlog margin static load time threshold is to characterize the situation where the turnover flexibility of steel is low and the long-term placement on the storage rack has a heavy impact on the quality of the steel itself and the stability of the storage rack. By obtaining the historical records of steel data of several batches of steel, calling the turnover rate historical data, the backlog margin historical data and the corresponding backlog margin static load time historical data, solving the turnover rate mean, the backlog margin mean and the static load time mean, based on setting the above three thresholds For the purpose of, the turnover rate threshold is set to the product of the turnover rate mean and the turnover rate deviation coefficient, the backlog margin threshold is set to the product of the backlog margin mean and the backlog margin deviation coefficient, and the static load time threshold is set to the product of the static load time mean and the static load time deviation coefficient, wherein the turnover rate deviation coefficient is selected within the interval [1.05,1.1], the backlog margin deviation coefficient is selected within the interval [1.05,1.15], and the static load time deviation coefficient is selected within the interval [1.15,1.2].

[0078] Specifically, the present invention sets up a storage analysis module to consider the impact of the turnover rate and backlog of steel on the quality of steel and the quality status of storage rack supports within a certain period of time;

[0079] Among them, the turnover rate reflects the length of time the steel is exposed to the storage environment and the stress state of the storage rack. For the steel itself, when the turnover rate of steel is high, it means that the steel stays in the storage environment for a relatively short time, and there is less long-term static load accumulation. This helps to reduce the rust, deformation and other quality problems that may occur when the steel is exposed to the storage environment for a long time, and the overall quality of the steel is relatively more guaranteed. When the turnover rate of steel is low, it means that the steel is stored in the storage environment for a long time, and is affected by static load pressure and storage environment factors for a long time. It is easy to have problems such as aggravated rust, deformation caused by internal stress changes, and performance degradation. For example, some steel that is prone to rust may be stored for a long time. It can cause surface rust, which in severe cases can affect the strength, toughness and other mechanical properties of the steel. On the other hand, for storage racks, the turnover rate of steel is relatively high. Due to the frequent entry and exit of steel, the stress state of the storage rack is relatively dynamic and balanced, and the pressure of a certain batch of steel will not be concentrated for a long time. This is conducive to reducing deformation, fatigue and other problems caused by long-term local stress on the storage rack, and extending the service life of the storage rack. However, the turnover rate of steel is low, and the long-term backlog of steel will cause the storage rack to be subjected to high pressure at specific locations for a long time, which can easily lead to local deformation, bending and even structural damage of the storage rack. For example, the crossbeam of the storage rack may sag due to long-term heavy pressure, affecting the stability and safety of the storage rack.

[0080] The backlog margin reflects the impact of mutual squeezing between steel materials during storage and the structural stability of the storage rack. For the steel materials themselves, if the backlog margin is small, it means that the inventory of steel materials is relatively reasonable, and the mutual squeezing effect between steel materials is relatively small. The steel materials are relatively less affected by static load pressure, and the possibility of quality problems such as deformation and damage caused by squeezing is low. However, if the backlog margin is large, a large amount of steel backlog will cause the bottom steel to bear a large static load pressure, which may cause local deformation and distortion of the steel. For the storage rack, if the backlog margin is small, the total weight borne by the storage rack is relatively small. The storage rack mainly bears the dead weight of the steel materials and is affected by normal storage environment factors. The structure is relatively stable and generally no obvious quality problems will occur. If the backlog margin is large, the storage rack will be subjected to huge pressure, and the possibility of exceeding its designed load-bearing capacity will increase. This may cause deformation of the support structure of the storage rack, cracking of welds, loosening of connectors and other problems, seriously affecting the quality and safety of the storage rack.

[0081] Furthermore, the present invention further considers the aggravating effect of the static load time on the backlog of steel. For the steel itself, the static load pressure that the steel bears in a relatively short period of time has a relatively limited impact on its quality. Generally speaking, under normal storage conditions, short-term static load will not have a significant adverse effect on the internal structure and performance of the steel, and the quality of the steel can remain relatively stable; but as the static load time increases, the internal microstructure of the steel may change, such as dislocation movement, grain growth, etc., which will lead to changes in the strength, hardness and other properties of the steel. At the same time, long-term static load may also cause fatigue damage to the contact area between the steel and the storage rack, reducing the service life of the steel; for the storage rack, in a short period of time, the pressure borne by the storage rack has a relatively small impact on its structure, generally will not have a significant adverse effect on its quality status, and the storage rack can maintain normal working condition; however, long-term static load will cause fatigue of the storage rack material, reduce its strength and toughness, and may cause irreversible deformation and damage to the storage rack, reducing the quality and safety performance of the storage rack;

[0082] Therefore, the present invention analyzes the storage backlog characterization value of each batch of steel through the storage characteristics combined with the static load time of the corresponding backlog margin to characterize the impact of the storage circulation of steel on the quality of steel and the performance of the storage rack, and provides data support for the subsequent classification of the storage backlog categories of each batch of steel. The present invention accurately evaluates the storage quality of steel while improving the monitoring efficiency.

[0083] Specifically, see Figure 2 As shown, it is a logical decision diagram for classifying the storage backlog categories of each batch of steel according to an embodiment of the present invention. The storage analysis module is used to classify the storage backlog categories of each batch of steel, including:

[0084] If the storage backlog characterization value of any batch of steel is greater than or equal to the storage backlog characterization threshold, the batch of steel will be classified as severely overstocked;

[0085] If the warehouse backlog characterization value of any batch of steel is less than the warehouse backlog characterization threshold, the batch of steel will be classified as a slight warehouse backlog category.

[0086] The threshold for warehouse backlog characterization is selected in the interval [1.75, 1.84].

[0087] Specifically, see Figure 3 As shown, it is a logical decision diagram for selecting and calling the depth monitoring module and the normal monitoring module in an embodiment of the present invention. The selection and calling module is used to select and call the depth monitoring module and the normal monitoring module according to the warehouse backlog category to perform quality monitoring on the corresponding batch of steel, including:

[0088] If the warehouse backlog category is a serious warehouse backlog category, choose to call the deep monitoring module;

[0089] If the warehouse backlog category is a slight warehouse backlog category, choose to call the normal monitoring module.

[0090] Specifically, the depth monitoring module is used to determine the storage rack area corresponding to the contact between the batch of steel and the storage rack, including:

[0091] Used to call the storage image data of the batch of steel;

[0092] The area where the storage rack contacts and overlaps with the bottom steel materials of the batch of steel materials is determined as the storage rack area.

[0093] Specifically, the depth monitoring module is used to analyze the force loss characterization parameters of the storage rack area based on the force characteristics of the storage rack area, including:

[0094] Used to call steel data to obtain standard vibration frequency;

[0095] used to determine the vibration frequency deviation value between the standard vibration frequency and the vibration frequency;

[0096] using a ratio of the vibration frequency deviation value to a vibration frequency deviation threshold as a first force loss feature;

[0097] The ratio of the force value to the force threshold is used as the second force loss feature;

[0098] The sum of the first stress loss characteristic and the second stress loss characteristic is used as a stress loss characterization parameter of the storage rack area.

[0099] In this embodiment, the center position of the storage rack area is determined by determining the storage rack area corresponding to the contact between the storage rack and the bottom steel, and a pressure sensor is arranged at the center position corresponding to the bottom of the storage rack to collect force value data of the storage rack area. Correspondingly, a vibration sensor is arranged to obtain vibration frequency data.

[0100] By calculating the absolute value of the difference between the vibration frequency and the standard vibration frequency, the ratio of the absolute value to the standard vibration frequency is used as the vibration frequency deviation value;

[0101] Specifically, in this embodiment, the purpose of setting the vibration frequency deviation threshold and the force threshold is to characterize the situation where the degree of deterioration of steel quality consumption is too high. Based on the purpose of setting the vibration frequency deviation threshold and the force threshold, by calling the historical data of the vibration frequency deviation value and the historical data of the force value of the storage rack area, the vibration frequency deviation mean and the force mean are solved, and the vibration frequency deviation threshold is set to the product of the vibration deviation mean and the vibration frequency deviation coefficient, and the force threshold is set to the product of the force mean and the force deviation coefficient, wherein the vibration frequency deviation coefficient is selected within the interval [1.05,1.1], and the force deviation coefficient is selected within the interval [1.1,1.2].

[0102] It can be understood that whether there is adhesion between the steel and the storage rack has a certain influence on the vibration frequency. Adhesion may cause the vibration modes of the steel and the storage rack to be coupled. The originally independent vibration modes will affect and superimpose on each other to produce new composite vibration modes. The vibration frequency corresponding to the new composite vibration mode will be different from the vibration frequency of the original individual steel or storage rack. Therefore, in this embodiment, the degree of adhesion between the steel and the storage rack is characterized by the deviation value between the vibration frequency and the standard vibration frequency, wherein the standard vibration frequency is the vibration frequency measured when there is no adhesion between the steel and the storage rack, and the measured vibration frequency is pre-stored in the data acquisition module, which can be the result of the first detection after the steel is placed on the storage rack, and this will not be repeated.

[0103] Specifically, the present invention sets up a depth monitoring module to consider the impact of the stress conditions in the contact area between the storage rack and the steel, as well as the adhesion between the steel and the storage rack, on the storage quality of the steel due to low steel turnover rate, large backlog and long static load time;

[0104] Uneven force distribution in the contact area may cause the steel to bear greater local pressure. Under long-term action, the steel is prone to plastic deformation in the high-stress area, affecting the mechanical properties of the steel. At the same time, if the force in the contact area is too high, the storage rack may also be deformed. For example, the beams may deflect and the columns may tilt, which will cause the support state of the steel to change, subjecting the steel to additional stress and further increasing the risk of steel deformation.

[0105] Vibration testing is performed on the contact area. The deviation between the detected vibration frequency and the standard vibration frequency when no adhesion occurs is used to characterize the degree of adhesion between the bottom steel and the storage rack. Slight adhesion indicates that the relative displacement between the bottom steel and the storage rack is small, resulting in less wear and scratches on the contact surface of the steel. At the same time, it has little impact on the internal structure and performance of the steel, and the quality of the steel is relatively stable. Severe adhesion, on the other hand, results in poor air circulation at the adhesion point, which easily accumulates moisture and creates conditions for rusting of the steel. Combined with the relative displacement or friction between the steel and the storage rack, it will destroy the passivation film or protective coating on the surface of the steel, accelerating the rusting process of the steel.

[0106] Therefore, the present invention analyzes the stress loss characteristics through the stress characteristics of the storage rack area to characterize the degree of deterioration of steel quality consumption, and provides data support for subsequent evaluation of whether the batch of steel meets the storage quality standards. The present invention accurately evaluates the storage quality of steel while improving monitoring efficiency.

[0107] Specifically, see Figure 4 As shown, it is a logical decision diagram for evaluating whether a batch of steel meets the storage quality standard according to an embodiment of the present invention. The monitoring and early warning module is used to evaluate whether the batch of steel meets the storage quality standard based on the force loss characterization parameter, call the deformation amount of the storage rack area and the corresponding contact surface rust extension area of ​​the bottom steel, including:

[0108] If the stress loss characterization parameter of the storage rack area corresponding to the batch of steel is greater than or equal to the stress loss characterization parameter threshold, the batch of steel is evaluated as not meeting the storage quality standards;

[0109] If the stress loss characterization parameter of the storage rack area corresponding to the batch of steel is less than the stress loss characterization parameter threshold, the batch of steel is evaluated as meeting the storage quality standard;

[0110] If the batch of steel is assessed as not meeting the storage quality standards, the deformation of the storage rack area and the rust extension area of ​​the corresponding steel contact surface are retrieved.

[0111] The threshold value of the stress loss characterization parameter is selected within the range [2.15,2.24].

[0112] Specifically, the present invention sets up a monitoring and early warning module, takes into account the situation where batches of steel do not meet the storage quality standards, conducts in-depth analysis of the rust degree of the steel and the deformation degree of the storage rack area, and evaluates whether the current storage environment of the batch of steel has a serious impact on the quality of the steel. The deformation amount reflects the severity of the pressure effect of the steel on the storage rack and the possible uneven stress situation of the steel itself. In addition to the presence of rust in the contact area between the steel and the storage rack, the area where the rust extends outside the contact area intuitively reflects the severity of corrosion on the contact surface of the steel. The storage quality of steel is comprehensively monitored from multiple dimensions, making the quality assessment more accurate and reliable. The present invention accurately evaluates the storage quality of steel while improving the monitoring efficiency.

[0113] Specifically, the monitoring and warning module is used to determine whether to issue a warning prompt signal, including:

[0114] If the batch of steel does not meet the storage conditions, an early warning signal is issued;

[0115] Among them, the warehouse storage conditions include that the deformation of the storage rack area corresponding to the batch of steel is less than the deformation threshold and the rust extension area of ​​the contact surface of the corresponding bottom steel is less than the rust extension area threshold.

[0116] In this embodiment, the purpose of setting the deformation threshold and the rust extension area threshold is to indicate that the steel material is too corroded and rusted, and the stability of the storage rack is reduced;

[0117] Based on the purpose of setting the rust extension area threshold, the rust extension area threshold is selected within [12 cm², 16 cm²];

[0118] The deformation threshold is determined by the structure of the storage rack itself. When the cantilever of a telescopic cantilever rack is extended, the deformation threshold can be set at 1 / 300 to 1 / 400 of the cantilever length; for a multi-layer three-dimensional rack, the deformation threshold can be set at 1 / 250 to 1 / 350 of the span. The deformation of the storage rack can be obtained by laying a strain gauge at the bottom of the storage rack area. According to the resistance strain effect, if the strain gauge is deformed, the resistance value of the strain gauge will also change accordingly. The strain value is calculated using the basic formula of the resistance strain effect, and the strain value is used as the deformation variable. This will not be repeated here.

[0119] Specifically, the normal monitoring module is used to adjust the quality monitoring frequency of the batch of steel according to the warehouse backlog representation value, including:

[0120] The quality monitoring frequency is increased, and the increase in the quality monitoring frequency is positively correlated with the warehouse backlog characterization value.

[0121] In this embodiment, optionally,

[0122] Compare the warehouse backlog representation value with the preset first warehouse backlog representation comparison threshold and the second warehouse backlog representation comparison threshold,

[0123] When the warehouse backlog representation value is greater than the second warehouse backlog representation comparison threshold, the increase amount of the quality monitoring frequency is determined to be the first increase amount, and the first increase amount is set to be 5 times the baseline quality monitoring frequency;

[0124] When the warehouse backlog representation value is greater than or equal to the first warehouse backlog representation comparison threshold and less than or equal to the second warehouse backlog representation comparison threshold, the increase amount of the quality monitoring frequency is determined to be the second increase amount, and the second increase amount is set to be twice the baseline quality monitoring frequency;

[0125] When the warehouse backlog representation value is less than the first warehouse backlog representation comparison threshold, the increase amount of the quality monitoring frequency is determined to be a third increase amount, and the third increase amount is set to 1 times the baseline quality monitoring frequency;

[0126] Among them, the first warehouse backlog characterization comparison threshold is 1.1 times the warehouse backlog characterization threshold, the second warehouse backlog characterization comparison threshold is 1.3 times the warehouse backlog characterization threshold, and the benchmark quality monitoring frequency is 1 time / 6 months.

[0127] Specifically, the data tag includes the batch code of the batch of steel, the corresponding warehouse backlog category and the storage rack location, etc.

[0128] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

Claims

1. A building material quality supervision system based on blockchain technology, characterized by: include: A data acquisition module, comprising a data storage unit for storing steel data corresponding to a plurality of batches of steel and an image acquisition unit for acquiring storage image data of each of the batches of steel; a storage analysis module connected to the data acquisition module, configured to extract the storage characteristics of each batch of steel and analyze the storage backlog representation value of each batch of steel in combination with the static load duration of the corresponding backlog margin, so as to classify the storage backlog category of each batch of steel; a depth monitoring module connected to the data acquisition module, configured to determine the storage rack area corresponding to the contact between the batch of steel and the storage rack, and to analyze a force loss characterization parameter of the storage rack area based on the force characteristics of the storage rack area; A monitoring and early warning module, connected to the depth monitoring module, is used to evaluate whether the batch of steel meets the storage quality standard based on the force loss characterization parameter, call the deformation of the storage rack area and the rust extension area of ​​the contact surface of the corresponding underlying steel to determine whether to issue an early warning signal, generate a data tag for the batch of steel, and store the data tag via blockchain; a normal monitoring module, connected to the warehouse analysis module, adjusting the quality monitoring frequency of the batch of steel according to the warehouse backlog representation value, generating a data tag for the batch of steel, and storing the data tag via blockchain; A selection and calling module is connected to the storage analysis module and is used to select and call the depth monitoring module and the normal monitoring module according to the storage backlog category to perform quality monitoring on the corresponding batch of steel; The storage characteristics include the turnover rate and backlog within a predetermined time period, and the stress characteristics include the stress value and vibration frequency; The storage analysis module is used to extract the storage characteristics of each batch of steel and analyze the storage backlog representation value of each batch of steel in combination with the static load time of the corresponding backlog margin, including: The sum of the ratio of the turnover rate threshold to the turnover rate within a predetermined time period and the ratio of the backlog margin to the backlog margin threshold is used as the first warehouse backlog feature; The ratio of the static load duration of the backlog margin to the static load duration threshold is used as the second warehouse backlog feature; performing a weighted summation of the first warehouse backlog feature and the second warehouse backlog feature as a warehouse backlog representation value of the batch of steel; The selection and calling module is used to select and call the depth monitoring module and the normal monitoring module according to the storage backlog category to monitor the quality of the corresponding batch of steel, including: If the warehouse backlog category is a serious warehouse backlog category, choose to call the deep monitoring module; If the warehouse backlog category is a slight warehouse backlog category, choose to call the normal monitoring module.

2. The construction material quality supervision system based on blockchain technology according to claim 1 is characterized in that: The storage analysis module is used to classify the storage backlog categories of each batch of steel, including: If the storage backlog characterization value of any batch of steel is greater than or equal to the storage backlog characterization threshold, the batch of steel will be classified as severely overstocked; If the warehouse backlog characterization value of any batch of steel is less than the warehouse backlog characterization threshold, the batch of steel will be classified as a slight warehouse backlog category.

3. The construction material quality supervision system based on blockchain technology according to claim 1 is characterized in that: The depth monitoring module is used to determine the storage rack area corresponding to the contact between the batch of steel and the storage rack. include, Used to call the storage image data of the batch of steel; The area where the storage rack contacts and overlaps with the bottom steel materials of the batch of steel materials is determined as the storage rack area.

4. The construction material quality supervision system based on blockchain technology according to claim 3 is characterized in that: The depth monitoring module is used to analyze the stress loss characterization parameters of the storage rack area based on the stress characteristics of the storage rack area, including: Used to call steel data to obtain standard vibration frequency; used to determine the vibration frequency deviation value between the standard vibration frequency and the vibration frequency; using a ratio of the vibration frequency deviation value to a vibration frequency deviation threshold as a first force loss feature; The ratio of the force value to the force threshold is used as the second force loss feature; The sum of the first stress loss characteristic and the second stress loss characteristic is used as a stress loss characterization parameter of the storage rack area.

5. The construction material quality supervision system based on blockchain technology according to claim 1 is characterized in that: The monitoring and early warning module is used to evaluate whether the batch of steel meets the storage quality standard based on the stress loss characterization parameter, and call the deformation of the storage rack area and the rust extension area of ​​the contact surface of the corresponding bottom steel. include, If the stress loss characterization parameter of the storage rack area corresponding to the batch of steel is greater than or equal to the stress loss characterization parameter threshold, the batch of steel is evaluated as not meeting the storage quality standards; If the batch of steel is assessed as not meeting the storage quality standards, the deformation of the storage rack area and the rust extension area of ​​the corresponding steel contact surface are retrieved.

6. The construction material quality supervision system based on blockchain technology according to claim 1 is characterized in that: The monitoring and early warning module is used to determine whether to issue an early warning signal, including: If the batch of steel does not meet the storage conditions, an early warning signal is issued; Among them, the warehouse storage conditions include that the deformation of the storage rack area corresponding to the batch of steel is less than the deformation threshold and the rust extension area of ​​the contact surface of the corresponding bottom steel is less than the rust extension area threshold.

7. The construction material quality supervision system based on blockchain technology according to claim 1 is characterized in that: The normal monitoring module is used to adjust the quality monitoring frequency of the batch of steel according to the warehouse backlog characterization value, include, The quality monitoring frequency is increased, and the increase in the quality monitoring frequency is positively correlated with the warehouse backlog characterization value.

8. The construction material quality supervision system based on blockchain technology according to claim 1 is characterized in that: The data tag includes the batch code of the batch of steel and the corresponding warehouse backlog category.

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