A jasmine tea intelligent storage dynamic operation and maintenance management system

Abstract

The present application relates to the technical field of intelligent warehouse operation and maintenance management, and discloses a kind of jasmine tea intelligent warehouse dynamic operation and maintenance management system, comprising: aroma modeling acquisition module, for obtaining batch information, key volatile concentration and determining aroma fidelity rate;Environment quota allocation module, for determining environment quota based on microclimate parameters and allocating storage location;Risk assessment early warning module, for calculating aroma risk index and generating early warning mark;Predictive operation and maintenance management module, for performing equipment trend evaluation and generating operation and maintenance work order;Scheduling decision module, for generating turnover priority queue according to aroma remaining margin and risk index;Job resource scheduling module, for executing joint scheduling to generate job scheduling table;Rebalancing audit module, for updating environment quota and priority queue based on execution receipt, to realize dynamic closed-loop management.The present application realizes dynamic monitoring, intelligent decision and closed-loop adaptive management of jasmine tea warehouse environment, aroma quality and operation and maintenance scheduling.

Description

Technical Field

[0001] This invention belongs to the field of intelligent warehouse operation and maintenance management technology, specifically relating to an intelligent warehouse dynamic operation and maintenance management system for jasmine tea. Background Technology

[0002] The aroma quality of jasmine tea is extremely sensitive to the storage environment. Traditional jasmine tea storage relies heavily on manual experience to control temperature, humidity, and ventilation, lacking precise quantification of the coupling relationship between aroma molecule activity and environmental changes. This easily leads to problems such as aroma decay, off-odor absorption, or quality stratification. Existing storage systems generally adopt a static environmental control mode, which can only passively adjust within preset threshold ranges. It is difficult to achieve dynamic response to environmental deviations, equipment instability, and batch quality differences, resulting in unstable aroma retention and poor product consistency during long-term storage.

[0003] With the development of the Internet of Things and intelligent control technology, some tea storage systems have introduced temperature and humidity monitoring and automated scheduling functions. However, these systems are still limited to environmental data collection and lack a systematic solution that combines aroma quality parameters, microclimate zoning control, and operation and maintenance decisions. In scenarios involving multiple batches, multiple storage areas, and heterogeneous equipment operating collaboratively, the lack of a dynamic management mechanism that can assess aroma fidelity in real time, predict risk trends, and automatically execute operation and maintenance scheduling makes it difficult to meet the quality stability requirements of high-grade jasmine tea during long-term storage. Summary of the Invention

[0004] This invention provides a smart warehousing dynamic operation and maintenance management system for jasmine tea, which solves the technical problems in related technologies such as reliance on human experience for environmental control during jasmine tea storage, lack of quantitative basis for aroma and quality maintenance, inability to dynamically respond to operation and maintenance decisions, and lack of adaptive rebalancing mechanism in the system.

[0005] This invention provides a smart warehouse dynamic operation and maintenance management system for jasmine tea, comprising:

[0006] The aroma modeling and acquisition module is used to acquire the batch set of jasmine tea to be stored, the initial concentration of key volatiles, determine the aroma fidelity, set the target temperature, target humidity and allowable residence time, and collect storage environment and residence monitoring data.

[0007] The environmental quota allocation module is used to determine the environmental quota of a microclimate zone based on the temperature, humidity, oxygen content and light intensity of the microclimate zone, and to allocate batches to the target storage location in combination with the storage capacity, and to implement adjacent placement restrictions according to the compatibility matrix.

[0008] The risk assessment and early warning module is used to calculate the aroma risk index based on the storage environment and resident monitoring data, accumulate the concentration of external odor volatiles over time to obtain the cumulative odor exposure of the batch, and generate early warning markers;

[0009] The predictive maintenance management module is used to assess the trends of critical equipment based on early warning markers, generate predictive maintenance work orders, and perform mutually exclusive locking of operations in affected warehouse areas.

[0010] The scheduling decision module is used to determine the remaining aroma margin based on the predicted aroma fidelity rate, and to calculate the comprehensive priority score based on the aroma risk index and the pre-operation time to form a turnover priority queue.

[0011] The job resource scheduling module is used to perform joint scheduling based on predictive maintenance work orders, turnover priority queues and warehouse location relocation lists, generate job scheduling tables, and execute the allocation of time slots, warehouse locations, equipment and personnel.

[0012] The rebalancing audit module is used to perform closed-loop rebalancing and decision audits based on actual receipts and the latest monitoring data, and to update the microclimate zone environmental quotas, target storage locations, aroma risk index, and turnover priority queue.

[0013] Furthermore, the storage environment and residence monitoring data include: actual temperature, actual humidity, actual oxygen content, concentration of external odor volatiles, and actual residence time.

[0014] Furthermore, the ratio of the actual concentration of each key volatile compound in the current batch of jasmine tea to its initial concentration is calculated. This ratio is then compared with 1, and the smaller of the two values ​​is taken as the aroma retention coefficient of the key volatile compound. The aroma retention coefficients of all key volatile compounds are weighted and summed to obtain the aroma fidelity of this batch of jasmine tea at the current moment.

[0015] Furthermore, environmental quotas for microclimate zones are determined, and batches are allocated to target storage locations based on storage capacity. Adjacent placement restrictions are implemented according to a compatibility matrix, including:

[0016] Step 11: Combine the temperature, humidity, oxygen content and light intensity of each microclimate zone into an environmental parameter vector, obtain the target environmental parameter vector for each batch, calculate the difference vector between the two, and perform a weighted summation to obtain the environmental matching function value.

[0017] Step 12: Using the environmental matching function value as the optimization objective, select the microclimate zone with the smallest environmental matching function value as the target quota zone, and generate an environmental quota matching table between the batch and the microclimate zone.

[0018] Step 13: Based on the environmental quota matching table and the storage capacity of each microclimate zone, establish a batch and storage location allocation constraint model. Each batch is allocated to only one target storage location, and the total number of allocated batches in each microclimate zone does not exceed its capacity. Based on the compatibility matrix, adjacent storage locations of incompatible batches are subject to adjacent placement restrictions. An element of 0 in the compatibility matrix indicates that adjacent placement is prohibited, and an element of 1 indicates that adjacent placement is allowed. By solving the allocation constraint model, the correspondence table between batches and storage locations is obtained.

[0019] Furthermore, an aroma risk index is calculated, and the cumulative odor exposure of each batch is obtained by summing the concentrations of external odor volatiles over time. Warning markers are then generated, including:

[0020] Step 21: Obtain the target environmental parameter vector and allowable residence time corresponding to the batch. Based on the actual temperature, actual humidity and oxygen content recorded in the storage environment and residence monitoring data, calculate the difference between each actual parameter and each target parameter and divide by each target parameter. After weighted summation, obtain the environmental deviation degree. Obtain the residence time deviation by the absolute difference between the allowable residence time and the actual residence time. Obtain the aroma risk index by weighted summation of the environmental deviation degree and the residence time deviation.

[0021] Step 22: Extract the concentration of external odor volatiles from the storage environment and residence monitoring data. When the change rate of external odor volatiles concentration is lower than the set change rate threshold, it is accumulated at a fixed sampling interval. When the change rate exceeds the set change rate threshold, it is accumulated at a dynamically shortened sampling interval. The product of the external odor volatiles concentration at each sampling time and the sampling interval is accumulated successively to obtain the batch cumulative odor exposure.

[0022] Step 23: Compare the aroma risk index with the preset risk threshold, and compare the cumulative odor exposure of the batch with the odor exposure threshold. When any comparison result reaches the corresponding threshold, generate a warning mark for the batch.

[0023] Furthermore, trend assessments are performed on critical equipment to generate predictive maintenance work orders, and mutually exclusive lockouts are implemented for affected storage areas, including:

[0024] Step 31: Determine the set of reservoir area numbers based on the early warning markers, extract the operating parameter sequences of key equipment in each reservoir area, calculate the difference between adjacent sampling times for each operating parameter sequence and perform a weighted average to obtain the equipment parameter change rate;

[0025] Step 32: When the absolute value of the rate of change of any equipment parameter exceeds the equipment trend threshold, mark the corresponding equipment as equipment requiring predictive maintenance, and write the equipment number, the warehouse area number, and the corresponding parameter rate of change into the trend assessment result table; generate a predictive maintenance work order table based on the trend assessment result table, and record the equipment number, warehouse area number, and work order generation time.

[0026] Step 33: Perform mutual exclusion locking based on the set of warehouse area numbers in the predictive maintenance work order table. When there is an active work order in any warehouse area, set the locking status of the warehouse area to 1, and prohibit loading, unloading, moving and redistribution operations in the warehouse area before the lock is released.

[0027] Further, determine the remaining aroma margin, calculate the overall priority score, and form a turnover priority queue, including:

[0028] Step 41: Based on the trend of aroma fidelity change in the historical monitoring data of the batch and the current environmental deviation, calculate the predicted aroma fidelity by time series extrapolation. Calculate the aroma margin based on the difference between the predicted aroma fidelity and the set minimum aroma fidelity threshold. When the difference is less than zero, take zero as the aroma margin.

[0029] Step 42: Multiply the reciprocals of the aroma risk index, aroma margin, and pre-operation time by their respective weighting coefficients and sum them to obtain the comprehensive priority score;

[0030] Step 43: Sort all batches by their overall priority scores in descending order, generate a turnover priority queue, and record the queue generation time.

[0031] Furthermore, the scheduling decision module also includes a priority reconfiguration mechanism, including:

[0032] Step 51: Continuously monitor the rate of change of temperature, humidity, oxygen content and light intensity in each microclimate zone. When the rate of change of any parameter exceeds the corresponding set rate of change threshold in three consecutive sampling periods, it is determined that the microclimate zone has an abnormal fluctuation and all batches in the microclimate zone are marked as affected batches.

[0033] Step 52: Calculate the risk increment based on the difference between the aroma risk index of the affected batch in the current scheduling cycle and the previous scheduling cycle, and calculate the aroma margin decay rate based on the difference between the predicted aroma fidelity rate at the current moment and the predicted aroma fidelity rate at the previous moment and the ratio of the predicted aroma fidelity rate at the previous moment.

[0034] Step 53: The risk increment and the aroma margin decay rate are weighted and summed to obtain the abnormal priority. The abnormal priority is inserted into the turnover priority queue. The comprehensive priority scores of all batches are reordered in descending order of abnormal priority to generate a temporary emergency queue. Priority operation instructions are triggered for abnormal batches.

[0035] Furthermore, a work schedule is generated, and time slots, storage locations, equipment, and personnel are allocated, including:

[0036] Step 61: Construct a joint task set based on the predictive maintenance work order table, turnover priority queue and warehouse relocation list. Establish the work location, duration, required equipment and personnel, task priority weight and task predecessor relationship for each task. Use the sum of task priority weight weighted delay amount, resource conflict amount and location change cost as the objective function to form a joint scheduling model.

[0037] Step 62: Establish mixed integer linear constraints for time indexing in the joint scheduling model. The constraints include: task start uniqueness, task predecessor dependency, resource non-overlap, reservoir locking, capacity and adjacency compatibility constraints. Solve the constraints using the branch and bound algorithm to obtain the final task start time slot and resource assignment results.

[0038] Step 63: Generate a job scheduling table based on the final task start time slot and resource assignment results, specifying the execution time slot, storage location, equipment number and personnel number of each task, and sorting parallel tasks with the same score in order of work order level, duration and conversion cost.

[0039] Furthermore, a closed-loop rebalancing and decision audit will be implemented to update microclimate zone environmental quotas, target storage locations, aroma risk indices, and turnover priority queues, including:

[0040] Step 71: Calculate the environmental deviation, residence time deviation, aroma risk index, and batch cumulative odor exposure based on the execution receipt of the work schedule and the latest storage environment and residence monitoring data.

[0041] Step 72: Based on the aroma risk index and cumulative off-odor exposure of each batch obtained in Step 71, recalculate the environmental matching function value of each batch and update the compatibility matrix under capacity and compatibility constraints. At the same time, calculate the aroma margin with the latest predicted aroma fidelity and recalculate the comprehensive priority score to generate an updated turnover priority queue.

[0042] Step 73: Generate a decision snapshot of the updated compatibility matrix, turnover priority queue and aroma risk index, calculate the hash fingerprint as the version number, perform consistency verification to confirm the compliance of the locking state and capacity constraints, and write the updated result that passes the verification into the audit database as the current effective version.

[0043] The beneficial effects of this invention are as follows: By constructing an intelligent dynamic operation and maintenance management system for jasmine tea storage, this invention achieves the coordinated integration of storage environment control, aroma quality assessment, and operation and maintenance decision-making. Real-time perception of aroma change trends is achieved by using environmental deviation, aroma risk index, and off-odor exposure as core indicators. The matching relationship between batches and storage locations is optimized through environmental quota allocation and compatibility matrix constraints, improving storage space utilization and environmental balance. A predictive operation and maintenance mechanism is introduced to analyze the operating trends of key equipment and generate work orders, preventing fault propagation. The constructed joint scheduling model achieves multi-objective optimization in task, resource, and space dimensions, improving scheduling efficiency and execution determinism. The rebalancing audit module forms a closed-loop control through execution feedback and hash audit mechanisms, achieving system self-correction and traceability. Overall, this invention improves the aroma stability, automation level, and decision-making security of jasmine tea storage. Attached Figure Description

[0044] Figure 1 This is a schematic diagram of a module of a smart warehouse dynamic operation and maintenance management system for jasmine tea according to the present invention. Detailed Implementation

[0045] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, features described in some examples may be combined in other examples.

[0046] It should be noted that, unless otherwise defined, the technical or scientific terms used in one or more embodiments of the present invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in one or more embodiments of the present invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0047] like Figure 1As shown, a smart warehouse dynamic operation and maintenance management system for jasmine tea includes:

[0048] Aroma modeling and acquisition module 1 is used to acquire the batch set of jasmine tea to be stored, the initial concentration of key volatiles, determine the aroma fidelity, set the target temperature, target humidity and allowable residence time, and collect storage environment and residence monitoring data.

[0049] The environmental quota allocation module 2 is used to determine the environmental quota of the microclimate zone based on the temperature, humidity, oxygen content and light intensity of the microclimate zone, and allocate the batch to the target storage location in combination with the storage capacity, and implement adjacent placement restrictions according to the compatibility matrix.

[0050] Risk assessment and early warning module 3 is used to calculate the aroma risk index based on the storage environment and residence monitoring data, accumulate the concentration of external odor volatiles over time to obtain the cumulative odor exposure of the batch, and generate early warning markers;

[0051] The predictive maintenance management module 4 is used to assess the trends of critical equipment based on early warning markers, generate predictive maintenance work orders, and perform mutually exclusive locking of operations in the affected warehouse area.

[0052] Scheduling decision module 5 is used to determine the remaining aroma margin based on the predicted aroma fidelity rate, calculate the comprehensive priority score based on the aroma risk index and the pre-operation time, and form a turnover priority queue.

[0053] The job resource scheduling module 6 is used to perform joint scheduling based on predictive maintenance work orders, turnover priority queues and warehouse location relocation lists, generate job scheduling tables, and perform time slot, warehouse location, equipment and personnel allocation.

[0054] The rebalancing audit module 7 is used to perform closed-loop rebalancing and decision audits based on actual receipts and the latest monitoring data, and to update the microclimate zone environmental quotas, target storage locations, aroma risk index and turnover priority queue.

[0055] In one embodiment of the present invention, the system acquires a set of batches of jasmine tea to be stored, wherein each batch corresponds to a unique batch identifier and production traceability information. For each batch, the initial concentration of key volatiles before storage is collected. Key volatiles refer to representative molecular components that can significantly affect the aroma characteristics of jasmine tea, including but not limited to linalool, phenylethyl alcohol, and methyl jasmonate.

[0056] The target temperature and target humidity are the environmental conditions required to ensure stable adsorption of aroma molecules and inhibit oxidation reactions, respectively; the allowable residence time indicates the maximum storage time of a specific batch without significant aroma decay.

[0057] The storage environment and residence monitoring data include: actual temperature, actual humidity, actual oxygen content, external odor volatile concentration, and actual residence duration. Actual temperature and humidity are collected in real-time by a multi-point sensor network deployed within the microclimate zone; actual oxygen content reflects the intensity of the oxidative environment in the sealed storage location; external odor volatile concentration is detected by a gas sensor array to measure the content of non-tea-derived organic matter in the air, used to assess odor exposure levels; and actual residence duration is calculated from the time difference between the system's entry time and the current time.

[0058] In one embodiment of the present invention, the ratio of the actual concentration of each key volatile compound in the current batch of jasmine tea at the current moment to its initial concentration is calculated. This ratio is compared with 1, and the smaller of the two values ​​is taken as the aroma retention coefficient of the key volatile compound. The aroma retention coefficients of all key volatile compounds are weighted and summed to obtain the aroma fidelity of this batch of jasmine tea at the current moment. The aroma fidelity ranges from 0 to 1, where a value close to 1 indicates high aroma retention, and a value close to 0 indicates severe aroma attenuation.

[0059] In one embodiment of the present invention, the system divides the storage space into several independently controllable microclimate zones. Each microclimate zone is equipped with an independent temperature and humidity control and gas circulation unit, capable of adjusting temperature, humidity, oxygen content, and light intensity within a certain range. Based on the temperature, humidity, oxygen content, and light intensity of each microclimate zone, an environmental quota is determined, and batches are allocated to target storage locations in conjunction with storage capacity. Adjacent placement restrictions are implemented according to a compatibility matrix, including:

[0060] Step 11: Combine the temperature, humidity, oxygen content and light intensity of each microclimate zone into an environmental parameter vector, obtain the target environmental parameter vector for each batch, calculate the difference vector between the two, and perform a weighted summation to obtain the environmental matching function value. The environmental matching function value is used to represent the degree of environmental adaptability between the batch and the microclimate zone. The smaller the value, the smaller the environmental difference and the higher the matching degree.

[0061] Step 12: Using the environmental matching function value as the optimization objective, select the microclimate zone with the smallest environmental matching function value as the target quota zone, and generate an environmental quota matching table between the batch and the microclimate zone. This matching table records the target environmental area allocation results for each batch. This step is used to perform batch-level environmental selection among all microclimate zones to determine the macro-environmental affiliation.

[0062] Step 13: Based on the environmental quota matching table and the storage capacity of each microclimate zone, establish a batch-to-storage allocation constraint model. The objective function of the allocation constraint model is: `min` represents the minimum value operation, `Z` represents the value of the objective function of the allocation constraint model, `N` represents the number of jasmine tea batches, `M` represents the number of storage locations in the microclimate zone, `i` represents the batch index, and `j` represents the storage location index. , and These represent the environmental matching weight coefficient, compatibility constraint weight coefficient, and capacity balance weight coefficient, respectively. Indicates the value of the environment matching function. This represents the compatibility penalty coefficient. If batch i is marked as incompatible with batches in adjacent storage locations in the compatibility matrix, then... The value is 1 if it is not 0 otherwise. The storage space utilization penalty coefficient represents the degree of imbalance between the remaining storage capacity and the allocated load. Specifically, when the inventory occupancy rate of the microclimate zone is greater than or equal to 80%, the storage space utilization penalty coefficient is 1, which activates the penalty item; otherwise, it is 0, and no penalty is applied. Let represent the allocation decision variable. If batch i is allocated to storage location j, then... If the value is 1, the value is 0 otherwise. Each batch is allocated to only one target storage location, and the total number of allocated batches in each microclimate zone does not exceed its capacity. Adjacent placement restrictions are implemented for adjacent storage locations of incompatible batches based on a compatibility matrix. The compatibility matrix is ​​a symmetric matrix, where an element of 0 indicates that adjacent placement is prohibited, and an element of 1 indicates that adjacent placement is allowed. By solving the allocation constraint model, a correspondence table between batches and storage locations is obtained to ensure that the final storage location allocation result meets the constraint requirements in terms of capacity, compatibility, and environmental matching. This relationship table determines the final storage location of each jasmine tea batch.

[0063] Through the above steps, this embodiment introduces an environmental matching function minimization strategy to ensure that jasmine tea obtains microclimate conditions that best match its aroma characteristics when it is put into storage, thereby reducing the risk of aroma decay from the source. By combining the allocation constraint model with the compatibility matrix, multi-objective collaborative optimization of batch allocation, space capacity and aroma compatibility is achieved, thereby improving the utilization rate of storage space and the level of aroma protection.

[0064] In one embodiment of the present invention, an aroma risk index is calculated, the concentration of external odor volatiles is accumulated over time to obtain the cumulative odor exposure of the batch, and a warning marker is generated, including:

[0065] Step 21: Obtain the target environmental parameter vector and allowable residence time corresponding to the batch. Based on the actual temperature, actual humidity, and oxygen content recorded in the storage environment and residence monitoring data, calculate the difference between each actual parameter and each target parameter, divide by each target parameter, and then sum the weighted values ​​to obtain the environmental deviation degree. The environmental deviation degree is used to represent the comprehensive difference between the current storage environment and the ideal storage conditions. The larger the value, the more serious the environmental deviation. The residence time deviation is obtained by the absolute difference between the allowable residence time and the actual residence time, which reflects whether the residence time of the batch in the warehouse exceeds the reasonable range. The environmental deviation degree and the residence time deviation are weighted and summed to obtain the aroma risk index. The aroma risk index is a non-negative real number used to represent the comprehensive risk impact of environmental and time factors on aroma quality.

[0066] Step 22: Extract the concentration of external odor volatiles from the storage environment and residence monitoring data. When the rate of change of external odor volatile concentration is lower than the set rate of change threshold, it is accumulated at a fixed sampling interval. When the rate of change exceeds the set rate of change threshold, it is determined that there is an odor disturbance. At this time, it is accumulated at a dynamically shortened sampling interval to improve the time resolution. The product of the external odor volatile concentration at each sampling time and the sampling interval is accumulated successively to obtain the batch cumulative odor exposure. The cumulative odor exposure is used to measure the total amount of external odor impact exposed to the tea sample during the entire storage period.

[0067] Step 23: Compare the aroma risk index with a preset risk threshold, and compare the cumulative off-odor exposure of the batch with the off-odor exposure threshold. When either comparison result reaches the corresponding threshold, generate a warning marker for the batch and record the timestamp and batch number. The off-odor exposure threshold represents the maximum acceptable off-odor exposure, which is the limit of the amount of volatile organic compounds that a tea sample can be exposed to per unit time, used to limit the interference of environmental pollution on aroma. The generated warning marker is used to trigger response actions such as equipment self-check, environmental adjustment, or storage location adjustment.

[0068] This embodiment introduces two quantitative indicators, the aroma risk index and the cumulative odor exposure, enabling the system to simultaneously reflect environmental adaptability deviations and the cumulative effects of external pollution. By employing a dynamic sampling mechanism triggered by a rate of change threshold, the system ensures the timeliness and sensitivity of odor monitoring, achieving proactive monitoring of the storage environment. This allows the invention to identify and adaptively control aroma quality deterioration trends in advance, improving the intelligence level of storage operation and maintenance and the ability to maintain aroma.

[0069] In one embodiment of the present invention, trend assessment is performed on critical equipment to generate predictive maintenance work orders, and mutual exclusion locking is implemented for affected storage areas, including:

[0070] Step 31: Determine the set of storage area numbers based on the early warning markers, and extract the operating parameter sequence of key equipment in each storage area. The operating parameter sequence includes equipment temperature control output, humidity control signal, and fan pressure feedback signal. Calculate the difference between adjacent sampling times for each operating parameter sequence and perform a weighted average according to time weight to obtain the equipment parameter change rate. This change rate is used to represent the dynamic change amplitude of equipment performance. The larger its absolute value, the higher the degree to which the equipment operating state deviates from the steady state.

[0071] Step 32: When the absolute value of the rate of change of any equipment parameter exceeds the equipment trend threshold, the system automatically determines that the equipment has a potential risk of instability, marks the corresponding equipment as equipment requiring predictive maintenance, and writes the equipment number, the warehouse area number, and the corresponding parameter rate of change into the trend assessment result table; a predictive maintenance work order table is generated based on the trend assessment result table, recording the equipment number, warehouse area number, and work order generation time;

[0072] Step 33: To prevent the environment from getting out of control if loading, unloading or moving operations continue when there is a potential risk of equipment instability, the system performs mutual exclusion locking based on the set of warehouse area numbers in the predictive maintenance work order table. When there is an active work order in any warehouse area, the locking status of that warehouse area is set to 1, and loading, unloading, moving and redistribution operations in that warehouse area are prohibited until the lock is released.

[0073] This embodiment uses time-weighted analysis of the operating parameter sequences of key equipment to enable the system to identify trend anomalies in advance when equipment performance fluctuates slightly, avoiding the lag of traditional passive maintenance. Through a mutual exclusion locking mechanism triggered by predictive maintenance work orders, it prevents loading, unloading, or relocation operations from being performed under abnormal equipment conditions, fundamentally avoiding aroma loss caused by temperature and humidity fluctuations in the storage environment, improving the predictability and safety of equipment operation and maintenance, and ensuring the authenticity of aroma and the reliability of the environment during the storage of jasmine tea.

[0074] In one embodiment of the present invention, determining the aroma margin, calculating a comprehensive priority score, and forming a turnover priority queue includes:

[0075] Step 41: Based on the aroma fidelity trend in the historical monitoring data of the batch and the current environmental deviation, calculate the predicted aroma fidelity using time series extrapolation. That is, construct a function of aroma fidelity changing with time based on the historical monitoring data of the batch, with time as the independent variable and aroma fidelity as the dependent variable. Combine the current environmental deviation to correct the function, and use the autoregressive moving average method to extrapolate the aroma fidelity for future times, i.e., the predicted aroma fidelity. Calculate the aroma margin based on the difference between the predicted aroma fidelity and the set minimum aroma fidelity threshold. When the difference is less than zero, take zero as the aroma margin. The aroma margin is used to represent the range of quality degradation that tea can tolerate in the future scheduling cycle. The smaller the value, the closer the batch is to the state that needs priority processing.

[0076] Step 42: Multiply the reciprocals of the aroma risk index, aroma margin, and pre-operation time by their respective weighting coefficients and sum them to obtain the comprehensive priority score. The higher the comprehensive priority score, the higher the risk of aroma quality degradation, the tight operation time, or the insufficient aroma margin of this batch, and the more priority it is to arrange turnover.

[0077] Step 43: Sort all batches by their overall priority scores in descending order, generate a turnover priority queue, and record the queue generation time.

[0078] This embodiment calculates the aroma margin by predicting the difference between the aroma fidelity rate and the minimum threshold, thus directly linking the turnover priority with the aroma quality change trend and achieving quality-driven scheduling. By weighting and integrating the aroma risk index, aroma margin, and pre-operation time, the system automatically seeks the optimal balance between quality risk and operation time, avoiding prolonged retention or neglect of batches with low aroma margin, and ensuring the aroma fidelity and overall turnover efficiency of jasmine tea during storage.

[0079] In one embodiment of the present invention, the scheduling decision module further includes a priority reconstruction mechanism, comprising:

[0080] Step 51: Continuously monitor the rate of change of temperature, humidity, oxygen content and light intensity in each microclimate zone. When the rate of change of any parameter exceeds the corresponding set rate of change threshold in three consecutive sampling periods, it is determined that the microclimate zone has an abnormal fluctuation and all batches in the microclimate zone are marked as affected batches.

[0081] Step 52: Calculate the risk increment based on the difference between the aroma risk index of the affected batch in the current scheduling cycle and the previous scheduling cycle; calculate the aroma margin decay rate based on the difference between the predicted aroma fidelity rate at the current moment and the predicted aroma fidelity rate at the previous moment and the ratio of the predicted aroma fidelity rate at the previous moment. The risk increment reflects the trend of risk change, while the aroma margin decay rate reflects the degree of dynamic deterioration of aroma quality.

[0082] Step 53: The risk increment and the aroma margin decay rate are weighted and summed to obtain the abnormal priority, which is used to measure the urgency of the batch under abnormal environmental conditions. The abnormal priority is inserted into the turnover priority queue. The comprehensive priority scores of all batches are reordered in descending order of abnormal priority to generate a temporary emergency queue, and priority operation instructions are triggered for abnormal batches.

[0083] This embodiment uses a joint assessment of risk increment and aroma margin decay rate to make priority calculation not only based on risk level but also consider the deterioration trend of aroma quality, forming a multi-dimensional decision-making mechanism; through a dynamic priority reconstruction mechanism, affected batches can be processed or redistributed in the shortest possible time, thereby reducing aroma quality loss, improving the overall resilience and reliability of system operation and maintenance, and enabling the system to have predictive perception and autonomous decision-making capabilities under environmental fluctuations.

[0084] In one embodiment of the present invention, generating a job scheduling table and performing time slot, storage location, equipment, and personnel allocation includes:

[0085] Step 61: Construct a joint task set containing maintenance tasks and relocation tasks based on the predictive maintenance work order table, the turnover priority queue, and the warehouse relocation list. For each task, establish the work location, duration, required equipment and personnel, task priority weight, and task predecessor relationship. Use the sum of the task priority weight-weighted delay, resource conflict, and location conversion cost as the objective function to form a joint scheduling model. The task priority weight is determined by the work order level of the predictive maintenance work order, the comprehensive priority score in the turnover priority queue, and the task type mapping value. The delay represents the deviation of the task execution time from the expected start time.

[0086] Resource conflict quantity represents the degree of competition between different tasks for the same type of equipment or personnel in the same time slot. Specifically, the system counts the occupation of equipment and personnel resources by all tasks in each time index. When a resource is occupied by two or more tasks at the same time index, a resource conflict is determined to have occurred. Each time a resource conflict occurs, the conflict quantity increases by 1. That is, the resource conflict quantity is the sum of the number of conflicts of all resources in the time dimension.

[0087] Location switching cost represents the spatial switching cost between tasks, including factors such as the distance of movement between storage locations and the length of the operation path. Specifically, the spatial distance between adjacent tasks is obtained and weighted according to the average movement cost of equipment or personnel to obtain the location switching cost. The location switching costs between adjacent tasks are then summed in the order of the tasks to obtain the total location switching cost.

[0088] Step 62: Establish mixed-integer linear constraints for the joint scheduling model, including: task start uniqueness, task predecessor dependency, resource non-overlap, storage area locking, capacity, and adjacency compatibility constraints. Solve these constraints using a branch-and-bound algorithm to obtain the final task start time slots and resource allocation results. Specifically, the task start uniqueness constraint ensures that each task has only one start time slot; the task predecessor dependency constraint ensures that tasks with dependencies are executed in logical order; the resource non-overlap constraint prevents the same equipment or personnel from participating in multiple tasks simultaneously; the storage area locking constraint ensures that storage area tasks locked by predictive maintenance work orders are not scheduled until the lock is released; and the capacity and adjacency compatibility constraints ensure that storage location allocation conforms to the capacity limit and adjacency placement rules. The branch-and-bound algorithm, through recursive decomposition and boundary pruning mechanisms, gradually narrows the feasible solution space, ultimately obtaining the globally optimal solution that minimizes the objective function, yielding the start time slot and resource allocation results for each task, including the corresponding equipment number, personnel number, and storage location number.

[0089] Step 63: Generate a job scheduling table based on the final task start time slot and resource assignment results, specifying the execution time slot, storage location, equipment number and personnel number of each task, and sorting parallel tasks with the same score in order of work order level, duration and conversion cost to ensure that high-priority tasks are executed first, short-time tasks are allocated first, and low-cost tasks are scheduled into idle time slots first.

[0090] This embodiment establishes a joint scheduling model to incorporate predictive maintenance tasks and tea batch relocation tasks into a unified optimization framework, avoiding time conflicts and resource competition between the two types of tasks. By adopting weighted multi-objective function modeling and branch-bound solution, the equipment, personnel, and storage resources are optimally matched under multi-task conditions, reducing system operating energy consumption and path switching costs, and enabling the jasmine tea storage system to have the ability to intelligently sense, optimize decision-making, and execute efficiently.

[0091] In one embodiment of the present invention, closed-loop rebalancing and decision auditing are performed to update microclimate zone environmental quotas, target storage locations, aroma risk indices, and turnover priority queues, including:

[0092] Step 71: Calculate the environmental deviation, residence time deviation, aroma risk index, and batch cumulative odor exposure based on the execution receipt of the work schedule and the latest storage environment and residence monitoring data.

[0093] Step 72: Based on the aroma risk index and cumulative off-odor exposure of each batch obtained in Step 71, recalculate the environmental matching function value of each batch and update the compatibility matrix under capacity and compatibility constraints. At the same time, calculate the aroma margin with the latest predicted aroma fidelity and recalculate the comprehensive priority score to generate an updated turnover priority queue. This step realizes the dynamic coupling between warehousing environment parameters, batch allocation strategy and scheduling priority, so that the system can automatically complete resource reallocation when the external environment or operating conditions change.

[0094] Step 73: Generate a decision snapshot using the updated compatibility matrix, turnaround priority queue, and aroma risk index. Calculate a hash fingerprint using a hash algorithm as the version number, perform a consistency check to confirm the compliance of the locking state and capacity constraints, ensuring logical consistency and scheduling safety across all storage areas under limited resources and mutual exclusion constraints. Write the updated result that passes the check into the audit database as the current effective version. The hash fingerprint is used to identify the complete state of the current scheduling decision, including parameter configuration, task queue, and risk indicator set.

[0095] This embodiment, through periodic recalculation of environmental deviation, aroma risk index, and off-odor exposure, enables the system to dynamically adjust scheduling parameters based on the latest monitoring data, maintaining the optimal matching state between the microclimate zone and tea quality. The consistency verification mechanism ensures that the locked storage area and capacity constraints are not violated during dynamic scheduling, preventing abnormal operations caused by resource conflicts or data asynchrony. Ultimately, the system achieves a closed-loop operation of monitoring, evaluation, correction, and auditing, promoting the upgrade of the jasmine tea storage system from static control to dynamic control.

[0096] It should be noted that the interval and threshold sizes are set for ease of comparison. The size of the threshold depends on the amount of sample data and the base number set by those skilled in the art for each set of sample data, as long as it does not affect the proportional relationship between the parameter and the quantized value. Furthermore, the above formulas are all dimensionless calculations, and the formulas are derived from software simulations using a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0097] The embodiments of the present invention have been described above, but the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms based on the guidance of the present embodiments, all of which are within the protection scope of the present embodiments.

Claims

1. A smart warehouse dynamic operation and maintenance management system for jasmine tea, characterized in that, include: The aroma modeling and acquisition module is used to acquire the batch set of jasmine tea to be stored, the initial concentration of key volatiles, determine the aroma fidelity, set the target temperature, target humidity and allowable residence time, and collect storage environment and residence monitoring data. The environmental quota allocation module is used to determine the environmental quota of a microclimate zone based on the temperature, humidity, oxygen content and light intensity of the microclimate zone, and to allocate batches to the target storage location in combination with the storage capacity, and to implement adjacent placement restrictions according to the compatibility matrix. Each batch is allocated to only one target storage location, and the total number of allocated batches in each microclimate zone does not exceed its capacity. In the compatibility matrix, an element of 0 indicates that adjacent placement is prohibited, and an element of 1 indicates that adjacent placement is allowed. The risk assessment and early warning module is used to calculate the aroma risk index based on the storage environment and resident monitoring data, accumulate the concentration of external odor volatiles over time to obtain the cumulative odor exposure of the batch, and generate early warning markers; The predictive maintenance management module is used to assess the trends of critical equipment based on early warning markers, generate predictive maintenance work orders, and perform mutually exclusive locking of operations in affected warehouse areas. The scheduling decision module is used to determine the remaining aroma margin based on the predicted aroma fidelity rate, and to calculate the comprehensive priority score based on the aroma risk index and the pre-operation time to form a turnover priority queue. The job resource scheduling module is used to perform joint scheduling based on predictive maintenance work orders, turnover priority queues and warehouse location relocation lists, generate job scheduling tables, and execute the allocation of time slots, warehouse locations, equipment and personnel. The rebalancing audit module is used to perform closed-loop rebalancing and decision audits based on actual receipts and the latest monitoring data, updating microclimate zone environmental quotas, target storage locations, aroma risk indices, and turnover priority queues, including: Step 71: Calculate the environmental deviation, residence time deviation, aroma risk index, and batch cumulative odor exposure based on the execution receipt of the work schedule and the latest storage environment and residence monitoring data. Step 72: Based on the aroma risk index and cumulative odor exposure of each batch obtained in Step 71, recalculate the environmental matching function value of each batch and update the compatibility matrix under capacity and compatibility constraints. At the same time, calculate the aroma margin with the latest predicted aroma fidelity and recalculate the comprehensive priority score to generate an updated turnover priority queue. Specifically, the temperature, humidity, oxygen content and light intensity of each microclimate zone are combined into an environmental parameter vector. The target environmental parameter vector of each batch is obtained, the difference vector between the two is calculated, and a weighted sum is performed to obtain the environmental matching function value. Step 73: Generate a decision snapshot of the updated compatibility matrix, turnover priority queue and aroma risk index, calculate the hash fingerprint as the version number, perform consistency verification, confirm the compliance of the locking state and capacity constraints, and write the updated result that passes the verification into the audit database as the current effective version.

2. The intelligent warehousing and dynamic operation and maintenance management system for jasmine tea according to claim 1, characterized in that, The storage environment and residence monitoring data include: actual temperature, actual humidity, actual oxygen content, concentration of external odor volatiles, and actual residence time.

3. The intelligent warehousing and dynamic operation and maintenance management system for jasmine tea according to claim 1, characterized in that, Calculate the ratio of the actual concentration of each key volatile compound in the current batch of jasmine tea to its initial concentration. Compare this ratio with 1 and take the smaller value as the aroma retention coefficient of the key volatile compound. Weighted summation of the aroma retention coefficients of all key volatile compounds yields the aroma fidelity of this batch of jasmine tea at the current moment.

4. The intelligent warehousing and dynamic operation and maintenance management system for jasmine tea according to claim 1, characterized in that, Determine the environmental quota for each microclimate zone, and allocate batches to target storage locations based on storage capacity. Implement adjacent placement restrictions according to the compatibility matrix, including: Step 11: Combine the temperature, humidity, oxygen content and light intensity of each microclimate zone into an environmental parameter vector, obtain the target environmental parameter vector for each batch, calculate the difference vector between the two, and perform a weighted summation to obtain the environmental matching function value. Step 12: Using the environmental matching function value as the optimization objective, select the microclimate zone with the smallest environmental matching function value as the target quota zone, and generate an environmental quota matching table between the batch and the microclimate zone. Step 13: Based on the environmental quota matching table and the storage capacity of each microclimate zone, establish a batch and storage location allocation constraint model, and implement adjacent placement restrictions on the allocation of adjacent storage locations for incompatible batches based on the compatibility matrix. By solving the allocation constraint model, obtain the correspondence table between batches and storage locations.

5. The intelligent warehousing and dynamic operation and maintenance management system for jasmine tea according to claim 1, characterized in that, The aroma risk index is calculated by summing the concentrations of external odor volatiles over time to obtain the cumulative odor exposure for each batch, and warning markers are generated, including: Step 21: Obtain the target environmental parameter vector and allowable residence time corresponding to the batch. Based on the actual temperature, actual humidity and oxygen content recorded in the storage environment and residence monitoring data, calculate the difference between each actual parameter and each target parameter and divide by each target parameter. After weighted summation, obtain the environmental deviation degree. Obtain the residence time deviation by the absolute difference between the allowable residence time and the actual residence time. Obtain the aroma risk index by weighted summation of the environmental deviation degree and the residence time deviation. Step 22: Extract the concentration of external odor volatiles from the storage environment and residence monitoring data. When the change rate of external odor volatiles concentration is lower than the set change rate threshold, it is accumulated at a fixed sampling interval. When the change rate exceeds the set change rate threshold, it is accumulated at a dynamically shortened sampling interval. The product of the external odor volatiles concentration at each sampling time and the sampling interval is accumulated successively to obtain the batch cumulative odor exposure. Step 23: Compare the aroma risk index with the preset risk threshold, and compare the cumulative odor exposure of the batch with the odor exposure threshold. When any comparison result reaches the corresponding threshold, generate a warning mark for the batch.

6. The intelligent warehousing and dynamic operation and maintenance management system for jasmine tea according to claim 1, characterized in that, Perform trend assessments on critical equipment, generate predictive maintenance work orders, and implement mutually exclusive lockouts for affected storage areas, including: Step 31: Determine the set of reservoir area numbers based on the early warning markers, extract the operating parameter sequences of key equipment in each reservoir area, calculate the difference between adjacent sampling times for each operating parameter sequence and perform a weighted average to obtain the equipment parameter change rate; Step 32: When the absolute value of the rate of change of any equipment parameter exceeds the equipment trend threshold, mark the corresponding equipment as equipment requiring predictive maintenance, and write the equipment number, the warehouse area number, and the corresponding parameter rate of change into the trend assessment result table; generate a predictive maintenance work order table based on the trend assessment result table, and record the equipment number, warehouse area number, and work order generation time. Step 33: Perform mutual exclusion locking based on the set of warehouse area numbers in the predictive maintenance work order table. When there is an active work order in any warehouse area, set the locking status of the warehouse area to 1, and prohibit loading, unloading, moving and redistribution operations in the warehouse area before the lock is released.

7. The intelligent warehousing and dynamic operation and maintenance management system for jasmine tea according to claim 1, characterized in that, Determine the remaining aroma margin, calculate the overall priority score, and form a turnover priority queue, including: Step 41: Based on the trend of aroma fidelity change in the historical monitoring data of the batch and the current environmental deviation, calculate the predicted aroma fidelity by time series extrapolation. Calculate the aroma margin based on the difference between the predicted aroma fidelity and the set minimum aroma fidelity threshold. When the difference is less than zero, take zero as the aroma margin. Step 42: Multiply the reciprocals of the aroma risk index, aroma margin, and pre-operation time by their respective weighting coefficients and sum them to obtain the comprehensive priority score; Step 43: Sort all batches by their overall priority scores in descending order, generate a turnover priority queue, and record the queue generation time.

8. The intelligent warehousing and dynamic operation and maintenance management system for jasmine tea according to claim 7, characterized in that, The scheduling decision module also includes a priority reconfiguration mechanism, including: Step 51: Continuously monitor the rate of change of temperature, humidity, oxygen content and light intensity in each microclimate zone. When the rate of change of any parameter exceeds the corresponding set rate of change threshold in three consecutive sampling periods, it is determined that the microclimate zone has an abnormal fluctuation and all batches in the microclimate zone are marked as affected batches. Step 52: Calculate the risk increment based on the difference between the aroma risk index of the affected batch in the current scheduling cycle and the previous scheduling cycle, and calculate the aroma margin decay rate based on the difference between the predicted aroma fidelity rate at the current moment and the predicted aroma fidelity rate at the previous moment and the ratio of the predicted aroma fidelity rate at the previous moment. Step 53: The risk increment and the aroma margin decay rate are weighted and summed to obtain the abnormal priority. The abnormal priority is inserted into the turnover priority queue. The comprehensive priority scores of all batches are reordered in descending order of abnormal priority to generate a temporary emergency queue. Priority operation instructions are triggered for abnormal batches.

9. The intelligent warehousing and dynamic operation and maintenance management system for jasmine tea according to claim 1, characterized in that, Generate a job scheduling table and execute the allocation of time slots, storage locations, equipment, and personnel, including: Step 61: Construct a joint task set based on the predictive maintenance work order table, turnover priority queue and warehouse relocation list. Establish the work location, duration, required equipment and personnel, task priority weight and task predecessor relationship for each task. Use the sum of task priority weight weighted delay amount, resource conflict amount and location change cost as the objective function to form a joint scheduling model. Step 62: Establish mixed integer linear constraints for time indexing in the joint scheduling model. The constraints include: task start uniqueness, task predecessor dependency, resource non-overlap, reservoir locking, capacity and adjacency compatibility constraints. Solve the constraints using the branch and bound algorithm to obtain the final task start time slot and resource assignment results. Step 63: Generate a job scheduling table based on the final task start time slot and resource assignment results, specifying the execution time slot, storage location, equipment number and personnel number of each task, and sorting parallel tasks with the same score in order of work order level, duration and conversion cost.

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