A method, device, and equipment for controlling the entry and exit of specimens in an intelligent maintenance system.

Abstract

This invention provides a method and apparatus for controlling the entry and exit of specimens in an intelligent curing system, relating to the field of data processing technology. The method includes: acquiring entry information of specimens to be cured; verifying the specimens based on the entry information to obtain verified specimens; determining target curing environment parameters based on the verified specimens and preset curing requirements; controlling the curing chamber according to the target curing environment parameters to obtain cured specimens; generating quality inspection data related to the cured specimens when an exit instruction for the cured specimens is received; comparing the quality inspection data with preset exit conditions to determine whether exit is permitted, and controlling the exit operation when exit is permitted. This invention achieves intelligent control of the entire process of specimen entry and exit, while ensuring the accuracy and completeness of the verified specimen exit information.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method, apparatus, and equipment for controlling the entry and exit of specimens in an intelligent maintenance system. Background Technology

[0002] In the field of concrete and building materials testing, the standard curing of specimens (such as concrete test blocks) is a key link to ensure the accuracy and comparability of their subsequent performance test results. Traditional curing systems rely heavily on manual operation and recording for specimen entry and exit management, which has problems such as low efficiency, easy error and difficulty in traceability. In recent years, in order to improve management efficiency, some maintenance systems with basic automation functions have emerged. For example, some systems use barcodes or QR codes to identify specimens, register and identify specimens through barcode scanning devices, and control the temperature and humidity of the curing room using preset programs. However, existing technical solutions still have limitations: for example, in the warehousing verification stage, existing systems typically only perform simple identification scanning and information entry, lacking in-depth verification and logical consistency judgment of key parameters of the specimens (such as material ratio, design strength, molding date, etc.); when information entry is incorrect, batch information is chaotic, or curing requirements are unclear, the system cannot automatically identify and correct these errors, resulting in specimens being stored with defects, creating potential quality risks for subsequent curing and testing; at the same time, in the curing control stage, existing systems mostly use fixed or a limited number of preset curing programs, making it difficult to adapt to the specific material characteristics and age of the specimens. The existing curing methods cannot meet the diverse curing standards required by factors such as curing period requirements, thus affecting the uniformity and reliability of specimen curing quality. Moreover, in the outbound decision-making process, the existing system often lacks linkage with the quality data of the curing process. The outbound operation is usually based on a simple judgment of curing time, or still requires manual verification of paper records. It cannot automatically integrate multi-dimensional information such as the compliance of the curing environment and the quality test data of the specimens after curing for a comprehensive outbound judgment. This results in insufficient intelligence in outbound control and the risk of substandard specimens being mistakenly released. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method, device and equipment for controlling the entry and exit of specimens in an intelligent curing system, so as to realize intelligent control of the entire process of specimen entry and exit, while ensuring the accuracy and completeness of the specimen exit information after verification.

[0004] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: This invention provides a method for controlling the entry and exit of specimens in an intelligent curing system, comprising: Obtain the warehousing information of the specimens to be cured; Based on the warehousing information, the specimens to be cured are verified to obtain the verified specimens; Based on the validated specimens and the preset curing requirements, the target curing environment parameters are determined; The target maintenance environment parameters are parsed into specific environmental control instructions, and the corresponding instructions are distributed to the intelligent maintenance room. Real-time monitoring of environmental parameters in the maintenance room, and comparison with target maintenance environmental parameters to obtain comparison results; The intelligent maintenance room is dynamically adjusted based on the comparison results until the actual environmental parameters stabilize within the target range. After the preset curing time is reached and the environmental parameters continue to meet the standards, a curing completion signal is generated, and the specimens that are in the curing completion signal state are marked as cured specimens. When the outbound instruction of the cured specimen is received, quality inspection data related to the cured specimen is generated; The quality inspection data is compared with the preset outbound conditions to determine whether outbound is allowed, and the outbound operation is executed when outbound is allowed.

[0005] Optionally, based on the warehousing information, the specimens to be cured are verified to obtain verified specimens, including: Based on the warehousing information, extract the set of key parameters of the specimen to be cured; The key parameter set is iteratively optimized and verified until the preset convergence condition is met, and the optimized specimen parameters are obtained. The optimized specimen parameters are used as the validated specimens; wherein, the set of key parameters includes: specimen identification information, specimen material property parameters, and associated curing requirement parameters.

[0006] Optionally, the key parameter set is iteratively optimized and verified until a preset convergence condition is met to obtain optimized specimen parameters, including: The set of key parameters is used as the position information of the initial population particles, and the particle swarm of the preset swarm optimization algorithm is initialized. Based on a pre-defined verification rule base, the verification matching degree of the parameter set represented by each particle is calculated, which is used as the fitness value of the corresponding particle. Based on the fitness value of each particle and the preset iteration rules, the velocity and position information of each particle are updated to obtain the updated parameter set population. Based on the updated parameter set population, the fitness value is repeatedly calculated and the velocity and position information of each particle are updated until the preset number of iterations is reached to obtain the final particle; The set of parameters represented by the final particles is used as the optimized specimen parameters.

[0007] Optionally, the fitness value is calculated using the following formula: ; Where F represents the final specimen parameter validation fitness value; This represents the basic weight coefficient corresponding to the specimen identification information, and satisfies α+β+γ=1; The basic verification matching score represents the specimen identification information. The deviation value of the specimen identification information matching degree is represented by ΔS, which is the absolute difference between the basic matching degree S and the ideal standard matching degree 1. 1|; σ S The standard deviation of the Gaussian correction term for the specimen identification information; C S This represents the confidence coefficient for collecting specimen identification information; β represents the basic weighting coefficient corresponding to the material property parameters of the specimen; M represents the basic degree of matching between the specimen material property parameters and the standard material parameter set; The deviation value of the material property parameters of the specimen is represented by ΔM, which is the absolute difference between the basic matching degree M and the ideal standard matching degree 1. 1|; σ M This represents the standard deviation of the Gaussian correction term for the material property parameters of the specimen; C M This represents the confidence coefficient for the detection of material property parameters of the specimen; γ represents the basic weighting coefficient corresponding to the specimen-related curing requirement parameters; R represents the basic degree of conformity between the specimen-related curing requirements parameters and the preset curing specifications; ΔR represents the deviation value of the specimen curing requirement parameters, which is the absolute difference between the basic compliance R and the ideal standard compliance 1. ΔR = |R| 1|; σ R This represents the standard deviation of the Gaussian correction term for the specimen curing requirement parameter; C R This represents the confidence coefficient of the specimen curing specification; λ represents the global penalty coefficient for total deviation; K represents the global normalization factor, which is used to constrain the final fitness value F within the interval [0,1].

[0008] Optionally, based on the validated specimens and preset curing requirements, the target curing environment parameters are determined, including: The verified specimens are analyzed to obtain the specimen material property parameters and associated curing requirement parameters contained in the verified specimens; Based on a pre-set curing parameter knowledge base, benchmark curing parameters are matched according to the specimen material property parameters and associated curing requirement parameters; The baseline maintenance parameters are dynamically adjusted according to the preset maintenance requirements to determine the target maintenance environment parameters.

[0009] Optionally, upon receiving the outbound instruction for the cured specimen, quality inspection data related to the cured specimen is generated, including: The outbound instructions of the cured specimens are parsed to obtain the identifier of the outbound specimens corresponding to the outbound instructions; The unique identifier of the specimen in the instruction is matched with the list of specimens in the cured state to obtain the target matching result; Based on the target matching results, generate the corresponding quality inspection data related to the cured specimens.

[0010] Optionally, the quality inspection data is compared with preset outbound conditions to determine whether outbound shipment is permitted, and if outbound shipment is permitted, the outbound operation is controlled to be executed, including: Each test data point in the quality inspection dataset is compared with the corresponding qualified threshold in the preset outbound conditions. If all test data meet the corresponding qualified threshold, it is determined that the goods can be released from the warehouse, and a release permission signal is generated. Based on the outbound permission signal, the outbound actuator in the intelligent curing room controls the outbound execution mechanism to move the cured specimen out of the curing room, and updates the inventory record and generates an outbound report.

[0011] The present invention also provides a specimen entry and exit control device for an intelligent curing system, comprising: The acquisition module is used to receive the warehousing information of the specimens to be cured; The processing module is used to verify the information of the specimens to be cured based on the warehousing information, and obtain the verified specimens; determine the target curing environment parameters based on the verified specimens and preset curing requirements; parse the target curing environment parameters into specific environmental control instructions and distribute the corresponding instructions to the intelligent curing room; monitor the environmental parameters in the curing room in real time and compare them with the target curing environment parameters to obtain the comparison results; dynamically adjust the intelligent curing room according to the comparison results until the actual environmental parameters are stable within the target range; after the preset curing time is reached and the environmental parameters continue to meet the standards, generate a curing completion signal and mark the specimens with the curing completion signal as cured specimens; when the warehousing instruction of the cured specimens is received, generate quality inspection data related to the cured specimens; compare the quality inspection data with preset warehousing conditions to determine whether warehousing is allowed, and control the execution of the warehousing operation when warehousing is allowed.

[0012] The present invention also provides a computing device, comprising: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method described herein.

[0013] The present invention also provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method described thereon.

[0014] The above-described solution of the present invention has at least the following beneficial effects: The specimen entry and exit control method of the intelligent curing system of the present invention involves: acquiring the entry information of the specimens to be cured; verifying the specimens based on the entry information to obtain verified specimens; determining the target curing environment parameters based on the verified specimens and preset curing requirements; parsing the target curing environment parameters into specific environmental control instructions and distributing the corresponding instructions to the intelligent curing room; monitoring the environmental parameters in the curing room in real time and comparing them with the target curing environment parameters to obtain comparison results; dynamically adjusting the intelligent curing room according to the comparison results until the actual environmental parameters stabilize within the target range; generating a curing completion signal after the preset curing time is reached and the environmental parameters continue to meet the standards, and marking the specimens at the curing completion signal as cured specimens; when receiving the exit instruction of the cured specimens, generating quality inspection data related to the cured specimens; comparing the quality inspection data with preset exit conditions to determine whether exit is allowed, and controlling the exit operation when exit is allowed. It has achieved intelligent control of the entire process of specimen entry and exit from the warehouse, while ensuring the accuracy and completeness of the specimen exit information after verification. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating the specimen entry and exit control method of the intelligent maintenance system of the present invention.

[0016] Figure 2 This is a schematic diagram of the specimen entry and exit control device frame structure of the intelligent maintenance system of the present invention. Detailed Implementation

[0017] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0018] like Figure 1 As shown, an embodiment of the present invention proposes a method for controlling the entry and exit of specimens in an intelligent maintenance system, comprising: Step 11: Obtain the warehousing information of the specimens to be cured; Step 12: Based on the warehousing information, verify the information of the specimen to be cured to obtain the verified specimen; Step 13: Determine the target curing environment parameters based on the verified specimens and preset curing requirements; Step 14: Parse the target maintenance environment parameters into specific environmental control instructions and distribute the corresponding instructions to the intelligent maintenance room; Step 15: Monitor the environmental parameters in the maintenance room in real time and compare them with the target maintenance environmental parameters to obtain the comparison results; Step 16: Dynamically adjust the intelligent maintenance room based on the comparison results until the actual environmental parameters stabilize within the target range; Step 17: After the preset curing time is reached and the environmental parameters continue to meet the standards, a curing completion signal is generated, and the specimens that are in the curing completion signal state are marked as cured specimens; Step 18: When the outbound instruction of the cured specimen is received, generate quality inspection data related to the cured specimen; Step 19: Compare the quality inspection data with the preset outbound conditions to determine whether outbound is allowed, and control the outbound operation to be executed when outbound is allowed.

[0019] In this embodiment, the specimen entry and exit control method of the intelligent curing system ensures the uniqueness and traceability of the data source by receiving the entry information in a structured manner, avoiding the omissions, illegible handwriting, or lost documents that are prone to occur in traditional manual registration, thus improving the efficiency and accuracy of information entry. Through in-depth verification of the entry information, it can automatically identify and filter specimen data with errors, incompatible formats, or logical contradictions. By transforming the original, potentially problematic specimen information to be cured into reliable, high-quality verified specimen data, the accuracy of the specimen information entering the curing stage is guaranteed. Based on the verified and accurate specimen information (such as materials, proportions, and age), the optimal curing environment parameters (temperature, humidity) are dynamically determined. The system implements real-time closed-loop control of the curing room, ensuring that each specimen is cured in an environment most suitable for its characteristics, thus improving the uniformity and reliability of curing quality. Simultaneously, automated control reduces manual intervention, improves curing efficiency, and reduces energy consumption and equipment wear through precise management of environmental parameters. By automatically comparing integrated full-process quality data with strictly preset outbound conditions (standards), the system can objectively and consistently determine whether a specimen is qualified and allows it to leave the warehouse. This completely replaces the traditional method of relying on human experience, eliminating human factors and subjective misjudgments, and achieving intelligent control of the entire process of specimen entry and exit from the warehouse. It also ensures the accuracy and completeness of verified specimen outbound information.

[0020] In this embodiment, the information stored in the database is a structured data package, including: a unique identifier for the specimen, such as a barcode or QR code, used to accurately track the specimen in the system; basic attributes of the specimen, such as concrete grade (strength grade), mix design number, molding date, engineering location, production batch, etc.; and associated curing requirements, such as the standard curing specifications (national standards, industry standards) to be followed, specific temperature and humidity curves, planned total curing time, etc. These requirements can be directly included in the information or obtained by associating the specimen attributes with the backend database.

[0021] In this embodiment, the receiving method of the warehousing information can be achieved by an operator scanning the barcode / QR code on the specimen with a barcode scanner; the specimen passes through the warehousing channel equipped with an RFID reader, and its electronic tag is automatically identified; the specimen information package is automatically received and bound from the production management system or laboratory information management system (LIMS) of the previous process through a data interface; after receiving this information, the various fields in the information package are parsed, and it is created as a specimen record to be processed in the database or memory queue, and the process chain is automatically triggered, marking that the specimen has officially entered the system's management queue, waiting to enter the next key link; through the automated entry of the warehousing information of the specimens to be cured, the starting point mapping of the curing management from physical flow to digital twin is realized, which solves the fundamental problems of low efficiency, error-proneness and lack of real-time traceability caused by the traditional method of relying on paper documents, manual visual identification and manual ledger records, and realizes standardized and automated information reception.

[0022] In an optional embodiment of the present invention, step 12, verifying the information of the specimen to be cured according to the warehousing information to obtain the verified specimen, may include: Step 121: Based on the information entered into the database, extract the set of key parameters for the specimen to be cured; wherein, the set of key parameters includes: specimen identification information, specimen material property parameters, and associated curing requirement parameters; Step 122: Iteratively optimize and verify the set of key parameters until the preset convergence condition is met to obtain the optimized specimen parameters; Step 123: Use the optimized specimen parameters as the verified specimen.

[0023] In this embodiment, step 121 may include: after receiving the structured entry information, scanning and extracting the unique identifier (such as barcode number) from the entry information data packet according to the predefined parameter template, i.e., the specimen identification information; locating and extracting the specimen material attribute parameters such as strength grade, cement type, water-cement ratio, and aggregate type from the entry information data packet; and parsing the explicitly specified curing specification number, target temperature and humidity value, curing age, and other curing requirement parameters.

[0024] In an optional embodiment of the present invention, step 122, iteratively optimizing and verifying the set of key parameters until a preset convergence condition is met to obtain optimized specimen parameters, may include: Step 1221: Use the set of key parameters as the position information of the initial population particles, and initialize the particle swarm of the preset swarm optimization algorithm; Step 1222: Based on the preset verification rule base, calculate the verification matching degree of the parameter set represented by each particle, and use it as the fitness value of the corresponding particle. Step 1223: Based on the fitness value of each particle and the preset iteration rules, update the velocity and position information of each particle to obtain the updated parameter set population. Step 1224: Based on the updated parameter set population, repeatedly calculate the fitness value and update the velocity and position information of each particle until the preset number of iterations is reached to obtain the final particle; Step 1225: Use the set of parameters represented by the final particles as the optimized specimen parameters.

[0025] In this embodiment, step 1221 can specifically involve vectorizing the set of key parameters, converting each parameter (e.g., intensity level C30 mapped to the value 30, temperature 20℃ mapped to 20) into a numerical vector, which is defined as the position of the particle in the search space. Simultaneously, the algorithm is initialized to perform the following operations: setting the particle swarm size (e.g., 50 particles); using the encoded parameter vector as the initial position of the first particle; to generate the initial population, the system generates the initial positions of the remaining particles near this initial position vector by adding small random perturbations (e.g., random fluctuations within ±5% of each parameter value); randomly initializing a velocity vector for each particle; thus, an initial population containing multiple parameter vector solutions (particle positions) is constructed, with each particle representing a candidate scheme for specimen parameters to be verified and optimized. In this embodiment, the formula for calculating the fitness value F in step 1222 is: ; Where F represents the final specimen parameter verification fitness value, and the value ranges between [0,1]. The basic weight coefficient corresponding to the specimen identification information is a preset constant with a value between (0,1). It is used to characterize the importance of the specimen identification information in the overall verification matching and satisfies α+β+γ=1. The basic verification matching score represents the specimen identification information, with a value range of [0,1]. The deviation value of the specimen identification information matching degree is the absolute difference between the basic matching degree S and the ideal standard matching degree 1, i.e., ΔS=|S 1|, with a value range of [0,1], represents the degree of deviation between the identification information and the ideal state; σ S The standard deviation of the Gaussian correction term for specimen identification information; C S β represents the confidence coefficient for collecting specimen identification information, with a value range of [0.8, 1]; β represents the basic weight coefficient corresponding to the specimen material property parameters, which is a preset constant with a value between (0, 1); M represents the basic matching degree between the specimen material property parameters and the standard material parameter set, with a value range of [0, 1]. This represents the deviation value of the material property parameters of the specimen, which is the absolute difference between the basic matching and the ideal standard matching degree 1, i.e., ΔM = |M 1|, with a value range of [0,1]; σ M This represents the standard deviation of the Gaussian correction term for the material property parameters of the specimen. It is a preset positive number used to adjust the attenuation range and tolerance interval of the material parameter matching degree; C M γ represents the confidence coefficient for the test of the material property parameters of the specimen, with a value range of [0.8, 1]; γ represents the basic weight coefficient corresponding to the specimen's associated curing requirement parameters, which is a preset constant with a value between (0, 1); R represents the basic conformity between the specimen's associated curing requirement parameters and the preset curing specifications, with a value range of [0, 1]; ΔR represents the deviation value of the specimen's curing requirement parameter conformity, which is the absolute difference between the basic conformity R and the ideal standard conformity 1, i.e., ΔR = |R| 1|, with a value range of [0,1]; σ R This represents the standard deviation of the Gaussian correction term for the specimen curing requirements parameter. It is a preset positive number used to adjust the attenuation characteristics and tolerance range of the Gaussian correction for the curing parameter; C R λ represents the confidence coefficient of the specimen curing specification, with a value range of [0.8,1], reflecting the timeliness and effectiveness of the adopted curing specification; λ represents the global penalty coefficient of the total deviation, which is a preset constant with a value range of (0,5); K represents the global normalization factor, which is a preset constant with a value of 0.95, used to constrain the final fitness value F within the interval [0,1].

[0026] In this embodiment, for each particle in the population, the position vector of each particle is decoded and restored to a readable set of parameters (e.g., {ID: A001, Intensity: 30, Temperature: )). 20.5}); For the S value, the format validity of the identifier ID can be checked based on the rule base, whether it already exists in the system (to prevent duplicate entries), whether it belongs to the valid number range, etc. Sub-scores are obtained for each rule passed, and the scores are accumulated and normalized to the S value; For the material property matching degree M, the rule base contains a material compatibility matrix and a standard parameter set. The material parameters in the particles are compared with the standard set, such as checking whether the water-cement ratio corresponds to the reasonable range of the cement type, and whether the aggregate type matches the strength grade; The matching degree is obtained by calculating the deviation between the parameter value and the standard value (after normalization) to obtain the M value; For the curing requirement compliance degree R, the rule base contains curing specification clauses; It is determined whether the curing parameters (such as temperature, humidity, and duration) in the particles meet the mandatory requirements under the declared curing specification; At the same time, the logic between parameters is checked, such as whether the curing temperature and the specimen type are compatible, and the degree of compliance is quantified into the R value.

[0027] In this embodiment, step 1223 updates the velocity and position information of each particle according to its fitness value and a preset iteration rule to obtain an updated parameter set population. Specifically, in each iteration, for each particle, the following steps are performed: Based on the standard particle swarm optimization formula, combined with the particle's current velocity, the difference between its current position and the individual historical best, the difference between its current position and the global historical best, and parameters such as inertia weight and learning factor, a new velocity vector is calculated; the new velocity determines the particle's search direction and step size in the parameter space; the updated velocity is added to the particle's current position to obtain a new position (i.e., a new set of candidate parameter values); where the individual historical best refers to the highest fitness value F reached by each particle in each iteration and its corresponding position; the global historical best refers to the highest fitness value F reached by the entire population in all iterations and its corresponding position.

[0028] In this embodiment, step 1224 involves repeatedly calculating the fitness value and updating the velocity and position information of each particle based on the updated parameter set population, until a preset number of iterations is reached to obtain the final particle. The loop start condition is that the number of iterations has not reached a preset maximum value (e.g., 100 times). In each loop, steps 1222 (calculating the fitness of the new population) and 1223 (updating particle velocity and position) are executed sequentially. After each fitness calculation, the individual historical best of each particle and the global historical best of the entire population are updated synchronously. When the loop termination condition is met (the maximum number of iterations is reached), the algorithm stops. At this time, the particle with the highest global fitness value and the global historical best is selected as the final particle.

[0029] In this embodiment, step 1225 uses the set of parameters represented by the final particle as the optimized specimen parameters. This can be achieved by decoding and restoring the position vector of the final particle into a set of specific specimen parameter values ​​that have been optimized and verified by the algorithm, i.e., the optimized specimen parameters. This set of parameters is the solution with the highest comprehensive matching degree under the rule constraints.

[0030] In this embodiment, step 123, using the optimized specimen parameters as the verified specimen, may include: encapsulating the output optimized specimen parameters into data, that is, encapsulating this set of parameters according to the verified specimen data structure defined internally by the system to obtain the verified specimen, including: standardizing and rounding certain continuous values ​​(such as temperature 20.5℃) or using a more precise representation according to the verified specimen data structure defined internally by the system; updating the status of the specimen from pending verification to verified in the system database; generating a verification log, recording the verification time, the final parameters used, the calculated fitness value F, etc., and permanently associating it with the unique identifier of the specimen to obtain a verified specimen.

[0031] In this embodiment, by initializing a preset group and iteratively optimizing in the parameter solution space, the system can actively seek the optimal matching solution under rule constraints. This design allows the system to automatically find a parameter set with the highest fitness F even if the original data has slight deviations, is incomplete, or some parameters are at fuzzy boundaries. This enables intelligent correction and completion of the original data, significantly reducing the overall rejection rate caused by minor information flaws and improving data entry efficiency. By constructing a fitness function that integrates the validity of the identifier S, the material matching degree M, and the compliance with the maintenance specifications R, the verification process is transformed from a single-dimensional yes / no judgment to a multi-dimensional comprehensive scoring. This improves the accuracy of subsequent determination of maintenance environment parameters, thereby further ensuring the accuracy and completeness of the verified specimen release information.

[0032] In an optional embodiment of the present invention, step 13, determining the target curing environment parameters based on the verified specimen and preset curing requirements, may include: Step 131: Analyze the verified specimen to obtain the specimen material property parameters and associated curing requirement parameters contained in the verified specimen; Step 132: Based on the preset curing parameter knowledge base, match the benchmark curing parameters according to the specimen material property parameters and the associated curing requirement parameters; Step 133: Dynamically adjust the baseline maintenance parameters according to the preset maintenance requirements to determine the target maintenance environment parameters.

[0033] In this embodiment, step 131 can specifically be as follows: after receiving the verified specimen data structure, extract the material property parameters and associated curing requirement parameters of the verified specimen from the verified specimen; wherein, the material property parameters include: concrete strength grade (e.g., C50), cementitious material type (e.g., ordinary Portland cement PO 42.5), water-cement ratio (0.38), aggregate particle size (25mm), admixture type (fly ash, mineral powder), admixture type (polycarboxylate superplasticizer), and the geometric characteristics of the specimen (size, specific surface area), etc.; the curing requirement parameters include: the curing standard number associated with the specimen, the target temperature and humidity value (e.g., high-heat concrete temperature control curve), and the planned curing age (e.g., 7d, 28d, 56d), etc.

[0034] In this embodiment, the curing parameter knowledge base includes a hybrid knowledge graph of historical best curing cases, national standard provisions, and material science mechanisms. Step 132 can be as follows: using the specimen material attribute parameters and associated curing requirement parameters parsed in step 131 as indexes, directly retrieve the benchmark curing curve (i.e., 20±2℃, 95%RH, 28d) under the specified standard from the knowledge base. During the retrieval process, if a corresponding benchmark curing curve exists, proceed directly to step 133 for dynamic fine-tuning. If there is no specific standard number, use the set of material attribute parameters (strength, water-cement ratio, cement type) as joint query conditions to retrieve curing cases with similar material combinations from the knowledge base. Use the K-nearest neighbor algorithm to calculate the Euclidean distance between the current specimen material vector and the historical specimen vectors in the knowledge base, select the 3-5 closest successful curing cases, and extract their environmental parameters as theoretically recommended curing benchmarks (i.e., benchmark curing parameters).

[0035] In this embodiment, step 133 can specifically involve using the baseline maintenance parameters output in step 132 (such as target temperature 20℃ and humidity 95%) as the target constraint center value, and dynamically adjusting and optimizing the baseline maintenance parameters based on preset maintenance requirements to determine the target maintenance environment parameters. The preset maintenance requirements include hard constraint boundaries and soft constraint boundaries. The hard constraint boundaries refer to the physical limits of the maintenance room equipment (such as minimum cooling temperature and maximum humidification rate); the soft constraint boundaries refer to energy-saving optimization targets (such as allowing a slight decrease in nighttime temperature to reduce electricity costs while meeting the intensity development curve). During the optimization process... In this embodiment, the baseline maintenance parameters are adjusted and optimized using preset hard and soft constraint boundaries as adjustment parameters to obtain maintenance environment parameters. In this embodiment, step 133 is mainly used to fine-tune the baseline maintenance curve to meet energy-saving requirements when the corresponding baseline maintenance curve exists in the maintenance parameter knowledge base (fine-tuning is mainly used for further optimization of maintenance parameters, or the parameters of the baseline maintenance curve can be used directly as the target maintenance environment parameters for subsequent maintenance without fine-tuning). When the corresponding baseline maintenance curve does not exist in the maintenance parameter knowledge base, the theoretically recommended maintenance baseline is subject to boundary restrictions to prevent exceeding the preset boundary.

[0036] In this embodiment, step 14 parses the target maintenance environment parameters into specific environmental control instructions and distributes the corresponding instructions to the intelligent maintenance room. Specifically, the determined target maintenance environment parameters (e.g., target temperature 20℃, target humidity 95%) are sent to the intelligent maintenance room. The intelligent maintenance room obtains the current real-time monitored temperature and humidity values ​​and compares them with the target values ​​(target maintenance environment parameters) to calculate the deviation. When the deviation is greater than a preset range, specific equipment operation instructions are generated based on the deviation. For example, if the temperature is lower than the target value and exceeds the preset range, a heating start instruction is sent to the heater and the output power is set. If the humidity is too low, a humidification start instruction is sent to the humidifier. If cooling or dehumidification is required, the corresponding cooling or dehumidification equipment is activated. These instructions are distributed to various actuators (heater, refrigeration compressor, humidification valve, fan, etc.) in the maintenance room.

[0037] In an optional embodiment of the present invention, step 15, which involves real-time monitoring of environmental parameters within the curing room and comparing them with target curing environment parameters to obtain a comparison result, may include: Step 151: Based on multiple preset sensors in the curing room, monitor the environmental parameters in the curing room in real time, and extract the average temperature and average humidity of the entire curing area as the actual environmental state vector. Step 152: Compare the actual state vector with the target maintenance environment parameters at the current moment item by item, and calculate the instantaneous deviation corresponding to each state vector; Step 154: Determine the comparison result based on the instantaneous deviation.

[0038] In this embodiment, the specific implementation process of step 15 is as follows: The curing room is equipped with a temperature and humidity sensor array (temperature and humidity transmitter, fiber optic grating sensor), and a surface temperature infrared thermal imager for the specimen. The system synchronously collects the raw data of each sensor at a period of 50ms and performs the following processing: Based on the temperature and humidity parameters transmitted by each sensor, the average temperature and average humidity of the entire curing area are extracted as the actual environmental state vector; the actual state vector is compared with the target curing environment parameters at the current moment item by item, and the instantaneous deviation value is calculated, which is the absolute value of the difference between the two; based on the instantaneous deviation, the comparison result is determined. Specifically, when the instantaneous deviation value exceeds the preset deviation range value, an abnormal comparison item is output; when the instantaneous deviation value is within the preset deviation range value, a normal comparison result is output.

[0039] In an optional embodiment of the present invention, step 16, which dynamically adjusts the intelligent maintenance room based on the comparison results until the actual environmental parameters stabilize within the target range, may include: Step 161: When the comparison result is abnormal, obtain the parameter value of the abnormal comparison item in the actual state vector corresponding to the abnormal time and the target parameter value of the corresponding item in the target maintenance environment parameter at the current time. Step 162: Compare the parameter value of the anomaly comparison item with the target parameter value of the corresponding item in the target maintenance environment parameters at the current time. If the parameter value of the anomaly comparison item is greater than the target parameter value of the corresponding item in the target maintenance environment parameters at the current time, output a downward adjustment signal; if the parameter value of the anomaly comparison item is less than the target parameter value of the corresponding item in the target maintenance environment parameters at the current time, output an upward adjustment signal. Step 163: Dynamically adjust the intelligent maintenance room according to the down-adjustment signal and / or up-adjustment signal until the actual environmental parameters stabilize within the target range.

[0040] In this embodiment, in step 162, the downward adjustment signal indicates that the currently detected actual value is greater than the target value and exceeds the preset range, requiring downward adjustment to approach the target value; the upward adjustment signal indicates that the currently detected actual value is less than the target value and exceeds the preset range, requiring upward adjustment to approach the target value. In this embodiment, in step 163, the intelligent maintenance room is adjusted according to the dynamic and instantaneous deviation values ​​of the downward adjustment signal and / or upward adjustment signal until the actual environmental parameters stabilize within the target range. Specifically, when a downward adjustment signal or an upward adjustment signal is received, a corresponding control command is sent to the adjustment device to control the corresponding adjustment device to adjust the temperature and humidity until the output of the comparison result at the next moment is a normal comparison result. For example, taking temperature as an example, if the output of the downward adjustment signal means that the actual temperature is higher than the upper limit of the target temperature and exceeds the preset range, then cooling or ventilation is started to lower the temperature; if the actual temperature is lower than the lower limit of the target temperature, then heating is started to raise the temperature; the same applies to humidity, if it is too high, dehumidification is started, and if it is too low, humidification is started, until the output of the comparison result at the next moment is a normal comparison result, then the adjustment ends.

[0041] In a preferred embodiment, the intelligent maintenance room is dynamically adjusted according to the down-adjustment signal and / or up-adjustment signal until the actual environmental parameters stabilize within the target range. This further includes: obtaining the instantaneous deviation value corresponding to the abnormal comparison item in the actual state vector at the abnormal moment; comparing the instantaneous deviation value with a first threshold; if the instantaneous deviation value is greater than the first threshold, controlling the adjustment device to operate at full power; if the instantaneous deviation value is less than the first threshold, controlling the adjustment device to maintain low power.

[0042] In this embodiment, by setting different adjustment methods, hierarchical control can be achieved during the adjustment process, thereby enabling the fastest adjustment in the shortest time while preventing over-adjustment and further ensuring the stability of the adjustment, thus ensuring that the actual environmental parameters can remain stable within the target range.

[0043] In an optional embodiment of the present invention, step 17, after reaching the preset curing time and the environmental parameters continuously meeting the standards, generates a curing completion signal and marks the specimens at the curing completion signal level as cured specimens, including: Step 171: Retrieve the full historical environmental records of the current specimen during the curing period, and calculate the deviation between the actual temperature and humidity at each moment during the entire curing period and the temperature and humidity in the target temperature and humidity curve. Step 172: Determine whether there are any moments when the environmental parameters exceed the preset soft or hard constraint boundaries; if the cumulative deviation is within the allowable range and there are no records of exceeding the limits, it is determined that the environmental parameters are continuously compliant; otherwise, it is determined that they are non-compliant. Step 173: When both the curing time has reached the standard (preset curing time) and the environmental parameters continue to meet the standard, a curing completion signal is generated, and the specimens in the curing completion signal are marked as cured specimens.

[0044] In this embodiment, step 17 is implemented as follows: An independent curing timer is set up, which begins accumulating data after verification in step 12 and when the specimen officially enters the curing station. When the accumulated time reaches the planned total curing time (e.g., 28 days ± 1 hour) parsed in step 131, a preliminary completion candidate state is triggered. Simultaneously, the entire historical environmental record of the specimen during the curing cycle is retrieved, and the deviation between the actual temperature and humidity at each moment during the entire curing cycle and the temperature and humidity in the target temperature and humidity curve is calculated. It is checked whether any environmental parameter exceeds the preset soft / hard constraint boundary (e.g., the temperature fluctuation required by national standards ≤ 2℃). If the accumulated deviation (i.e., the sum of the number of deviations) is within the allowable range and there are no records of exceeding the boundary, it is determined that the environmental parameter continuously meets the standard; otherwise, it is determined that it does not meet the standard. When a non-compliance judgment occurs, the system automatically extends the curing time. The system compensates for the loss of effective curing time due to environmental non-compliance and generates a deviation compensation report. When both the curing duration and environmental parameters are met simultaneously, a curing completion signal is generated, and the specimens with the curing completion signal are marked as post-curing specimens. Specifically, the system performs the following operations: a curing completion event is recorded for the specimen in the database, including a precise completion timestamp (accurate to the second), cumulative effective curing time, environmental compliance confidence level, and other metadata; the status of the specimen is updated from curing to curing completion, and the specimen is marked as a post-curing specimen; the information of the marked specimen is automatically pushed to the outbound cache queue, triggering pre-preparation actions and starting to actively collect the full-process quality data package associated with the specimen; at this point, the physical specimen officially becomes a post-curing specimen with a complete digital history, awaiting outbound instructions.

[0045] In an optional embodiment of the present invention, step 18, when receiving the outbound instruction for the cured specimen, generates quality inspection data related to the cured specimen, including: Step 181: Parse the outbound instruction of the cured specimen to obtain the identifier of the outbound specimen corresponding to the outbound instruction; Step 182: Match the unique identifier of the specimen in the instruction with the list of specimens in the cured state to obtain the target matching result; Step 183: Based on the target matching results, generate the corresponding quality inspection data related to the cured specimens.

[0046] Step 183, based on the target matching results, generates corresponding quality inspection data related to the cured specimens, which may include: Step 1831: Based on the target matching results, retrieve the basic information of the current cured specimen, the whole process environmental monitoring data, the process inspection and anomaly handling data, and the physical performance test data. Step 1832: Associate the basic information, full-process environmental monitoring data, process inspection and anomaly handling data, and physical performance test data with the current cured specimen identifier and output them to generate quality test data related to the cured specimen.

[0047] In this embodiment, the basic information includes: unique identifier, concrete strength grade, mix proportion number, molding date, production batch, and project location, which can be retrieved from the verification data in step 12; the full-process environmental monitoring data includes: actual temperature and humidity, ventilation frequency, curing medium concentration, and other environmental parameters at each time point during the curing period, as well as records of deviations between environmental parameters and target values, dynamic adjustment action logs, and environmental anomaly alarm records, which can be extracted from the curing log in step 14; the physical performance testing data includes: core quality testing indicators (such as compressive strength) after the specimen curing is completed. The data sources are divided into two categories: real-time test results uploaded by automated testing equipment (such as pressure testing machines) and manually entered laboratory test reports (which must include traceability information such as the testing personnel, equipment number, and testing time); process inspection and anomaly handling data, including: records of manual or intelligent inspections during the curing process (such as whether the specimen appearance is intact, whether there are cracks, deformation, etc.), image recognition records (such as the internal temperature distribution of the specimen detected by infrared thermal imaging, and surface defects identified by vision), and abnormal working condition handling records (such as the handling measures after environmental parameters exceed the standard, and curing time compensation records, etc.).

[0048] In an optional embodiment of the present invention, step 19, comparing the quality inspection data with preset outbound conditions to determine whether outbound shipment is permitted, and controlling the execution of the outbound operation when outbound shipment is permitted, includes: Step 191: Compare each test data in the quality inspection data with the corresponding qualified threshold in the preset outbound conditions; Step 192: If all the test data meet the corresponding qualified threshold, then it is determined that the goods can be released from the warehouse, and a release permission signal is generated. Step 193: Based on the outbound permission signal, control the outbound actuator in the intelligent curing room to move the cured specimen out of the curing room, update the inventory record and generate an outbound report.

[0049] In this embodiment, the quality inspection data includes at least one of the following: basic information of the current cured specimen, full-process environmental monitoring data, process inspection and anomaly handling data, and physical performance test data. When comparing, the difference between the current quality inspection data and the preset outbound conditions can be calculated to see if it is within a preset range. If it exceeds the preset range, it is judged as unqualified; if it is within the preset range, it is qualified. If it is determined that outbound is allowed, a standardized outbound allowance signal is generated, which includes the specimen's unique identifier, judgment time, judgment basis (preset outbound conditions), reviewer, and other information. The signal is encrypted to ensure that it cannot be tampered with. If it is determined that outbound is not allowed, an outbound rejection signal is generated, which clearly indicates the reason for not allowing outbound (e.g., compressive strength test value 28MPa < qualified threshold 30MPa), details of abnormal indicators, and automatically triggers the abnormal handling process (e.g., re-inspection, extension of curing time, marking as unqualified specimen). The outbound judgment result (allowed / not allowed) and the corresponding signal are synchronously recorded to the specimen's full life cycle database and pushed to the outbound execution agency, inventory management system, and operator terminal to ensure information synchronization at each stage.

[0050] In this embodiment, step 193, based on the outbound permission signal, controls the outbound execution mechanism in the intelligent curing room to move the cured specimen out of the curing room, and updates the inventory record and generates an outbound report, may include: Based on the outbound permission signal, the outbound execution mechanism in the intelligent curing room is controlled to move the cured specimens out of the curing room. The system integrates quality inspection data, comparison results, outbound judgment results, execution operation logs, and inventory change records to automatically generate a standardized specimen outbound report. The report includes full lifecycle traceability information of the specimens, quality inspection conclusions, outbound judgment basis, and execution process records. After the report is generated, it is automatically archived to the engineering quality archive and simultaneously pushed to the upstream management system, completing the closed loop of fully automated control.

[0051] like Figure 2 As shown, an embodiment of the present invention also provides a specimen entry and exit control device 20 for an intelligent maintenance system, comprising: The acquisition module 201 is used to acquire the warehousing information of the specimens to be cured; The processing module 202 is used to verify the information of the specimens to be cured based on the warehousing information, and obtain the verified specimens; determine the target curing environment parameters based on the verified specimens and preset curing requirements; parse the target curing environment parameters into specific environmental control instructions and distribute the corresponding instructions to the intelligent curing room; monitor the environmental parameters in the curing room in real time and compare them with the target curing environment parameters to obtain the comparison results; dynamically adjust the intelligent curing room according to the comparison results until the actual environmental parameters are stable within the target range; after the preset curing time is reached and the environmental parameters continue to meet the standards, generate a curing completion signal and mark the specimens with the curing completion signal as cured specimens; when the warehousing instruction of the cured specimens is received, generate quality inspection data related to the cured specimens; compare the quality inspection data with preset warehousing conditions to determine whether warehousing is allowed, and control the execution of the warehousing operation when warehousing is allowed.

[0052] Optionally, based on the warehousing information, the specimens to be cured are verified to obtain verified specimens, including: Based on the warehousing information, extract the set of key parameters of the specimen to be cured; The key parameter set is iteratively optimized and verified until the preset convergence condition is met, and the optimized specimen parameters are obtained. The optimized specimen parameters are used as the validated specimens; wherein, the set of key parameters includes: specimen identification information, specimen material property parameters, and associated curing requirement parameters.

[0053] Optionally, the key parameter set is iteratively optimized and verified until a preset convergence condition is met to obtain optimized specimen parameters, including: The set of key parameters is used as the position information of the initial population particles, and the particle swarm of the preset swarm optimization algorithm is initialized. Based on a pre-defined verification rule base, the verification matching degree of the parameter set represented by each particle is calculated, which is used as the fitness value of the corresponding particle. Based on the fitness value of each particle and the preset iteration rules, the velocity and position information of each particle are updated to obtain the updated parameter set population. Based on the updated parameter set population, the fitness value is repeatedly calculated and the velocity and position information of each particle are updated until the preset number of iterations is reached to obtain the final particle; The set of parameters represented by the final particles is used as the optimized specimen parameters.

[0054] Optionally, the fitness value is calculated using the following formula: ; Where F represents the final specimen parameter verification fitness value, and the value ranges between [0,1]. The basic weight coefficient corresponding to the specimen identification information is a preset constant with a value between (0,1) and satisfying α+β+γ=1; The basic verification matching score represents the specimen identification information, with a value range of [0,1]. The deviation value of the specimen identification information matching degree is represented by ΔS, which is the absolute difference between the basic matching degree S and the ideal standard matching degree 1. 1|, with a value range of [0,1]; σ S The standard deviation of the Gaussian correction term for specimen identification information; C S β represents the confidence coefficient for collecting specimen identification information, with a value range of [0.8, 1]; β represents the basic weight coefficient corresponding to the specimen material property parameters, which is a preset constant with a value between (0, 1); M represents the basic matching degree between the specimen material property parameters and the standard material parameter set, with a value range of [0, 1]. The deviation value of the material property parameters of the specimen is represented by ΔM, which is the absolute difference between the basic matching degree M and the ideal standard matching degree 1. 1|, with a value range of [0,1]; σ M The standard deviation of the Gaussian correction term for the material property parameters of the specimen is represented by a preset positive number; C M γ represents the confidence coefficient for the test of the material property parameters of the specimen, with a value range of [0.8, 1]; γ represents the basic weight coefficient corresponding to the specimen's associated curing requirement parameters, which is a preset constant with a value between (0, 1); R represents the basic conformity between the specimen's associated curing requirement parameters and the preset curing specifications, with a value range of [0, 1]; ΔR represents the deviation value of the specimen's curing requirement parameter conformity, which is the absolute difference between the basic conformity R and the ideal standard conformity 1, ΔR = |R 1|, with a value range of [0,1]; σ R The standard deviation of the Gaussian correction term for the specimen curing requirement parameter is a preset positive number; C R λ represents the confidence coefficient of the specimen curing specification, with a value range of [0.8,1]; λ represents the global penalty coefficient of the total deviation, which is a preset constant with a value range of (0,5); K represents the global normalization factor, which is a preset constant with a value of 0.95, used to constrain the final fitness value F within the interval [0,1].

[0055] Optionally, based on the validated specimens and preset curing requirements, the target curing environment parameters are determined, including: The verified specimens are analyzed to obtain the specimen material property parameters and associated curing requirement parameters contained in the verified specimens; Based on a pre-set curing parameter knowledge base, benchmark curing parameters are matched according to the specimen material property parameters and associated curing requirement parameters; The baseline maintenance parameters are dynamically adjusted according to the preset maintenance requirements to determine the target maintenance environment parameters.

[0056] Optionally, upon receiving the outbound instruction for the cured specimen, quality inspection data related to the cured specimen is generated, including: The outbound instructions of the cured specimens are parsed to obtain the identifier of the outbound specimens corresponding to the outbound instructions; The unique identifier of the specimen in the instruction is matched with the list of specimens in the cured state to obtain the target matching result; Based on the target matching results, generate the corresponding quality inspection data related to the cured specimens.

[0057] Optionally, the quality inspection data is compared with preset outbound conditions to determine whether outbound shipment is permitted, and if outbound shipment is permitted, the outbound operation is controlled to be executed, including: Each test data point in the quality inspection dataset is compared with the corresponding qualified threshold in the preset outbound conditions. If all test data meet the corresponding qualified threshold, it is determined that the goods can be released from the warehouse, and a release permission signal is generated. Based on the outbound permission signal, the outbound actuator in the intelligent curing room controls the outbound execution mechanism to move the cured specimen out of the curing room, and updates the inventory record and generates an outbound report.

[0058] It should be noted that this device corresponds to the method described above, and all implementations in the method embodiments described above are applicable to the embodiments of this device and can achieve the same technical effect. Further details will not be provided in this embodiment.

[0059] Embodiments of the present invention also provide a computing device, including: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the above-described method. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0060] Embodiments of the present invention also provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the above-described method. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0061] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0062] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0063] In the embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0064] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0065] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0066] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0067] Furthermore, it should be noted that in the apparatus and method of the present invention, it is obvious that the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered equivalent solutions of the present invention. Moreover, the steps performing the above series of processes can naturally be executed in the order described, but are not necessarily required to be executed in chronological order; some steps can be executed in parallel or independently of each other. Those skilled in the art will understand that all or any step or component of the method and apparatus of the present invention can be implemented in any computing device (including processors, storage media, etc.) or network of computing devices, in hardware, firmware, software, or a combination thereof. This is something that those skilled in the art can achieve by using their basic programming skills after reading the description of the present invention.

[0068] Therefore, the object of the present invention can also be achieved by running a program or a set of programs on any computing device. The computing device can be a known general-purpose device. Therefore, the object of the present invention can also be achieved simply by providing a program product containing program code implementing the method or apparatus. That is, such a program product also constitutes the present invention, and the storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any known storage medium or any storage medium developed in the future. It should also be noted that in the apparatus and method of the present invention, it is obvious that the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered equivalent to the present invention. Furthermore, the steps performing the above series of processes can naturally be performed in the order described, but are not necessarily required to be performed in chronological order. Some steps can be performed in parallel or independently of each other.

[0069] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for controlling the entry and exit of specimens in an intelligent curing system, characterized in that, include: Obtain the warehousing information of the specimens to be cured; Based on the warehousing information, the specimens to be cured are verified to obtain the verified specimens; Based on the validated specimens and the preset curing requirements, the target curing environment parameters are determined; The target maintenance environment parameters are parsed into specific environmental control instructions, and the corresponding instructions are distributed to the intelligent maintenance room. Real-time monitoring of environmental parameters in the maintenance room, and comparison with target maintenance environmental parameters to obtain comparison results; The intelligent maintenance room is dynamically adjusted based on the comparison results until the actual environmental parameters stabilize within the target range. After the preset curing time is reached and the environmental parameters continue to meet the standards, a curing completion signal is generated, and the specimens that are in the curing completion signal state are marked as cured specimens. When the outbound instruction of the cured specimen is received, quality inspection data related to the cured specimen is generated; The quality inspection data is compared with the preset outbound conditions to determine whether outbound is allowed, and the outbound operation is executed when outbound is allowed.

2. The specimen entry and exit control method of the intelligent curing system according to claim 1, characterized in that, Based on the warehousing information, the specimens to be cured are verified to obtain verified specimens, including: Based on the warehousing information, extract the set of key parameters of the specimen to be cured; The key parameter set is iteratively optimized and verified until the preset convergence condition is met, and the optimized specimen parameters are obtained. The optimized specimen parameters are used as the validated specimens; wherein, the set of key parameters includes: specimen identification information, specimen material property parameters, and associated curing requirement parameters.

3. The specimen entry and exit control method of the intelligent curing system according to claim 2, characterized in that, The key parameter set is iteratively optimized and verified until a preset convergence condition is met, resulting in optimized specimen parameters, including: The set of key parameters is used as the position information of the initial population particles, and the particle swarm of the preset swarm optimization algorithm is initialized. Based on a pre-defined verification rule base, the verification matching degree of the parameter set represented by each particle is calculated, which is used as the fitness value of the corresponding particle. Based on the fitness value of each particle and the preset iteration rules, the velocity and position information of each particle are updated to obtain the updated parameter set population. Based on the updated parameter set population, the fitness value is repeatedly calculated and the velocity and position information of each particle are updated until the preset number of iterations is reached to obtain the final particle; The set of parameters represented by the final particles is used as the optimized specimen parameters.

4. The specimen entry and exit control method of the intelligent curing system according to claim 3, characterized in that, The formula for calculating the fitness value is: ， Where F represents the final specimen parameter validation fitness value; This represents the basic weight coefficient corresponding to the specimen identification information, and satisfies α+β+γ=1; The basic verification matching score represents the specimen identification information. The deviation value of the specimen identification information matching degree is represented by ΔS, which is the absolute difference between the basic matching degree S and the ideal standard matching degree 1. 1|; σ S The standard deviation of the Gaussian correction term for the specimen identification information; C S This represents the confidence coefficient for collecting specimen identification information; β represents the basic weighting coefficient corresponding to the material property parameters of the specimen; M represents the basic degree of matching between the specimen material property parameters and the standard material parameter set; The deviation value of the material property parameters of the specimen is represented by ΔM, which is the absolute difference between the basic matching degree M and the ideal standard matching degree 1. 1|; σ M This represents the standard deviation of the Gaussian correction term for the material property parameters of the specimen; C M This represents the confidence coefficient for the detection of material property parameters of the specimen; γ represents the basic weighting coefficient corresponding to the specimen-related curing requirement parameters; R represents the basic degree of conformity between the specimen-related curing requirements parameters and the preset curing specifications; ΔR represents the deviation value of the specimen curing requirement parameters, which is the absolute difference between the basic compliance R and the ideal standard compliance 1. ΔR = |R| 1|; σ R This represents the standard deviation of the Gaussian correction term for the specimen curing requirement parameter; C R This represents the confidence coefficient of the specimen curing specification; λ represents the global penalty coefficient for total deviation; K represents the global normalization factor, which is used to constrain the final fitness value F within the interval [0,1].

5. The specimen entry and exit control method of the intelligent curing system according to claim 1, characterized in that, Based on the validated specimens and preset curing requirements, the target curing environment parameters are determined, including: The verified specimens are analyzed to obtain the specimen material property parameters and associated curing requirement parameters contained in the verified specimens; Based on a pre-set curing parameter knowledge base, benchmark curing parameters are matched according to the specimen material property parameters and associated curing requirement parameters; The baseline maintenance parameters are dynamically adjusted according to the preset maintenance requirements to determine the target maintenance environment parameters.

6. The specimen entry and exit control method of the intelligent curing system according to claim 1, characterized in that, When the outbound instruction for the cured specimen is received, quality inspection data related to the cured specimen is generated, including: The outbound instructions of the cured specimens are parsed to obtain the identifier of the outbound specimens corresponding to the outbound instructions; The unique identifier of the specimen in the instruction is matched with the list of specimens in the cured state to obtain the target matching result; Based on the target matching results, generate the corresponding quality inspection data related to the cured specimens.

7. The specimen entry and exit control method of the intelligent curing system according to claim 1, characterized in that, The quality inspection data is compared with preset outbound conditions to determine whether outbound shipment is permitted, and if outbound shipment is permitted, the outbound operation is controlled to be executed, including: Each test data point in the quality inspection dataset is compared with the corresponding qualified threshold in the preset outbound conditions. If all test data meet the corresponding qualified threshold, it is determined that the goods can be released from the warehouse, and a release permission signal is generated. Based on the outbound permission signal, the outbound actuator in the intelligent curing room is controlled to move the cured specimen out of the curing room, update the inventory record and generate an outbound report.

8. A specimen entry and exit control device for an intelligent curing system, characterized in that, include: The acquisition module is used to receive the warehousing information of the specimens to be cured; The processing module is used to verify the information of the specimens to be cured based on the warehousing information, and obtain the verified specimens; determine the target curing environment parameters based on the verified specimens and preset curing requirements; parse the target curing environment parameters into specific environmental control instructions and distribute the corresponding instructions to the intelligent curing room; monitor the environmental parameters in the curing room in real time and compare them with the target curing environment parameters to obtain the comparison results; dynamically adjust the intelligent curing room according to the comparison results until the actual environmental parameters are stable within the target range; after the preset curing time is reached and the environmental parameters continue to meet the standards, generate a curing completion signal and mark the specimens with the curing completion signal as cured specimens; when the warehousing instruction of the cured specimens is received, generate quality inspection data related to the cured specimens; compare the quality inspection data with preset warehousing conditions to determine whether warehousing is allowed, and control the execution of the warehousing operation when warehousing is allowed.

9. A computing device, characterized in that, include: A processor, a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The system stores instructions that, when executed on a computer, cause the computer to perform the method as described in any one of claims 1 to 7.

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