A performance evaluation method for load-bearing nodes in factory ceilings

Abstract

This invention relates to the field of factory ceiling load-bearing safety testing technology, and discloses a performance evaluation method for factory ceiling load-bearing nodes, comprising: selecting a sample of load-bearing nodes to be evaluated, matching corresponding evaluation equipment, and formulating an evaluation index system; conducting multi-dimensional tests on the sample of load-bearing nodes to be evaluated based on experimental simulation equipment to obtain several simulation factors, and generating a first performance evaluation value based on the evaluation index system and simulation factors; conducting multi-dimensional monitoring on the sample of load-bearing nodes to be evaluated based on on-site monitoring equipment to obtain several monitoring factors, and generating a second performance evaluation value based on the evaluation index system and monitoring factors; generating a comprehensive performance evaluation value for the sample of load-bearing nodes to be evaluated, and combining it with the usage requirements of the factory ceiling to form a performance evaluation result for the factory ceiling load-bearing nodes, which can provide targeted performance evaluation results for cleanroom ceiling load-bearing nodes under different working conditions, effectively ensuring the long-term operational safety of large-span ceilings in cleanrooms.

Description

Technical Field

[0001] This application relates to the field of safety testing technology for load-bearing capacity of factory ceilings, and in particular to a performance evaluation method for load-bearing nodes of factory ceilings. Background Technology

[0002] The large-span suspended ceiling load-bearing system in cleanrooms is the core structure supporting FFUs, lighting fixtures, and pipeline equipment. The performance of its load-bearing nodes (mainly composed of screws and keel groove connections) directly determines the safety and stability of the ceiling system. Currently, T-head screws connected to 55-type FFU keel grooves are widely used in cleanrooms. However, the traditional T-head screws and grooves are connected by line contact, which leads to problems such as easy slippage and insufficient anti-slip force, seriously threatening the safety of the suspended ceiling load-bearing system.

[0003] Existing performance evaluation methods for load-bearing nodes in factory ceilings mostly rely on single laboratory static load tests, which can only verify the short-term load-bearing capacity of the nodes. They cannot fully cover the special operating environment of cleanrooms, long-term vibration loads, and compatibility issues caused by existing keel size deviations. Furthermore, existing evaluation methods lack standardized testing procedures and evaluation systems, which cannot accurately reflect the long-term performance of load-bearing nodes under actual working conditions. As a result, the evaluation conclusions lack practicality and cannot provide a reliable basis for the safety upgrade of factory ceiling load-bearing systems. Summary of the Invention

[0004] To address the aforementioned technical problems, this application provides a performance evaluation method for load-bearing nodes in factory ceilings. This method aims to solve the technical problem that existing technologies rely solely on static laboratory tests for evaluation, which cannot fully reflect the long-term performance of load-bearing nodes under actual working conditions, resulting in inaccurate evaluation conclusions and failing to provide a reliable reference for the safety evaluation of load-bearing nodes in cleanroom ceilings.

[0005] In some embodiments of this application, a method for performance evaluation of load-bearing nodes in factory ceilings is provided, including: Select load-bearing nodes to be evaluated, match them with corresponding evaluation equipment, and formulate an evaluation index system. The evaluation equipment includes test simulation equipment and on-site monitoring equipment. Based on the experimental simulation equipment, multi-dimensional tests are performed on each load-bearing node sample to be evaluated to obtain several simulation factors. Based on the evaluation index system and all simulation factors, a first performance evaluation value is generated. Based on the on-site monitoring equipment, multi-dimensional monitoring is performed on each load-bearing node sample to be evaluated to obtain several monitoring factors. A second performance evaluation value is generated based on the evaluation index system and all monitoring factors. A comprehensive performance evaluation value is generated based on the first performance evaluation value and the second performance evaluation value, and the performance evaluation result of the load-bearing node of the factory ceiling is formed by combining the usage requirements of the factory ceiling.

[0006] In some embodiments of this application, a sample of load-bearing nodes to be evaluated is selected, a corresponding evaluation device is matched, and an evaluation index system is established, including: The sample of load-bearing nodes to be evaluated includes square head bolts, 55-type FFU keel slots, and connecting accessories; The test simulation equipment includes a universal testing machine, a fatigue testing machine, a salt spray test chamber, a laser diameter measuring instrument, a high-definition camera, and a tensile testing device; The on-site monitoring equipment includes a wireless sensor network, vibration sensors, data acquisition terminals, and a cloud analysis platform; The evaluation index system includes several evaluation indicators, including structural adaptability indicators, mechanical performance indicators, corrosion resistance indicators, stability indicators, and reliability indicators. Each evaluation indicator is mapped to a corresponding weight coefficient.

[0007] In some embodiments of this application, multi-dimensional tests are performed on each load-bearing node sample to be evaluated based on the experimental simulation equipment to obtain several simulation factors, including: Based on the laser diameter measuring instrument and high-definition camera, structural adaptability tests are performed on the load-bearing node samples to be evaluated to obtain structural test parameters, including the bonding area and the fit stability coefficient. The mechanical properties of the load-bearing node sample to be evaluated are tested using a universal testing machine, a fatigue testing machine, and a tensile testing device to obtain mechanical test parameters, including yield strength, tensile strength, anti-slip force, deformation coefficient, and connection loosening status. The corrosion resistance performance of the load-bearing node sample to be evaluated was tested using a salt spray test chamber to obtain corrosion resistance test parameters, including the corrosion coefficient and the coating peeling status. The structural test parameters, mechanical test parameters, and corrosion resistance test parameters were all set as simulation factors.

[0008] In some embodiments of this application, the geometric parameters of the square head bolt and the actual dimensions of the 55-type FFU keel slot are collected based on the laser diameter measuring instrument, and an observation network is constructed. The observation network includes several observation points, and each observation point corresponds to a set of associated points. The fitting distance values ​​of each observation point are collected by a high-definition camera, and the fitting area is generated based on the fitting distance values ​​between each observation point and the corresponding set of associated points. Several factory environment simulation scenarios were set up, and thermal expansion and contraction tests were conducted based on the simulation scenarios. Real-time bonding distance values ​​at each observation point were collected under different temperature conditions, the distance change rate was calculated, and the bonding stability coefficient was obtained by combining the initial bonding area. The deformation of key parts of the square head screw under different cycles during the fatigue test is monitored in real time, and the ratio of its deformation to the initial size is calculated as the deformation coefficient. The surface rust area of ​​the square head bolts after the neutral salt spray test simulation is monitored in real time, and the percentage of the surface rust area to the total surface area is calculated as the rust coefficient.

[0009] In some embodiments of this application, generating a first performance evaluation value based on the evaluation index system and all simulation factors includes: The fitting area and fit stability coefficient in the structural test parameters are compared with the pre-set standard thresholds in the structural adaptability index. The corresponding scores are assigned according to the comparison results, and the scores are weighted and summed in combination with the weight coefficients of the corresponding simulation factors to obtain the structural adaptability score. The yield strength, tensile strength, slip resistance, deformation coefficient, and connection loosening status in the mechanical test parameters are compared with the pre-set standard thresholds in the mechanical performance indicators. Based on the comparison results, corresponding scores are assigned, and the scores are weighted and summed according to the corresponding weight coefficients to obtain the mechanical performance score. The corrosion coefficient and coating peeling in the corrosion test parameters are compared with the pre-set standard thresholds in the corrosion performance indicators. The grades are evaluated based on the comparison results and converted into corresponding scores. The corrosion performance score is obtained by combining the corresponding weight coefficients. The first performance evaluation value is generated based on the structural adaptability score, mechanical performance score, and corrosion resistance score.

[0010] In some embodiments of this application, before performing multi-dimensional monitoring of the corresponding load-bearing node sample to be evaluated based on the on-site monitoring equipment, the method further includes: Obtain historical monitoring logs for each load-bearing node sample to be evaluated, and extract historical operating condition data from the historical monitoring logs. The historical operating condition data includes FFU status data, plant environment data, and runtime data. The FFU status data includes the operating power of several FFUs. Perform outlier detection and correction on historical operating data, and retain valid data; A working condition feature library is established based on effective data, and the working condition feature library includes several working condition features of different categories; Different categories of working condition characteristics are randomly combined to obtain several working condition categories, each of which is mapped to a corresponding monitoring level and weight coefficient. Set the monitoring time interval for the corresponding operating condition category according to the monitoring level.

[0011] In some embodiments of this application, several monitoring factors are obtained, including: Generate a list of monitoring tasks for each operating condition category; Simulate operating scenarios based on the monitoring task list and the operating condition characteristics involved in the corresponding operating condition categories; Data is collected from the load-bearing node samples to be evaluated in simulated operating scenarios under corresponding working conditions according to the monitoring time interval, and monitoring data packages of the load-bearing node samples to be evaluated under each working condition are obtained. The data in the monitoring data packet is preprocessed to obtain a standardized monitoring data packet; The monitoring data associated with stability and reliability indicators are extracted from the standardized monitoring data package to obtain the feature data package; Feature data packets are generated sequentially; The feature data package includes the dynamic stress response value of the load-bearing node sample to be evaluated under the FFU operating power of the corresponding working condition category, the cumulative displacement after continuous operation, the material property fluctuation coefficient caused by changes in ambient temperature and humidity, the spectral characteristic value of the vibration signal, and the real-time gap change of the node connection part. All data within the feature monitoring data package are set as monitoring factors.

[0012] In some embodiments of this application, a second performance evaluation value is generated based on the evaluation index system and all monitoring factors, including: The dynamic stress response value, cumulative displacement, and spectral characteristic value of vibration signal of the load-bearing node sample under various working conditions are compared with the pre-set standard thresholds in the stability index. The corresponding score is assigned according to the comparison results, and the stability score under each working condition is obtained by weighting the corresponding monitoring factor weight coefficient. The overall stability score is obtained by weighted summation of the stability scores for all operating conditions. The material performance fluctuation coefficient and real-time gap change of the node connection part of the sample of load-bearing node to be evaluated under various working conditions are compared with the pre-set standard thresholds in the reliability index. The corresponding scores are assigned according to the comparison results. The weighted calculation is also combined with the weight coefficient of the corresponding monitoring factor to obtain the reliability score under each working condition. The overall reliability score is obtained by weighted summation of the reliability scores for all operating conditions. A second performance evaluation value is generated based on the overall stability score and the overall reliability score.

[0013] In some embodiments of this application, the method further includes: Randomly select a feature from one of the operating condition categories as the target feature; Using the target feature as the main factor and the working condition features of other categories as auxiliary factors, the correlation coefficient between each working condition category is calculated, and the working condition categories with correlation coefficients greater than the preset correlation coefficient threshold are constructed into a working condition cluster. The multiple operating condition categories in the operating condition cluster are sorted according to the specific values ​​of the main factors, and the stability score sequence and reliability score sequence of the corresponding operating condition cluster are generated according to the sorting results. Calculate the first score confidence coefficient under the corresponding working condition cluster based on the stability score sequence, and obtain the stability confidence index based on the first score confidence coefficient of all working condition clusters. Calculate the second score confidence coefficient under the corresponding operating condition cluster based on the reliability score sequence, and obtain the reliability confidence index based on the second score confidence coefficient of all operating condition clusters. A revised second performance evaluation value is generated based on the stability credibility index and the reliability credibility index.

[0014] In some embodiments of this application, a comprehensive performance evaluation value is generated based on a first performance evaluation value and a second performance evaluation value, and combined with the usage requirements of the factory ceiling, a performance evaluation conclusion for the load-bearing node of the factory ceiling is formed, including: The comprehensive performance evaluation value of the corresponding load-bearing node sample is obtained by weighting and summing the first performance evaluation value and the corrected second performance evaluation value of the same load-bearing node sample. The comprehensive performance evaluation value is matched with the preset performance level threshold range to classify the performance level of the corresponding load-bearing node sample to be evaluated. Based on the performance level of each load-bearing node sample to be evaluated, and in combination with the usage requirements of the factory ceiling, it is determined whether the load-bearing node sample to be evaluated meets the current usage requirements of the factory, and the advantages and disadvantages of each load-bearing node sample to be evaluated are marked to form the final performance evaluation conclusion. The usage requirements include the actual FFU layout scale, design load-bearing requirements, and corrosion level of the usage environment.

[0015] The performance evaluation method for a load-bearing node of a factory ceiling according to an embodiment of this application has the following advantages compared with the prior art: Through multi-dimensional simulation tests and on-site monitoring under actual working conditions, basic performance data of load-bearing nodes in terms of structural adaptability, mechanical properties, and corrosion resistance were obtained. Long-term stability and reliability data of nodes under actual operating conditions were also obtained through on-site multi-condition monitoring. Based on a standardized evaluation system that includes multi-dimensional indicators, the basic performance data and long-term stability and reliability data were evaluated. This can provide targeted performance evaluation results for cleanroom ceiling load-bearing nodes under different working conditions, effectively ensuring the long-term operational safety of large-span ceilings in cleanrooms. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating a performance evaluation method for a load-bearing node of a factory ceiling according to an embodiment of this application. Detailed Implementation

[0017] The specific embodiments of this application will be described in further detail below with reference to the accompanying drawings and examples. The following examples are used to illustrate this application, but are not intended to limit the scope of this application.

[0018] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0019] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0020] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0021] like Figure 1 As shown in the figure, a performance evaluation method for a load-bearing node of a factory ceiling according to an embodiment of this application includes: S101: Select a sample of load-bearing nodes to be evaluated, match the corresponding evaluation equipment, and formulate an evaluation index system. The evaluation equipment includes test simulation equipment and on-site monitoring equipment. S102: Based on the test simulation equipment, perform multi-dimensional tests on each load-bearing node sample to be evaluated to obtain several simulation factors, and generate the first performance evaluation value based on the evaluation index system and all simulation factors. S103: Based on the on-site monitoring equipment, multi-dimensional monitoring is performed on each load-bearing node sample to be evaluated to obtain several monitoring factors. A second performance evaluation value is generated based on the evaluation index system and all monitoring factors. S104: Generate a comprehensive performance evaluation value based on the first performance evaluation value and the second performance evaluation value, and combine it with the usage requirements of the factory ceiling to form the performance evaluation result of the load-bearing node of the factory ceiling.

[0022] In some embodiments of this application, a sample of load-bearing nodes to be evaluated is selected, a corresponding evaluation device is matched, and an evaluation index system is established, including: The sample of load-bearing nodes to be evaluated includes square head bolts, 55-type FFU keel slots, and connecting accessories; The test simulation equipment includes a universal testing machine, a fatigue testing machine, a salt spray test chamber, a laser diameter measuring instrument, a high-definition camera, and a tensile testing device; The on-site monitoring equipment includes a wireless sensor network, vibration sensors, data acquisition terminals, and a cloud analysis platform; The evaluation index system includes several evaluation indicators, including structural adaptability indicators, mechanical performance indicators, corrosion resistance indicators, stability indicators, and reliability indicators. Each evaluation indicator is mapped to a corresponding weight coefficient.

[0023] In this embodiment, the connecting accessories include lubricating grease and fastening tools.

[0024] In this embodiment, each sample consists of a square head screw, a 55-type FFU keel slot, and connecting accessories. In this application, there are multiple sets of samples. By evaluating the performance of multiple sets of samples, reliable data support is provided for the subsequent standard formulation.

[0025] Understandably, by selecting complete load-bearing nodes, including square-headed bolts and 55-type FFU keel slots, as the samples to be evaluated, the performance of the complete node connection structure can be directly assessed. This avoids the evaluation errors caused by testing only a single component. By matching evaluation equipment and establishing an evaluation system, evaluations can be conducted from multiple dimensions, including structural compatibility, mechanical performance, corrosion resistance, operational stability under actual working conditions, and reliability. This covers various risk factors faced by load-bearing nodes in the actual use of factory ceilings, effectively improving the comprehensiveness and accuracy of the evaluation results. It can accurately reflect the actual performance level of the load-bearing nodes and provide data support for the design, selection, installation, acceptance, and subsequent maintenance of factory ceilings.

[0026] In some embodiments of this application, multi-dimensional tests are performed on each load-bearing node sample to be evaluated based on the experimental simulation equipment to obtain several simulation factors, including: Based on the laser diameter measuring instrument and high-definition camera, structural adaptability tests are performed on the load-bearing node samples to be evaluated to obtain structural test parameters, including the bonding area and the fit stability coefficient. The mechanical properties of the load-bearing node sample to be evaluated are tested using a universal testing machine, a fatigue testing machine, and a tensile testing device to obtain mechanical test parameters, including yield strength, tensile strength, anti-slip force, deformation coefficient, and connection loosening status. The corrosion resistance performance of the load-bearing node sample to be evaluated was tested using a salt spray test chamber to obtain corrosion resistance test parameters, including the corrosion coefficient and the coating peeling status. The structural test parameters, mechanical test parameters, and corrosion resistance test parameters were all set as simulation factors.

[0027] In this embodiment, a tensile test is performed using a universal testing machine to obtain the yield strength and tensile strength of the square-head screw; the anti-slip force of the screw in the keel groove is tested using a tensile testing device; and a fatigue testing machine is used to simulate the vibration load generated by the operation of the FFU to conduct 1 million fatigue tests to monitor the deformation of the screw structure and the loosening of the connection.

[0028] In this embodiment, an accelerated corrosion test is conducted using a salt spray test chamber. After a preset corrosive environment is continuously tested for a preset duration, a sample of the load-bearing node to be evaluated is taken out. The surface rust area of ​​each component of the node is photographed using a high-definition camera. The rust coefficient is calculated by combining image recognition. At the same time, the area of ​​coating peeling is statistically analyzed to obtain the coating peeling situation.

[0029] Understandably, through multi-dimensional simulation tests, the basic performance parameters of various aspects of the load-bearing nodes can be quickly obtained in a laboratory environment, providing an accurate data basis for the calculation of the first performance evaluation value. Different test equipment corresponds to tests of different performance dimensions, ensuring the measurement accuracy of each test parameter.

[0030] In some embodiments of this application, the geometric parameters of the square head bolt and the actual dimensions of the 55-type FFU keel slot are collected based on the laser diameter measuring instrument, and an observation network is constructed. The observation network includes several observation points, and each observation point corresponds to a set of associated points. The fitting distance values ​​of each observation point are collected by a high-definition camera, and the fitting area is generated based on the fitting distance values ​​between each observation point and the corresponding set of associated points. Several factory environment simulation scenarios were set up, and thermal expansion and contraction tests were conducted based on the simulation scenarios. Real-time bonding distance values ​​at each observation point were collected under different temperature conditions, the distance change rate was calculated, and the bonding stability coefficient was obtained by combining the initial bonding area. The deformation of key parts of the square head screw under different cycles during the fatigue test is monitored in real time, and the ratio of its deformation to the initial size is calculated as the deformation coefficient. The surface rust area of ​​the square head bolts after the neutral salt spray test simulation is monitored in real time, and the percentage of the surface rust area to the total surface area is calculated as the rust coefficient.

[0031] In this embodiment, the formula for calculating the fit stability coefficient is: Fit stability coefficient = (1 - maximum distance change rate) × initial bonding area / standard bonding area.

[0032] In this embodiment, the geometric parameters include the side length, thickness, chamfer radius, and transition structure dimensions between the head and the rod.

[0033] In this embodiment, the observation network refers to a three-dimensional coordinate observation system established around the connection area between the square head screw and the 55-type FFU keel slot. Each observation point is set at a key contact part of the connection interface. The set of associated points of the observation point refers to the auxiliary observation points of the key contact part, which are composed of other points that have direct contact or force transmission relationship with the observation point in space. Thus, the actual shape of the contact area is fitted by multi-point distance data.

[0034] Specifically, on the horizontal contact surface between the square head screw and the keel groove, a polar coordinate system is established with the center of the screw as the origin. An observation point is set every 15° along the radial direction. The set of associated points for each observation point includes three auxiliary points distributed equidistantly along the axial direction, one above the other. By measuring the relative distance between these points, the contact gap at different angles is calculated, and then the overall bonding area is calculated using the integral method.

[0035] In this embodiment, when the contact area accounts for more than 90% of the theoretical contact area, the structural compatibility test is deemed to be preliminarily qualified; otherwise, further evaluation is required based on the fit stability coefficient. The fit stability coefficient is determined by monitoring the relative displacement change between the observation point and the associated point set under simulated vibration frequencies. The smaller the displacement change, the higher the fit stability coefficient. When the fit stability coefficient is greater than 0.85, the structural compatibility index meets the standard.

[0036] In this embodiment, the high-definition camera uses macro photography and image recognition technology to accurately measure the actual gap distance between each observation point and each point in its associated point set, i.e., the fitting distance value.

[0037] In this embodiment, based on the three-dimensional coordinates of the observation network, each observation point and its associated point set constitute a local evaluation unit. Then, based on the fitting distance value between each point, the actual contact state of the connecting surface within the unit is determined. When the fitting distance value is less than a preset contact threshold, it is determined to be an effective contact area. Then, through integration calculation, the surface area of ​​all effective contact areas is accumulated to obtain the fitting area of ​​several local evaluation units. These areas together constitute the fitting area index in the structural test parameters, which is used to quantify the structural fit tightness of each key part of the evaluation node.

[0038] In this embodiment, the factory environment simulation scenario refers to simulating the temperature and humidity fluctuations inside the factory in different seasons, allowing the entire load-bearing node sample to fully complete thermal expansion and contraction deformation, and then using a laser diameter gauge and a high-definition camera to collect the real-time fitting distance values ​​of each observation point under each temperature gradient.

[0039] Understandably, by constructing an observation network to divide local evaluation units and combining three-dimensional coordinates and multi-point bonding distance values ​​to calculate the bonding area, the actual contact state of the square head screw and the 55-type FFU keel slot connection interface can be accurately quantified, avoiding the subjective error of traditional manual inspection in judging the degree of bonding. At the same time, by combining the distance change after thermal expansion and contraction under different temperature environments to calculate the stability coefficient, the impact of seasonal temperature changes in the factory on the adaptability of the node connection structure can be fully simulated, further improving the accuracy of the structural adaptability test results.

[0040] In some embodiments of this application, generating a first performance evaluation value based on the evaluation index system and all simulation factors includes: The fitting area and fit stability coefficient in the structural test parameters are compared with the pre-set standard thresholds in the structural adaptability index. The corresponding scores are assigned according to the comparison results, and the scores are weighted and summed in combination with the weight coefficients of the corresponding simulation factors to obtain the structural adaptability score. The yield strength, tensile strength, slip resistance, deformation coefficient, and connection loosening status in the mechanical test parameters are compared with the pre-set standard thresholds in the mechanical performance indicators. Based on the comparison results, corresponding scores are assigned, and the scores are weighted and summed according to the corresponding weight coefficients to obtain the mechanical performance score. The corrosion coefficient and coating peeling in the corrosion test parameters are compared with the pre-set standard thresholds in the corrosion performance indicators. The grades are evaluated based on the comparison results and converted into corresponding scores. The corrosion performance score is obtained by combining the corresponding weight coefficients. The first performance evaluation value is generated based on the structural adaptability score, mechanical performance score, and corrosion resistance score.

[0041] In this embodiment, the weight coefficients of each evaluation index are determined by the analytic hierarchy process. In this application, the weight of the structural adaptability index is 0.15, the weight of the mechanical performance index is 0.2, the weight of the corrosion resistance index is 0.15, the weight of the stability index is 0.2, and the weight of the reliability index is 0.2.

[0042] In this embodiment, the score for connection loosening in the mechanical performance index is as follows: full marks are given when no loosening occurs after the fatigue test, 80 marks are given when there is slight loosening but it does not affect the load-bearing capacity, and 0 marks are given when there is obvious loosening or structural damage.

[0043] In this embodiment, the pre-set standard threshold refers to the minimum qualified limit that meets the current factory ceiling design requirements. All parameters are based on meeting this limit as the basis for qualified scoring. When the test parameter exceeds the qualified requirements, points are added appropriately according to the excess ratio. If the qualified limit is not met, the corresponding points are deducted to ensure that the scoring results can intuitively reflect the actual compliance status of the parameters.

[0044] In this embodiment, the value range is 0-100. The higher the score, the better the performance of the corresponding performance factor. The first performance evaluation value is the sum of the structural adaptability score, mechanical performance score, and corrosion resistance score multiplied by their respective weight coefficients.

[0045] Understandably, assigning scores based on preset thresholds accurately assesses the compliance of each simulation factor. Combining this with the preset weighting coefficients of the indicators, the first performance evaluation value is calculated. This accurately reflects the basic performance level of the load-bearing node under laboratory simulation conditions and can convert test parameters of different dimensions into a unified quantitative evaluation result. This facilitates the subsequent integration with the second performance evaluation value obtained from on-site monitoring, ensuring the consistency of the calculation logic of the comprehensive evaluation result.

[0046] In some embodiments of this application, before performing multi-dimensional monitoring of the corresponding load-bearing node sample to be evaluated based on the on-site monitoring equipment, the method further includes: Obtain historical monitoring logs for each load-bearing node sample to be evaluated, and extract historical operating condition data from the historical monitoring logs. The historical operating condition data includes FFU status data, plant environment data, and runtime data. The FFU status data includes the operating power of several FFUs. Perform outlier detection and correction on historical operating data, and retain valid data; A working condition feature library is established based on effective data, and the working condition feature library includes several working condition features of different categories; Different categories of working condition characteristics are randomly combined to obtain several working condition categories, each of which is mapped to a corresponding monitoring level and weight coefficient. Set the monitoring time interval for the corresponding operating condition category according to the monitoring level.

[0047] In this embodiment, different categories include, but are not limited to, environmental categories, operating status categories, and operating duration categories. In this application, environmental categories include constant temperature and humidity, high temperature and high humidity, low temperature and dryness, etc., operating status categories cover FFU full load operation, half load operation, intermittent start-stop operation, and static load, and operating duration categories are divided into less than 1 year after commissioning, 1-5 years, and more than 5 years.

[0048] In this embodiment, each operating condition category includes one operating condition feature under each category.

[0049] In this embodiment, the monitoring levels corresponding to different operating conditions are divided into three levels, from Level 1 to Level 3. The monitoring time interval for Level 1 monitoring is set to collect data once every hour, the monitoring interval for Level 2 monitoring is 6 hours, and the monitoring interval for Level 3 monitoring is 24 hours. This ensures the data collection density of key operating conditions, avoids invalid data redundancy, and reduces the load on monitoring equipment and data transmission.

[0050] Understandably, by pre-sorting historical operating data of the nodes to be evaluated, establishing a categorized operating condition feature library, and dividing monitoring levels and setting corresponding monitoring intervals, differentiated and accurate monitoring can be achieved, ensuring the integrity of key operating condition data collection, while optimizing the allocation of monitoring resources and improving the efficiency of on-site monitoring.

[0051] In some embodiments of this application, several monitoring factors are obtained, including: Generate a list of monitoring tasks for each operating condition category; Simulate operating scenarios based on the monitoring task list and the operating condition characteristics involved in the corresponding operating condition categories; Data is collected from the load-bearing node samples to be evaluated in simulated operating scenarios under corresponding working conditions according to the monitoring time interval, and monitoring data packages of the load-bearing node samples to be evaluated under each working condition are obtained. The data in the monitoring data packet is preprocessed to obtain a standardized monitoring data packet; The monitoring data associated with stability and reliability indicators are extracted from the standardized monitoring data package to obtain the feature data package; Feature data packets are generated sequentially; The feature data package includes the dynamic stress response value of the load-bearing node sample to be evaluated under the FFU operating power of the corresponding working condition category, the cumulative displacement after continuous operation, the material property fluctuation coefficient caused by changes in ambient temperature and humidity, the spectral characteristic value of the vibration signal, and the real-time gap change of the node connection part. All data within the feature monitoring data package are set as monitoring factors.

[0052] In this embodiment, the monitoring task list refers to the monitoring execution list compiled for the load-bearing node sample to be evaluated, based on the monitoring level, monitoring time interval, and collection parameter requirements corresponding to different working condition categories. The list clearly marks the monitoring parameter types, collection point locations, collection frequency, and data storage paths that need to be collected under each working condition category, ensuring that on-site monitoring is carried out in an orderly manner according to the predetermined plan, avoiding the omission of characteristic data of key working conditions, and providing complete and accurate on-site actual operation data support for the subsequent calculation of the second performance evaluation value.

[0053] In this embodiment, the simulated operation scenario refers to adjusting the equipment operating status and environmental control parameters at the site where the load-bearing node to be evaluated has been installed, based on the operating characteristics of the operating condition category, to restore the actual operating scenario of the corresponding operating conditions. After the environmental parameters stabilize, data is collected according to the monitoring time interval to ensure that the monitoring data matches the target operating condition characteristics.

[0054] In this embodiment, the monitoring data includes the real-time stress value, vibration frequency, amplitude, temperature, humidity and displacement change of the node. The monitoring data is preprocessed by the data acquisition terminal, including data filtering, noise reduction and format conversion, to obtain standardized monitoring data.

[0055] In this embodiment, dynamic stress response values ​​are collected by strain gauge sensors to capture the impact load at the moment of FFU start-up and shutdown; cumulative displacement is monitored by laser displacement sensors; the material performance fluctuation coefficient is calculated based on the ambient temperature data collected by temperature sensors and the temperature characteristic curve of the material; the spectral characteristic value of the vibration signal is obtained by performing Fourier transform on the time-domain signal collected by vibration sensors; and the real-time gap change at the node connection is obtained by miniature displacement sensors deployed on the contact edge between the square head screw and the keel slot.

[0056] Understandably, by extracting targeted monitoring factors based on the characteristics of different on-site working conditions, it is possible to accurately capture the key dynamic parameters that affect the performance of load-bearing nodes during actual operation, more realistically reflect the long-term performance of nodes in the actual factory environment, and provide effective data that fits the actual operating state for the calculation of the second performance evaluation value.

[0057] In some embodiments of this application, a second performance evaluation value is generated based on the evaluation index system and all monitoring factors, including: The dynamic stress response value, cumulative displacement, and spectral characteristic value of vibration signal of the load-bearing node sample under various working conditions are compared with the pre-set standard thresholds in the stability index. The corresponding score is assigned according to the comparison results, and the stability score under each working condition is obtained by weighting the corresponding monitoring factor weight coefficient. The overall stability score is obtained by weighted summation of the stability scores for all operating conditions. The material performance fluctuation coefficient and real-time gap change of the node connection part of the sample of load-bearing node to be evaluated under various working conditions are compared with the pre-set standard thresholds in the reliability index. The corresponding scores are assigned according to the comparison results. The weighted calculation is also combined with the weight coefficient of the corresponding monitoring factor to obtain the reliability score under each working condition. The overall reliability score is obtained by weighted summation of the reliability scores for all operating conditions. A second performance evaluation value is generated based on the overall stability score and the overall reliability score.

[0058] In this embodiment, the weight coefficients of each monitoring factor are also determined by the analytic hierarchy process. The weight of the dynamic stress response value is 0.3, the weight of the cumulative displacement is 0.35, the weight of the spectral characteristic value of the vibration signal is 0.35, the weight of the material performance fluctuation coefficient is 0.4, and the weight of the real-time gap change of the node connection is 0.6.

[0059] In this embodiment, when calculating the overall stability score and the overall reliability score, it is necessary to combine the weight coefficients of each operating condition category and sum them up. The longer the operating period and the more extreme the operating conditions, the higher the corresponding weight, highlighting the impact of actual harsh operating conditions on node performance.

[0060] Understandably, by combining the weights of different on-site working conditions to calculate the overall score, the impact of different working conditions on the performance of load-bearing nodes in actual operation can be fully reflected, avoiding the result bias caused by single working condition evaluation. The resulting second performance evaluation value can accurately reflect the true performance level of the load-bearing node under actual on-site operating conditions.

[0061] In some embodiments of this application, the method further includes: Randomly select a feature from one of the operating condition categories as the target feature; Using the target feature as the main factor and the working condition features of other categories as auxiliary factors, the correlation coefficient between each working condition category is calculated, and the working condition categories with correlation coefficients greater than the preset correlation coefficient threshold are constructed into a working condition cluster. The multiple operating condition categories in the operating condition cluster are sorted according to the specific values ​​of the main factors, and the stability score sequence and reliability score sequence of the corresponding operating condition cluster are generated according to the sorting results. Calculate the first score confidence coefficient under the corresponding working condition cluster based on the stability score sequence, and obtain the stability confidence index based on the first score confidence coefficient of all working condition clusters. Calculate the second score confidence coefficient under the corresponding operating condition cluster based on the reliability score sequence, and obtain the reliability confidence index based on the second score confidence coefficient of all operating condition clusters. A revised second performance evaluation value is generated based on the stability credibility index and the reliability credibility index.

[0062] In this embodiment, the correlation coefficient is calculated based on the degree of correlation between the parameters of the main factors and auxiliary factors between different working condition categories. When only the values ​​of the main factors change in multiple working condition categories, while the parameters of other auxiliary factors remain the same, the correlation coefficient will be higher than the preset threshold. These working condition categories can then be classified into the same working condition cluster, which facilitates the analysis of the impact of single factor changes on node performance.

[0063] In this embodiment, the operating condition categories are sorted from largest to smallest according to their specific values, and the stability score and reliability score under the corresponding operating condition category are arranged in sequence to generate a corresponding sequence.

[0064] In this embodiment, the preset correlation coefficient threshold can be adjusted according to the evaluation accuracy requirements. In this application, it is set to 0.6. Working conditions exceeding this threshold can be classified into the same working condition cluster.

[0065] In this embodiment, the first score confidence coefficient is the goodness of fit of the stability score within the working condition cluster with the change of the main factors. The higher the goodness of fit, the stronger the regularity of the score with the change of the factors, and the higher the confidence of the monitoring data and the score results. Similarly, the second score confidence coefficient is the goodness of fit of the reliability score within the corresponding working condition cluster with the change of the main factors.

[0066] In this embodiment, the score adjustment range of each working condition category within the corresponding working condition cluster is set according to the confidence coefficient and goodness of fit of each working condition cluster. The lower the goodness of fit, the higher the probability that there is a deviation in the monitoring data or score assignment under the working condition cluster, and the larger the adjustment range of the corresponding correction coefficient. The corrected scores of each working condition category are all set as confidence indicators.

[0067] In this embodiment, the corrected scores for each working condition category (i.e., reliable indicators) are reweighted to obtain the corrected second performance evaluation value, which can effectively reduce the impact of abnormal or random deviations in monitoring data on the final evaluation result and further improve the accuracy of the on-site performance evaluation result.

[0068] It is understandable that by aggregating related working conditions through correlation coefficients to generate working condition clusters, analyzing the regularity of score changes with key factors to obtain reliable indicators, and then correcting the scores based on reliable indicators, the accuracy and reliability of the second performance evaluation value can be effectively improved, and the evaluation error caused by random factors can be reduced.

[0069] In some embodiments of this application, a comprehensive performance evaluation value is generated based on a first performance evaluation value and a second performance evaluation value, and combined with the usage requirements of the factory ceiling, a performance evaluation conclusion for the load-bearing node of the factory ceiling is formed, including: The comprehensive performance evaluation value of the corresponding load-bearing node sample is obtained by weighting and summing the first performance evaluation value and the corrected second performance evaluation value of the same load-bearing node sample. The comprehensive performance evaluation value is matched with the preset performance level threshold range to classify the performance level of the corresponding load-bearing node sample to be evaluated. Based on the performance level of each load-bearing node sample to be evaluated, and in combination with the usage requirements of the factory ceiling, it is determined whether the load-bearing node sample to be evaluated meets the current usage requirements of the factory, and the advantages and disadvantages of each load-bearing node sample to be evaluated are marked to form the final performance evaluation conclusion. The usage requirements include the actual FFU layout scale, design load-bearing requirements, and corrosion level of the usage environment.

[0070] In this embodiment, for the ceiling acceptance evaluation of a newly built factory, the weight of the first performance evaluation value is set to 0.6 and the weight of the second performance evaluation value is set to 0.4; for the node performance testing evaluation of a factory that has already been put into production, the weight of the first performance evaluation value is set to 0.3 and the weight of the second performance evaluation value is set to 0.7, thereby adapting to the evaluation needs of different scenarios and making the evaluation results more in line with the evaluation purpose.

[0071] In this embodiment, the performance level is divided into four ranges: a comprehensive score of 90 or above is excellent, 80-89 is qualified, 60-79 is qualified but requires attention, and below 60 is unqualified. Different levels correspond to different follow-up processing suggestions.

[0072] In this embodiment, the performance evaluation conclusions specifically include load-bearing node samples that meet the usage requirements, the comprehensive performance score and performance level of each load-bearing node sample, and clear follow-up processing recommendations: for nodes of the excellent level, the original design scheme can be maintained for normal use; for nodes of the good level, they can be put into normal use after confirming that they meet the load-bearing requirements; for nodes of the qualified level but requiring attention, it is clearly indicated that a performance re-inspection should be carried out every six months, focusing on tracking changes in node displacement and gaps; for nodes of the unqualified level, direct suggestions for reinforcement, rectification, or replacement are proposed, providing clear guidance for the safe operation and maintenance of factory ceilings.

[0073] Understandably, by combining the first performance evaluation value from the laboratory design phase with the second performance evaluation value under actual on-site working conditions, the design performance of the nodes and their actual operational performance are taken into account. This allows for accurate and practical performance evaluation results for load-bearing nodes of factory ceilings at different stages and in different usage scenarios. This provides reliable decision support for ceiling construction acceptance, operation and maintenance testing, and fault early warning, effectively avoiding the safety risks caused by ceiling load-bearing failure.

[0074] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and substitutions can be made without departing from the technical principles of this application, and these improvements and substitutions should also be considered within the scope of protection of this application.

Claims

1. A performance evaluation method for load-bearing nodes in factory ceilings, characterized in that, include: Select load-bearing nodes to be evaluated, match them with corresponding evaluation equipment, and formulate an evaluation index system. The evaluation equipment includes test simulation equipment and on-site monitoring equipment. Based on the experimental simulation equipment, multi-dimensional tests are performed on each load-bearing node sample to be evaluated to obtain several simulation factors. Based on the evaluation index system and all simulation factors, a first performance evaluation value is generated. Based on the on-site monitoring equipment, multi-dimensional monitoring is performed on each load-bearing node sample to be evaluated to obtain several monitoring factors. A second performance evaluation value is generated based on the evaluation index system and all monitoring factors. A comprehensive performance evaluation value is generated based on the first performance evaluation value and the second performance evaluation value, and the performance evaluation result of the load-bearing node of the factory ceiling is formed by combining the usage requirements of the factory ceiling.

2. The performance evaluation method for load-bearing nodes of factory ceilings as described in claim 1, characterized in that, Select a sample of load-bearing nodes to be evaluated, match them with corresponding evaluation equipment, and formulate an evaluation index system, including: The sample of load-bearing nodes to be evaluated includes square head bolts, 55-type FFU keel slots, and connecting accessories; The test simulation equipment includes a universal testing machine, a fatigue testing machine, a salt spray test chamber, a laser diameter measuring instrument, a high-definition camera, and a tensile testing device; The on-site monitoring equipment includes a wireless sensor network, vibration sensors, data acquisition terminals, and a cloud analysis platform; The evaluation index system includes several evaluation indicators, including structural adaptability indicators, mechanical performance indicators, corrosion resistance indicators, stability indicators, and reliability indicators. Each evaluation indicator is mapped to a corresponding weight coefficient.

3. The performance evaluation method for load-bearing nodes of factory ceilings as described in claim 2, characterized in that, Based on the aforementioned experimental simulation equipment, multi-dimensional tests were conducted on each load-bearing node sample to be evaluated, resulting in several simulation factors, including: Based on the laser diameter measuring instrument and high-definition camera, structural adaptability tests are performed on the load-bearing node samples to be evaluated to obtain structural test parameters, including the bonding area and the fit stability coefficient. The mechanical properties of the load-bearing node sample to be evaluated are tested using a universal testing machine, a fatigue testing machine, and a tensile testing device to obtain mechanical test parameters, including yield strength, tensile strength, anti-slip force, deformation coefficient, and connection loosening status. The corrosion resistance performance of the load-bearing node sample to be evaluated was tested using a salt spray test chamber to obtain corrosion resistance test parameters, including the corrosion coefficient and the coating peeling status. The structural test parameters, mechanical test parameters, and corrosion resistance test parameters were all set as simulation factors.

4. The performance evaluation method for load-bearing nodes of factory ceilings as described in claim 3, characterized in that, Based on the geometric parameters of the square head bolts and the actual dimensions of the 55-type FFU keel slots collected by the laser diameter measuring instrument, an observation network is constructed. The observation network includes several observation points, and each observation point corresponds to a set of related points. The fitting distance values ​​of each observation point are collected by a high-definition camera, and the fitting area is generated based on the fitting distance values ​​between each observation point and the corresponding set of associated points. Several factory environment simulation scenarios were set up, and thermal expansion and contraction tests were conducted based on the simulation scenarios. Real-time bonding distance values ​​at each observation point were collected under different temperature conditions, the distance change rate was calculated, and the bonding stability coefficient was obtained by combining the initial bonding area. The deformation of key parts of the square head screw under different cycles during the fatigue test is monitored in real time, and the ratio of its deformation to the initial size is calculated as the deformation coefficient. The surface rust area of ​​the square head bolts after the neutral salt spray test simulation is monitored in real time, and the percentage of the surface rust area to the total surface area is calculated as the rust coefficient.

5. The performance evaluation method for load-bearing nodes of factory ceilings as described in claim 4, characterized in that, Based on the aforementioned evaluation index system and all simulation factors, a first performance evaluation value is generated, including: The fitting area and fit stability coefficient in the structural test parameters are compared with the pre-set standard thresholds in the structural adaptability index. The corresponding scores are assigned according to the comparison results, and the scores are weighted and summed in combination with the weight coefficients of the corresponding simulation factors to obtain the structural adaptability score. The yield strength, tensile strength, slip resistance, deformation coefficient, and connection loosening status in the mechanical test parameters are compared with the pre-set standard thresholds in the mechanical performance indicators. Based on the comparison results, corresponding scores are assigned, and the scores are weighted and summed according to the corresponding weight coefficients to obtain the mechanical performance score. The corrosion coefficient and coating peeling in the corrosion test parameters are compared with the pre-set standard thresholds in the corrosion performance indicators. The grades are evaluated based on the comparison results and converted into corresponding scores. The corrosion performance score is obtained by combining the corresponding weight coefficients. The first performance evaluation value is generated based on the structural adaptability score, mechanical performance score, and corrosion resistance score.

6. The performance evaluation method for load-bearing nodes of factory ceilings as described in claim 1, characterized in that, Before performing multi-dimensional monitoring of the corresponding load-bearing node sample to be evaluated based on the aforementioned on-site monitoring equipment, the following steps are also included: Obtain historical monitoring logs for each load-bearing node sample to be evaluated, and extract historical operating condition data from the historical monitoring logs. The historical operating condition data includes FFU status data, plant environment data, and runtime data. The FFU status data includes the operating power of several FFUs. Perform outlier detection and correction on historical operating data, and retain valid data; A working condition feature library is established based on effective data, and the working condition feature library includes several working condition features of different categories; Different categories of working condition characteristics are randomly combined to obtain several working condition categories, each of which is mapped to a corresponding monitoring level and weight coefficient. Set the monitoring time interval for the corresponding operating condition category according to the monitoring level.

7. The performance evaluation method for load-bearing nodes of factory ceilings as described in claim 6, characterized in that, Several monitoring factors were obtained, including: Generate a list of monitoring tasks for each operating condition category; Simulate operating scenarios based on the monitoring task list and the operating condition characteristics involved in the corresponding operating condition categories; Data is collected from the load-bearing node samples to be evaluated in simulated operating scenarios under corresponding working conditions according to the monitoring time interval, and monitoring data packages of the load-bearing node samples to be evaluated under each working condition are obtained. The data in the monitoring data packet is preprocessed to obtain a standardized monitoring data packet; The monitoring data associated with stability and reliability indicators are extracted from the standardized monitoring data package to obtain the feature data package; Feature data packets are generated sequentially; The feature data package includes the dynamic stress response value of the load-bearing node sample to be evaluated under the FFU operating power of the corresponding working condition category, the cumulative displacement after continuous operation, the material property fluctuation coefficient caused by changes in ambient temperature and humidity, the spectral characteristic value of the vibration signal, and the real-time gap change of the node connection part. All data within the feature monitoring data package are set as monitoring factors.

8. The performance evaluation method for load-bearing nodes of factory ceilings as described in claim 7, characterized in that, A second performance evaluation value is generated based on the aforementioned evaluation index system and all monitoring factors, including: The dynamic stress response value, cumulative displacement, and spectral characteristic value of vibration signal of the load-bearing node sample under various working conditions are compared with the pre-set standard thresholds in the stability index. The corresponding score is assigned according to the comparison results, and the stability score under each working condition is obtained by weighting the corresponding monitoring factor weight coefficient. The overall stability score is obtained by weighted summation of the stability scores for all operating conditions. The material performance fluctuation coefficient and real-time gap change of the node connection part of the sample of load-bearing node to be evaluated under various working conditions are compared with the pre-set standard thresholds in the reliability index. The corresponding scores are assigned according to the comparison results. The weighted calculation is also combined with the weight coefficient of the corresponding monitoring factor to obtain the reliability score under each working condition. The overall reliability score is obtained by weighted summation of the reliability scores for all operating conditions. A second performance evaluation value is generated based on the overall stability score and the overall reliability score.

9. The performance evaluation method for load-bearing nodes of factory ceilings as described in claim 8, characterized in that, Also includes: Randomly select a feature from one of the operating condition categories as the target feature; Using the target feature as the main factor and the working condition features of other categories as auxiliary factors, the correlation coefficient between each working condition category is calculated, and the working condition categories with correlation coefficients greater than the preset correlation coefficient threshold are constructed into a working condition cluster. The multiple operating condition categories in the operating condition cluster are sorted according to the specific values ​​of the main factors, and the stability score sequence and reliability score sequence of the corresponding operating condition cluster are generated according to the sorting results. Calculate the first score confidence coefficient under the corresponding working condition cluster based on the stability score sequence, and obtain the stability confidence index based on the first score confidence coefficient of all working condition clusters. Calculate the second score confidence coefficient under the corresponding operating condition cluster based on the reliability score sequence, and obtain the reliability confidence index based on the second score confidence coefficient of all operating condition clusters. A revised second performance evaluation value is generated based on the stability credibility index and the reliability credibility index.

10. The performance evaluation method for load-bearing nodes of factory ceilings as described in claim 9, characterized in that, A comprehensive performance evaluation value is generated based on the first and second performance evaluation values. Combined with the usage requirements of the factory ceiling, a performance evaluation conclusion for the load-bearing nodes of the factory ceiling is formed, including: The comprehensive performance evaluation value of the corresponding load-bearing node sample is obtained by weighting and summing the first performance evaluation value and the corrected second performance evaluation value of the same load-bearing node sample. The comprehensive performance evaluation value is matched with the preset performance level threshold range to classify the performance level of the corresponding load-bearing node sample to be evaluated. Based on the performance level of each load-bearing node sample to be evaluated, and combined with the usage requirements of the factory ceiling, determine whether the load-bearing node sample to be evaluated meets the current usage requirements of the factory, and mark the advantages and disadvantages of each load-bearing node sample to be evaluated, thus forming the final performance evaluation conclusion. The usage requirements include the actual FFU layout scale, design load-bearing requirements, and corrosion level of the usage environment.

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