Method and system for collaborative management and control of multiple devices in an agricultural microbial formulation production line

Abstract

The present application relates to a method and system for collaborative management of multiple devices in an agricultural microbial preparation production line, which method comprises calculating a device health score based on operating state information, combining the device health score with a biological risk index to generate a task release score, generating a production task instruction when the task release score meets a preset release threshold, constructing a collaborative interlocking matrix between multiple types of process equipment and material handling equipment based on the production task instruction, distributing control execution instructions to the multiple types of process equipment and material handling equipment, obtaining device feedback state information and material flow information respectively, performing consistency verification on the device feedback state information and the material flow information to obtain a consistency verification result, generating a revision audit chain corresponding to the control event information when manually modified, and generating a disposal control instruction when the operating state information or the control event information triggers a preset abnormal threshold. The present application has the effect of improving production safety.

Description

Technical Field

[0001] This invention relates to the technical field of monitoring and control in biological agent production, and in particular to a method and system for collaborative management and control of multiple devices in agricultural microbial agent production lines. Background Technology

[0002] In the past, the monitoring and control of equipment in agricultural microbial preparation production lines were typically conducted in isolation. Assessing equipment operating status often involved simply checking whether the equipment was normally on or off, lacking a comprehensive quantitative analysis of equipment health. Task release was mostly based on experience or basic production plans, without adequately considering biological risk factors. For the coordinated control of multiple devices, execution generally followed a fixed process sequence, lacking real-time interlocking mechanisms to ensure the accuracy of the execution timing. Information feedback and verification were also limited to simply receiving equipment operating results without verifying the consistency between equipment status information and material flow information.

[0003] However, these existing conventional methods have significant shortcomings. Individual monitoring devices cannot accurately assess equipment health, easily leading to continued operation even with potential malfunctions, impacting production quality and efficiency. The lack of comprehensive consideration of biological risks creates biosafety hazards in the production process. Fixed-process multi-device collaborative control cannot adapt to dynamic changes in the production process, easily resulting in chaotic equipment execution sequences. Furthermore, without information consistency verification, anomalies in the production process cannot be detected in a timely manner, leading to the accumulation and amplification of errors, ultimately affecting the production quality and yield of agricultural microbial agents. Summary of the Invention

[0004] To improve production safety, this invention provides a method and system for collaborative management of multiple devices in agricultural microbial preparation production lines.

[0005] The above-mentioned objective of this invention is achieved through the following technical solution:

[0006] A method for collaborative management and control of multiple devices in an agricultural microbial preparation production line, comprising:

[0007] Obtain the operating status information of the target equipment in the production line, and calculate the equipment health score based on the operating status information;

[0008] Obtain the biological risk index of the target batch, combine the equipment health score with the biological risk index to generate a task release score, and generate a production task instruction when the task release score meets the preset release threshold.

[0009] Based on production task instructions, a collaborative interlock matrix is ​​constructed between various types of process equipment and material handling equipment. The collaborative interlock matrix is ​​used to constrain the execution sequence between multiple devices. When multiple types of process equipment are in an executable state and material handling equipment has reached the designated position and is in a safe state, a control execution instruction is generated.

[0010] The control execution instructions are distributed to various types of process equipment and material handling equipment to drive the execution of process operations and material handling operations, and the equipment feedback status information and material flow information are obtained during the execution of process operations and material handling operations, respectively.

[0011] Perform consistency checks on equipment feedback status information and material flow information to obtain consistency check results. When the consistency check results indicate that there is a deviation between the equipment feedback status information and the material flow information, generate control event information. When manual modification is detected, generate a revision audit chain corresponding to the control event information.

[0012] When the running status information or control event information triggers the preset abnormal threshold, a handling control instruction is generated, and the corresponding abnormal handling operation is executed according to the handling control instruction under different abnormal levels.

[0013] By adopting the above technical solutions, equipment health scores can be calculated based on operational status information, enabling a quantitative assessment of equipment operational stability and coordination, avoiding the potential for continued operation due to rough judgments based solely on switch status. In the task release phase, equipment health scores can be combined with the biological risk index of the target batch to generate a task release score, ensuring that the task release process considers both equipment status and biological risk, reducing biosafety hazards. A collaborative interlocking matrix can be constructed based on production task instructions to ensure the accuracy of the execution sequence of process equipment and material handling equipment, avoiding sequence chaos under fixed processes. Consistency checks can be performed to compare equipment feedback status information with material flow information, promptly identifying operational deviations and generating a revision audit chain when manual modifications are detected. Traceability can be achieved by combining the target batch's genotype, ensuring the integrity of records. Disposal control instructions can be generated when an anomaly triggers a threshold, and graded or global disposal can be dynamically implemented based on the anomaly level, improving response efficiency. Overall, this solution solves the problems of single equipment monitoring, lack of biological risk consideration in task release, lack of interlocking mechanisms for multi-equipment collaboration, and insufficient information feedback verification in existing technologies, thereby significantly improving the operational reliability, biosafety, and production efficiency of agricultural microbial preparation production lines.

[0014] Preferred method: Obtain the operating status information of the target equipment in the production line, and calculate the equipment health score based on the operating status information, including:

[0015] Obtain the corresponding time series of operating parameters from the operating status information, and calculate the fluctuation trend index that characterizes the stability of equipment operation based on the time series of operating parameters;

[0016] Based on the operating parameters in the operating status information that characterize the collaborative relationship between the target equipment and other process equipment, calculate the collaborative coupling deviation value of the target equipment;

[0017] The fluctuation trend index is combined with the collaborative coupling deviation value, and a weighted calculation is performed based on the risk sensitivity factors corresponding to the target process link to obtain the equipment health score.

[0018] By adopting the above technical solution, it is possible to extract the time series of operating parameters based on the operating status information and calculate the fluctuation trend index, thereby quantifying the stability of equipment operation. It can also calculate the collaborative coupling deviation value by combining operating parameters reflecting the collaborative relationship between equipment in the operating status information, thus revealing the degree of deviation of the target equipment in multi-equipment collaboration. Furthermore, it can combine the fluctuation trend index and the collaborative coupling deviation value, and weight them according to the risk sensitivity factors corresponding to different process links, to obtain a comprehensive equipment health score. This ensures that the equipment status not only reflects the operation of a single machine but also reflects collaborative adaptability and process risk weights. This solution overcomes the limitations of traditional methods that rely solely on whether equipment is switched on or off or on a single parameter for judging the status, preventing potentially faulty equipment from continuing to operate and affecting production quality. Simultaneously, it improves the adaptability of health assessment to process differences and risk sensitivity, providing a more reliable quantitative basis for subsequent task release and anomaly control.

[0019] Preferred method: Obtain the biological risk index of the target batch, and combine the equipment health score with the biological risk index to generate a task release score, including:

[0020] The risk parameters of the target batch are standardized to obtain a risk feature vector;

[0021] The risk feature vector of the target batch is weighted and calculated based on the risk sensitivity factors corresponding to the target process steps to obtain the biological risk index.

[0022] When the biological risk index is higher than the preset risk threshold, the equipment health score is corrected according to the preset inhibition rules to obtain the corrected equipment health score.

[0023] The revised equipment health score and the biological risk index of the target batch are weighted and combined according to the calculation rules of the mission release score to obtain the mission release score.

[0024] By adopting the above technical solution, the risk parameters of the target batch can be standardized, avoiding calculation deviations caused by inconsistent dimensions or differences in value ranges. This results in a comparable and consistent risk feature vector. The risk feature vector can be weighted and calculated by combining the risk sensitivity factors of the target process steps to obtain a biological risk index. This allows the risk assessment results to reflect the degree of impact of different process steps on biosafety. When the biological risk index exceeds a preset risk threshold, the equipment health score is corrected according to preset suppression rules. This ensures that the weight of the equipment health score is reduced under high-risk conditions, resulting in a corrected score that better reflects the actual operational risk level. Finally, the corrected equipment health score and the biological risk index are weighted and synthesized to generate a task release score. This ensures that task release decisions consider both equipment status and biological risk. This solution avoids the one-sidedness of releasing based on a single indicator and improves the scientific nature and biosafety of release decisions.

[0025] Preferred: Constructing a collaborative interlocking matrix between various types of process equipment and material handling equipment based on production task instructions, including:

[0026] Analyze production task instructions and extract the corresponding process execution requirements and material handling requirements;

[0027] Determine the execution sequence of various process equipment according to process execution requirements, and determine the operating location and timing of material handling equipment according to material handling requirements.

[0028] Based on the execution sequence of various types of process equipment and the operating position and timing of material handling equipment, establish the dependencies and safety constraints between equipment actions;

[0029] Dependencies and security constraints are transformed into interlocking rules, generating a collaborative interlocking matrix.

[0030] By adopting the above technical solution, the process execution requirements and material handling requirements can be accurately extracted after parsing production task instructions, ensuring consistency between control logic and batch tasks. It can determine the execution sequence of various types of process equipment based on process execution requirements and, combined with material handling requirements, determine the operating position and timing of material handling equipment, thereby ensuring the matching of material flow and process operation. It can establish dependencies and safety constraints between equipment actions based on the execution sequence of process equipment and the spatiotemporal conditions of material handling equipment, avoiding operational risks caused by action conflicts or missing safety conditions. Furthermore, these dependencies and safety constraints are transformed into interlocking rules, generating a collaborative interlocking matrix, enabling dynamic adjustment of equipment operation under matrix constraints. This ensures that process equipment and material handling equipment have correct execution sequences and safety boundaries during collaborative processes. This solution overcomes the limitations of traditional fixed-process control methods and achieves flexible adaptation to dynamic production line conditions.

[0031] Preferably, the process of distributing control execution instructions to various types of process equipment and material handling equipment also includes:

[0032] By appending an idempotent flag to the control execution instructions, control execution instructions with an idempotent flag are obtained.

[0033] Control execution instructions with idempotent flags are queued in the order determined by the collaborative interlock matrix, and a pre-state verification is performed based on the equipment feedback status information and material flow information before issuance to obtain control execution instructions that pass the verification.

[0034] The control execution command that has passed the verification is sent to the corresponding target device, and the execution confirmation flag is identified based on the device feedback status information within the preset confirmation time limit to obtain the confirmation result;

[0035] When the confirmation result indicates that the execution was unsuccessful, the control execution command is resent according to the preset number of retries and retry interval. If the retry still fails, a rollback command is generated according to the preset rollback table and sent to the corresponding device to restore the state to the safe baseline.

[0036] By adopting the above technical solution, idempotency flags can be added to control execution commands to ensure that the same equipment action will not be executed twice when repeatedly triggered, thus avoiding process disturbances caused by repeated operations. Control execution commands with idempotency flags can be queued according to the order determined by the collaborative interlock matrix, and a pre-state verification based on equipment feedback status information and material flow information can be performed before issuance, ensuring that the command issuance process has precondition constraints and guarantees the synchronization and consistency of equipment action and material flow. After issuance, confirmation results can be obtained within a preset confirmation time limit by identifying the execution confirmation flag, making the execution process verifiable. When the confirmation result indicates unsuccessful execution, the system restores the equipment to a safe baseline state through a preset retry mechanism and rollback table, ensuring that the equipment can be safely rolled back in abnormal situations. This solution effectively improves the reliability of control commands and the controllability of the execution process.

[0037] Preferred method: Perform consistency verification on equipment feedback status information and material flow information to obtain consistency verification results, including:

[0038] Based on equipment feedback status information and material flow information, batch identifier, pallet identifier, container identifier, workstation identifier and time slice identifier are extracted to obtain five related elements;

[0039] Combine the five elements of association to establish a five-element association key;

[0040] Under the constraint of the five-element correlation key, the corresponding records of equipment feedback status information and material flow information are compared to generate a consistency verification result.

[0041] By adopting the above technical solution, batch identifiers, pallet identifiers, container identifiers, workstation identifiers, and time slice identifiers can be extracted based on equipment feedback status information and material flow information to form five-element correlation elements. This allows for a unified description of key objects and timing in the production process at the data level. Furthermore, these five-element correlation elements can be combined to establish five-element correlation keys, enabling binding relationships between equipment operation and material flow in batch, spatial, and temporal dimensions. Under the constraints of these five-element correlation keys, corresponding records of equipment feedback status information and material flow information can be compared to generate consistency verification results, ensuring that actual equipment actions match the material flow path. This solution overcomes the shortcomings of traditional methods that rely solely on single equipment feedback or material records and lack cross-validation. It can promptly detect deviations and anomalies, preventing the accumulation and expansion of errors in the production process, thereby improving the consistency and reliability of process data in agricultural microbial preparation production lines.

[0042] Preferred: When manual modification is detected, a revision audit chain corresponding to the control event information is generated, including:

[0043] When a manual modification is detected, the associated information of the manual modification is obtained to get the manual modification record.

[0044] Based on the production task instructions corresponding to the target batch, the correspondence between the target batch and process equipment, sterilization cycle and inoculation room ledger is extracted, and the gene lineage of the target batch is constructed.

[0045] By linking manually modified records with the gene lineage of the target batch, the modification traceability information after binding is obtained;

[0046] The bound modification traceability information is sequentially written into the revision audit chain to generate a revision audit chain corresponding to the control event information.

[0047] By adopting the above technical solution, it is possible to obtain information related to manual modifications when they are detected, forming a manual modification record, thereby completely preserving key information such as data before and after modification, operator identity, and operation time. It is also possible to extract the correspondence between the target batch and process equipment, sterilization cycle, and inoculation room ledger based on the production task instructions corresponding to the target batch, constructing the target batch's genetic lineage, making the batch execution trajectory traceable across multiple stages. Furthermore, it is possible to bind the manual modification record with the target batch's genetic lineage, generating bound modification traceability information, ensuring that manual intervention can be mapped to specific process stages. Finally, it is possible to sequentially write the modification traceability information into the revision audit chain, generating a revision audit chain corresponding to the control event information, thereby achieving complete traceability across stages and across time. This solution overcomes the shortcomings of traditional production line manual interventions that cannot be systematically recorded and traced across stages, ensuring the integrity and transparency of data management, and improving the controllability of abnormal event handling and accountability.

[0048] Preferably: When the operating status information or control event information triggers a preset abnormal threshold, a handling control instruction is generated, including:

[0049] Based on the running status information, deviation information within a continuous time slice is extracted to obtain a deviation information sequence;

[0050] Obtain historical control event information;

[0051] Based on the anomaly occurrence information of the gene lineage and historical control event information corresponding to the target batch, the lineage anomaly statistics are obtained.

[0052] The task release score and the spectrum anomaly statistics are weighted and synthesized to obtain threshold correction factor information, and the preset anomaly threshold is corrected accordingly to obtain dynamic anomaly threshold information.

[0053] The deviation information sequence is accumulated within a sliding time slice to obtain the abnormal accumulation degree information. The abnormal accumulation degree information is compared with the dynamic abnormal threshold information to obtain the abnormal judgment result. Based on the abnormal judgment result, a handling control instruction is generated.

[0054] By adopting the above technical solution, deviation information within continuous time slices can be extracted based on operational status information to form a deviation information sequence, enabling the quantitative expression of equipment operational deviations in the time dimension. It can combine historical control event information with the anomaly occurrence of the target batch gene spectral system to form phylogenetic anomaly statistical information, allowing anomaly judgment to be linked to batch trajectory and historical experience. It can weightedly synthesize task release scores and phylogenetic anomaly statistical information to obtain threshold correction factor information, and correct preset anomaly thresholds, thereby forming dynamic anomaly threshold information, making the threshold adaptive. It can accumulate and calculate the deviation information sequence within a sliding time slice to obtain anomaly accumulation information, and compare it with dynamic anomaly threshold information to obtain anomaly judgment results. Furthermore, it generates handling control instructions based on the anomaly judgment results, ensuring timely and effective anomaly response. This solution solves the problems of fixed thresholds, delayed anomaly judgment, and inaccurate response in traditional methods, improving the reliability and intelligence level of production line monitoring and anomaly control.

[0055] Preferred: Execute corresponding exception handling operations according to the handling control instructions at different exception levels, including:

[0056] Based on the control commands and the collaborative interlock matrix, the device action dependencies related to the anomalies are identified to obtain the control coverage information.

[0057] Based on the coverage information, the disposal control instructions are processed by scope narrowing and timing rearrangement to obtain the narrowed disposal control instructions;

[0058] The compressed disposal control command is sent to the corresponding process equipment and material handling equipment to obtain disposal information;

[0059] When the abnormality level is not an emergency stop level, the graded handling actions are carried out in sequence according to the phased execution strategy, and the handling coverage information is updated based on the equipment feedback status information and material flow information after the completion of each phase.

[0060] When the anomaly level is an emergency stop level, global handling is executed based on the contracted handling control instructions, and the handling information is bound with the control event information to obtain the bound handling information. The bound handling information is written into the revision audit chain for the purpose of correcting the target batch gene lineage.

[0061] By adopting the above technical solution, the dependencies between abnormal equipment actions can be identified based on the handling control commands and the collaborative interlocking matrix, thus obtaining the handling coverage information. This ensures that the handling scope covers all process equipment and material handling equipment affected by the abnormality. The handling control commands can be narrowed and rearranged in sequence based on the handling coverage information, generating narrowed handling control commands to avoid irrelevant actions and ensure a reasonable action sequence. The narrowed handling control commands can be issued to the corresponding equipment and feedback can be collected to form handling information, making the abnormality handling process verifiable. When the abnormality level is not an emergency stop level, a phased execution strategy can be implemented for graded handling, and the handling coverage information can be dynamically updated based on feedback after each stage, achieving gradual convergence of handling. When the abnormality level is an emergency stop level, global handling can be executed directly, and the handling information and control event information can be bound and written into the revision audit chain, ensuring that the target batch genealogy can completely record the emergency stop event. This solution achieves unified management of graded and global handling, ensuring the safety, flexibility, and traceability of abnormal response.

[0062] The second objective of this invention is achieved through the following technical solution:

[0063] A multi-equipment collaborative management and control system for agricultural microbial preparation production lines, comprising:

[0064] The operation status assessment module is used to acquire the operation status information of target equipment in the production line and calculate the equipment health score based on the operation status information;

[0065] The task release judgment module is used to obtain the biological risk index of the target batch, combine the equipment health score with the biological risk index to generate a task release score, and generate a production task instruction when the task release score meets the preset release threshold.

[0066] The collaborative interlocking construction module is used to build a collaborative interlocking matrix between various types of process equipment and material handling equipment based on production task instructions. The collaborative interlocking matrix is ​​used to constrain the execution sequence between multiple devices. When multiple types of process equipment are in an execution-allowed state and material handling equipment has reached the designated position and is in a safe state, control execution instructions are generated.

[0067] The instruction distribution and execution module is used to distribute control execution instructions to various types of process equipment and material handling equipment, drive the execution of process operations and material handling operations, and obtain equipment feedback status information and material flow information during the execution of process operations and material handling operations, respectively.

[0068] The consistency verification and event management module is used to perform consistency verification on the equipment feedback status information and material flow information, and obtain the consistency verification result. When the consistency verification result indicates that there is a deviation between the equipment feedback status information and the material flow information, control event information is generated. When manual modification is detected, a revision audit chain corresponding to the control event information is generated.

[0069] The exception handling module is used to generate handling control instructions when the running status information or control event information triggers the preset exception threshold, and to perform corresponding exception handling operations according to the handling control instructions under different exception levels.

[0070] By adopting the above technical solutions, the operation status assessment module can calculate equipment health scores based on the time series of operating parameters and collaborative relationships, making equipment status assessment more comprehensive. The task release judgment module can combine equipment health scores with the biological risk index of the target batch to generate task release scores, ensuring that release decisions take into account both equipment stability and biological safety. The collaborative interlock construction module can establish an interlock matrix between process equipment and material handling equipment to ensure correct execution sequence and avoid process conflicts. The instruction distribution and execution module can collect feedback while issuing control execution instructions, forming corresponding records of equipment operation and material flow, ensuring that the operation process is traceable. The consistency verification and event management module can compare five-element correlation elements to detect inconsistencies between feedback and flow, and generate a revision audit chain to achieve cross-link traceability. The anomaly handling module can dynamically implement graded handling or global emergency stop according to the anomaly level, improving the safety and flexibility of anomaly response. Overall, the system improves the reliability, traceability, and intelligence level of production line operation.

[0071] In summary, the present invention has at least one of the following beneficial technical effects:

[0072] 1. This solution can calculate equipment health scores based on operational status information, enabling a quantitative assessment of equipment operational stability and coordination, avoiding the potential for continued operation due to rough judgments based solely on switch status. It can combine equipment health scores with the biological risk index of the target batch to generate a task release score during the task release process, considering both equipment status and biological risk to reduce biosafety hazards. It can construct a collaborative interlock matrix based on production task instructions, ensuring the accuracy of the execution sequence of process equipment and material handling equipment, avoiding sequence chaos under fixed processes. It can compare equipment feedback status information and material flow information through consistency verification, promptly identifying operational deviations and generating a revision audit chain when manual modifications are detected, achieving traceability by combining the target batch's genotype, ensuring record integrity. It can generate disposal control instructions when an anomaly triggers a threshold, and dynamically implement graded or global disposal based on the anomaly level, improving response efficiency. Overall, this solution solves the problems of single equipment monitoring, lack of biological risk consideration in task release, lack of interlocking mechanisms for multi-equipment collaboration, and insufficient information feedback verification in existing technologies, thereby significantly improving the operational reliability, biosafety, and production efficiency of agricultural microbial preparation production lines. Attached Figure Description

[0073] Figure 1 This is a flowchart of a multi-equipment collaborative management method for an agricultural microbial preparation production line according to an embodiment of the present invention.

[0074] Figure 2 This is a flowchart illustrating the implementation of step S10 in the multi-equipment collaborative management method for agricultural microbial preparation production lines according to an embodiment of the present invention.

[0075] Figure 3 This is a flowchart illustrating the implementation of step S20 in the multi-equipment collaborative management method for agricultural microbial preparation production lines according to an embodiment of the present invention.

[0076] Figure 4 This is a schematic diagram of a multi-equipment collaborative management and control system for an agricultural microbial preparation production line according to one embodiment of the present invention. Detailed Implementation

[0077] The present invention will be further described in detail below with reference to the accompanying drawings.

[0078] In one embodiment, such as Figure 1 As shown, this invention discloses a method for collaborative management of multiple devices in an agricultural microbial preparation production line, specifically including the following steps:

[0079] S10: Obtain the operating status information of the target equipment in the production line, and calculate the equipment health score based on the operating status information.

[0080] Specifically, operational status information refers to the operational parameter information generated and collected by the target equipment in the production line during process operations or material handling operations. This information includes both real-time operational parameters collected during the current batch production process and operational parameters stored in historical batches or multiple production cycles. The content of operational status information includes, but is not limited to, equipment start / stop status, access control switch status, sterilizer temperature and pressure parameters, roller shutter door lifting position, inoculation room operation status, and the operating position and task execution status of automated guided vehicles. By setting up data acquisition channels in the control interface of the target equipment, operational status information is acquired in real time and converted into a time series of operational parameters arranged chronologically. Equipment health score refers to a quantitative score obtained based on the operational stability of the target equipment and its collaborative relationship with other equipment as represented by the operational status information. Statistical analysis is performed on the time series of operating parameters to extract a volatility trend index. This index, calculated using the mean, variance, and rate of change of the time series, reflects the stability of equipment operation. Combined with operating parameters from the operational status information that characterize the interaction between the target equipment and other process equipment, a cooperative coupling deviation value is calculated. This deviation value represents the degree of difference between the operating parameters of the target equipment and those of dependent or cooperating equipment. Finally, the volatility trend index and the cooperative coupling deviation value are weighted and calculated. The weighting factors are determined by the risk sensitivity factors corresponding to the target process stage to obtain an equipment health score. Based on the equipment health score sequences of different batches or periods, the production priority of the target equipment is further determined.

[0081] S20: Obtain the biological risk index of the target batch, combine the equipment health score with the biological risk index to generate a task release score, and generate a production task instruction when the task release score meets the preset release threshold.

[0082] Specifically, a target batch refers to a specific production batch executed according to the production plan on the agricultural microbial preparation production line. Each target batch has a unique batch number and corresponds to a specific process step, material flow record, and operation log. The biosafety risk index is a quantitative indicator representing the degree of biosafety risk, calculated based on risk parameters associated with the target batch. These risk parameters include the sterilization temperature and time of the sterilizer, the cleanliness level and operation time of the inoculation room, the temperature and humidity stability during the cultivation process, and the material exposure time during automated guided vehicle (AGV) transportation. The risk parameters of the target batch are standardized to form a risk feature vector. This risk feature vector is then weighted according to the risk sensitivity factors corresponding to the target process step to obtain the biosafety risk index. The task release score is a quantitative result obtained by weighting the equipment health score and the biosafety risk index according to preset calculation rules. It is used to characterize whether the target batch meets the release conditions under the current production state. The task release score is compared with a release threshold. When the task release score is greater than or equal to the release threshold, a production task instruction is generated. The production task instruction is a control instruction used to drive various process equipment and material handling equipment on the production line to execute according to a collaborative interlock matrix.

[0083] S30: Construct a collaborative interlock matrix between multiple types of process equipment and material handling equipment based on production task instructions. The collaborative interlock matrix is ​​used to constrain the execution sequence between multiple devices. When multiple types of process equipment are in an executable state and material handling equipment has reached the designated position and is in a safe state, a control execution instruction is generated.

[0084] Specifically, production task instructions refer to control instructions generated based on task release scoring, used to drive various types of process equipment and material handling equipment in the production line to perform process operations and material flow operations. Process equipment refers to equipment in the production line that performs process handling functions, including sterilizers, inoculation rooms, incubation chambers, and their associated roller shutters and access control devices. Material handling equipment refers to automated guided vehicles (AGVs) used to transfer materials between different process stages. The collaborative interlock matrix refers to a set of rules that establishes the execution dependencies and safety constraints between process equipment and material handling equipment based on the process execution requirements and material handling requirements contained in the production task instructions. In practice, the production task instructions are parsed to obtain the process execution requirements and material handling requirements. The start-up and shutdown sequence and operating conditions of each process equipment are determined based on the process execution requirements, and the arrival position and operating time of the AGV are determined based on the material handling requirements. The start-up and shutdown sequence of the process equipment and the arrival conditions of the material handling equipment are combined to form the dependencies between equipment actions, and a collaborative interlock matrix is ​​constructed by combining these with safety constraints. When the process equipment is in an executable state and the material handling equipment has reached the designated position and is in a safe state, control execution instructions are generated based on the collaborative interlock matrix. Control execution instructions are specific control instructions used to drive the target process equipment and the target material handling equipment to perform corresponding operations.

[0085] S40: Distribute control execution instructions to various types of process equipment and material handling equipment, drive the execution of process operations and material handling operations, and obtain equipment feedback status information and material flow information during the execution of process operations and material handling operations.

[0086] Specifically, when control execution commands are issued to various types of process equipment and material handling equipment, the process equipment includes sterilizers, inoculation rooms, culture chambers, and roller shutters and access control devices, while the material handling equipment is an automated guided vehicle (AGV). The control execution commands establish communication connections with the control ports of the process equipment and the scheduling interface of the AGV via Ethernet or an industrial communication bus. After the commands are issued, they drive the target process equipment to complete the process operation and simultaneously drive the AGV to complete the material handling operation. Equipment feedback status information refers to the operational feedback information generated by the process equipment during the execution of control execution commands, including temperature and pressure feedback values ​​for the sterilizer, air cleanliness parameters for the inoculation room, the opening and closing status of the roller shutters and access control devices, and temperature and humidity parameters for the culture chamber. Material flow information refers to the material location and handling progress information generated by the AGV during material handling operations, including material batch identifiers, pallet identifiers, container identifiers, running path coordinates, and arrival time. By configuring status acquisition modules on the control nodes of the process equipment and the AGV, equipment feedback status information and material flow information are collected in real time.

[0087] S50: Perform consistency verification on the equipment feedback status information and material flow information, and obtain the consistency verification result. When the consistency verification result indicates that there is a deviation between the equipment feedback status information and the material flow information, generate control event information. When manual modification is detected, generate a revision audit chain corresponding to the control event information.

[0088] Specifically, when performing consistency verification on equipment feedback status information and material flow information, the process first extracts batch identifiers, pallet identifiers, container identifiers, workstation identifiers, and time slice identifiers based on the equipment feedback status information and material flow information, forming five-element association elements. These five-element association elements are then combined to establish a five-element association key. Using this five-element association key as the comparison basis, the corresponding records of equipment feedback status information and material flow information are matched one by one to obtain the consistency verification result. The consistency verification result refers to the comparison result reflecting whether the equipment feedback status information and material flow information maintain a corresponding relationship. If the comparison result indicates a deviation, control event information is generated. Control event information refers to event records used to mark anomalies when equipment feedback status information and material flow information are inconsistent. Control event information includes the deviation location, deviation time, and corresponding batch identifier. When manual modification is detected during the consistency verification process, a revision audit chain corresponding to the control event information is generated. The revision audit chain refers to a sequential traceability record built based on the values ​​before and after the modification, the operator's identity information, and the modification time information. The revision audit chain is linked to the gene lineage of the target batch to ensure that human intervention during the production line operation can be traced and verified.

[0089] S60: When the running status information or control event information triggers the preset abnormal threshold, a handling control instruction is generated, and the corresponding abnormal handling operation is executed according to the handling control instruction under different abnormal levels.

[0090] Specifically, anomaly thresholds refer to numerical limits set for operational status information and control event information, used to determine whether the production line is in an abnormal state. Anomaly thresholds include temperature thresholds, pressure thresholds, cleanliness thresholds, position deviation thresholds, and workstation time deviation thresholds. When operational status information or control event information exceeds anomaly thresholds, a handling control command is triggered. The handling control command is a control command generated based on the anomaly determination result, used to drive process equipment and material handling equipment to perform anomaly handling operations. Anomaly level refers to a classification based on the degree to which anomaly thresholds are exceeded and the scope of anomaly impact. Anomaly levels include warning level, speed limit level, phased shutdown level, and emergency stop level. Anomaly handling operations refer to the countermeasures performed by process equipment and material handling equipment under the action of handling control commands. Different anomaly levels correspond to different anomaly handling operations. The warning level executes audible and visual warnings; the speed limit level executes reducing the speed of the automated guided vehicle and delaying the opening and closing of the roller shutter door; the phased shutdown level executes the operation procedure of closing the sterilization cabinet of the target process step or suspending the inoculation room; and the emergency stop level executes a complete line shutdown and locking of access control and roller shutter doors. After generating the disposal control instruction, the disposal control instruction is issued to the target process equipment and material handling equipment according to the corresponding anomaly level. After execution, the disposal information is associated with the control event information to support complete traceability in the subsequent revision audit chain.

[0091] In one embodiment, such as Figure 2 As shown, in step S10, the operating status information of the target equipment in the production line is obtained, and the equipment health score is calculated based on the operating status information, including:

[0092] S101: Obtain the corresponding time series of operating parameters from the operating status information, and calculate the fluctuation trend index that characterizes the stability of equipment operation based on the time series of operating parameters.

[0093] Specifically, the operating parameter time series refers to a numerical sequence formed by arranging similar operating parameters in the operating status information in chronological order, such as the sterilizer temperature parameter sequence, sterilizer pressure parameter sequence, inoculation room cleanliness parameter sequence, roller shutter door position parameter sequence, and automated guided vehicle position coordinate sequence. The fluctuation trend index is a numerical indicator characterizing the operational stability of the target equipment, calculated after normalizing the operating parameter time series. In practice, the operating parameter time series x(t) is first normalized to obtain the dimensionless sequence x. ’ (t), the normalization method is:

[0094] ,in, The mean of the time series of running parameters. x is the standard deviation of the time series of running parameters, after normalization. ’(t) is a dimensionless value to ensure dimensional consistency in subsequent calculations. Then, the volatility trend index W is calculated based on the normalized sequence, using the following formula:

[0095] Where N is the number of sampling points, This represents the normalized variation range between adjacent time slices. The larger the fluctuation trend index W value, the more obvious the fluctuation of the target equipment's operating parameters and the lower the operating stability; the smaller the value, the more stable the target equipment's operating parameters and the higher the operating stability.

[0096] S102: Calculate the cooperative coupling deviation value of the target equipment based on the operating parameters in the operating status information that characterize the cooperative relationship between the target equipment and other process equipment.

[0097] Specifically, the collaborative coupling deviation value refers to a dimensionless index calculated based on operating parameters characterizing the collaborative relationship between the target equipment and other process equipment in the operating status information, used to quantify the degree of consistency in collaboration between the target equipment and related equipment. To calculate the collaborative coupling deviation value, a set of paired operating parameters is first constructed according to process interlocking and collaborative logic. A pairing set of operating parameters refers to a set formed by pairing the time series of key operating parameters of the target equipment with the time series of key operating parameters of related process equipment or material handling equipment. Examples include: pairing the sterilizer chamber pressure parameter series with the sterilizer door opening / closing status series; pairing the roller shutter door opening / closing position parameter series with the automated guided vehicle (AGV) arrival status series; and pairing the inoculation room door control status series with the AGV's operating position series. To ensure dimensional consistency, normalization and time alignment are performed on each pair of operating parameter time series. Normalization involves converting the time series of different physical quantities into dimensionless series with zero mean and unit standard deviation. Time alignment involves setting a desired time lag for each pair of paired parameters based on the sequence of the collaborative process and the process cycle time. (For example, the automated guided vehicle arrives before the roller shutter door opens by a fixed time interval). For the i-th pair of operating parameters, perform the following processing:

[0098] ,in, , and These represent the mean and standard deviation of the corresponding time series, respectively. After normalization and time alignment, the correlation consistency of the i-th pair of parameters is calculated. The interlocking violation rate pi. Correlation consistency refers to the Pearson correlation coefficient, which reflects the degree of synchronous change between two aligned dimensionless sequences. The interlocking violation rate is the dimensionless ratio of the proportion of samples exhibiting violations under a given process interlocking rule. The calculation formula is: ,in, This is the set of overlapping time slices after aligning the two sequences. For the sample size, For the interlock rule determination corresponding to the i-th pair of parameters (e.g., "Door cannot be opened if cabin pressure is not zero" or "Roller door cannot be opened if automated guided vehicle is not in position"), The indicator function is used. The correlation deviation term di is constructed from the correlation consistency: When the two sequences are highly consistent and When the two sequences are inversely related or uncorrelated, di increases. Taking into account the differences in process risk under different synergistic relationships, a risk sensitivity factor is introduced to determine the weight wi (satisfying...). The collaborative coupling deviation value C is obtained through weighted fusion. ,in, This represents a trade-off coefficient between the relevant deviation and the interlock violation rate. A larger cooperative coupling deviation value C indicates poorer coordination consistency between the target device and related devices; a smaller value C indicates better coordination consistency. This calculation process is uniformly applicable to various process scenarios, including access control-cabin pressure, roller shutter door-automated guided vehicle positioning, and vaccination room door control-automated guided vehicle position.

[0099] S103: Combine the fluctuation trend index with the collaborative coupling deviation value, and perform weighted calculation based on the risk sensitivity factors corresponding to the target process link to obtain the equipment health score.

[0100] Specifically, the risk sensitivity factor refers to a weighted parameter set for a target process step, used to adjust the influence ratio of the fluctuation trend index and the cooperative coupling deviation value in the equipment health score calculation. The risk sensitivity factor consists of fluctuation weights. With collaborative weights Composition, forming a risk sensitivity factor vector ,in, In the calculation process, the fluctuation trend index W obtained from the time series of operating parameters is first benchmarked. Benchmarking refers to using the baseline value obtained from the statistics of historical stable batches under the same process step. For reference, W is mapped to a dimensionless ratio: ,in, This characterizes the fluctuation level of the target equipment under the current process stage. The cooperative coupling deviation value C is calculated from the operating parameters characterizing the cooperative relationship between the target equipment and other process equipment in the operating status information, and its value ranges from [0,1]. After obtaining the dimensionless... After combining with C, a weighted combination is performed based on risk sensitivity factors to calculate the health risk quantity R: The equipment health score H is defined as: A health score (H) closer to 1 indicates stable operation and good consistency of the target equipment, while a score closer to 0 indicates significant fluctuations or poor consistency. In practice, risk sensitivity factors can be configured based on the characteristics of each process step; for example, a higher value can be set when consistency is emphasized in the sterilization process. When emphasizing operational stability during the cultivation process, a larger setting is required. .

[0101] In one embodiment, such as Figure 3 As shown, in step S20, the biological risk index of the target batch is obtained, and the equipment health score is combined with the biological risk index to generate a task release score, including:

[0102] S201: Perform standardization processing on the risk parameters of the target batch to obtain the risk feature vector.

[0103] Specifically, a target batch refers to a specific production batch executed according to the production plan on the agricultural microbial preparation production line. This target batch is associated with several risk parameters. Risk parameters are quantitative data characterizing the biosafety risk level of the target batch during the production process, including sterilization temperature and time in the sterilizer, air cleanliness level and operation time in the inoculation room, temperature and humidity stability parameters during the cultivation phase, and material exposure time during automated guided vehicle (AGV) transportation. To ensure comparability of different physical quantities, the risk parameters are standardized to obtain a risk feature vector. Standardization involves averaging each risk parameter xj according to its historical statistical mean. and standard deviation The dimensionless conversion is performed using the following formula: Where m is the dimension of the risk parameter, and xj' is the j-th risk parameter after standardization. By performing standardization on all risk parameters, the risk feature vector is obtained: .

[0104] S202: The risk feature vector of the target batch is weighted and calculated based on the risk sensitivity factors corresponding to the target process steps to obtain the biological risk index.

[0105] Specifically, risk sensitivity factors refer to weighted parameters set for the target process stage, used to adjust the influence ratio of different risk parameters in the calculation of the biological risk index. The risk feature vector of the target batch is represented as... The risk sensitivity factor corresponding to the target process step is expressed as: ,in, Here is the weight corresponding to the j-th risk parameter. The biological risk index is the result calculated by weighting the risk feature vector and risk sensitivity factors, and its calculation formula is: Rb is the biological risk index. This weighting method reflects the risk differences of the target batch at different process stages. For example, temperature and pressure parameters are given greater weight in the sterilization stage, while air cleanliness and operation time parameters are given greater weight in the inoculation stage.

[0106] S203: When the biological risk index is higher than the preset risk threshold, the equipment health score is corrected according to the preset inhibition rule to obtain the corrected equipment health score.

[0107] Specifically, the risk threshold is a reference value used to determine whether the biological risk index is within an acceptable range, and it is set based on historical batch statistics and process safety specifications. When the biological risk index R_b is higher than the risk threshold T_b, a correction to the equipment health score is triggered. The suppression rule is an adjustment rule used to reduce the equipment health score, typically employing a decreasing function based on the extent of the exceedance. Let the original equipment health score be H, and the suppression factor function be:

[0108] ,in, The adjustment coefficient is used. The original equipment health score is multiplied by the inhibition factor function to obtain the corrected equipment health score H': .when When Rb > Tb, the inhibition factor function is set to 1, and the equipment health score remains unchanged. When Rb > Tb, as the over-limit magnitude increases, the inhibition factor function decreases, and the equipment health score is correspondingly suppressed.

[0109] S204: The revised equipment health score and the biological risk index of the target batch are weighted and synthesized according to the calculation rules of the mission release score to obtain the mission release score.

[0110] Specifically, the task release score refers to the evaluation result used to determine whether a production task should be released, obtained by weighting and synthesizing the corrected equipment health score and the biological risk index of the target batch. Let the corrected equipment health score be H', the biological risk index of the target batch be Rb, and the task release score be Sf. According to the calculation rules of the task release score, a weight vector is defined. ,in, The formula for calculating the task release score is: Among them, (1-Rb) is used to characterize that the higher the biological risk index, the more significant the reduction in the release score. The corrected equipment health score H' and the risk suppression effect jointly affect the final result. The higher the task release score, the more the target batch meets the process safety and operational stability requirements. When the task release score is greater than or equal to the release threshold, a production task instruction is generated.

[0111] In one embodiment, step S30, namely constructing a collaborative interlock matrix between various types of process equipment and material handling equipment based on production task instructions, includes:

[0112] S301: Parse production task instructions and extract the corresponding process execution requirements and material handling requirements.

[0113] Specifically, process execution requirements refer to the operational requirements generated for process equipment in the production task instruction. These requirements include the heating and pressurization process of the sterilizer, air purification and operation time allocation in the inoculation room, temperature and humidity control of the incubation chamber, and opening and closing control of the roller shutter door and access control system. Material handling requirements refer to the transfer requirements generated for material handling equipment in the production task instruction. These requirements include the running path of the automated guided vehicle (AGV), batch identification of the loading pallet, workstation information for container arrival, and time constraints during the handling process. When parsing the production task instruction, the parameter fields in the instruction are first subjected to syntactic analysis and structured decomposition. Fields related to process equipment are categorized as process execution requirements, and fields related to AGVs are categorized as material handling requirements. Then, the process execution requirements and material handling requirements are associated according to the task number and batch identification to ensure that process operations and material handling operations remain synchronized and traceable during execution.

[0114] S302: Determine the execution sequence of multiple types of process equipment according to process execution requirements, and determine the operating location and operating time of material handling equipment according to material handling requirements.

[0115] Specifically, the operating position refers to the spatial coordinates or designated workstations that material handling equipment needs to reach during the material handling task. The operating position is identified by workstation number, path node coordinates, or sensor positioning information. The operating time refers to the trigger moment when the material handling equipment performs a handling action in the production process. The operating time is set based on the execution status of the process equipment and the production cycle. When determining the execution sequence of process equipment according to process execution requirements, the heating and pressurization process of the sterilizer is placed before the material enters the incubation chamber, and the air purification and inoculation operation in the inoculation room are placed after the sterilization process. The opening and closing sequence of the roller shutter door and access control is matched with the operation status of the inoculation room and the incubation chamber, forming an orderly start-stop chain for the process equipment. When determining the operating position and operating time of the material handling equipment according to material handling requirements, the running path of the automated guided vehicle (AGV) is bound to the corresponding pallet identifier. After sterilization in the sterilizer is completed, the AGV is triggered to run to the inoculation room workstation; after the inoculation operation is completed, the AGV is triggered to run to the incubation chamber workstation. In this way, the execution sequence of process equipment is kept consistent with the operating position and operating time of the material handling equipment, achieving continuity and coordination of the process flow.

[0116] S303: Based on the execution sequence of multiple types of process equipment and the operating position and timing of material handling equipment, establish the dependencies and safety constraints between equipment actions.

[0117] Specifically, dependencies refer to the sequential connection between process equipment and material handling equipment in terms of execution order. Dependencies are defined by task triggering conditions and completion confirmation conditions. For example, the automated guided vehicle (AGV) can only be triggered to move to the inoculation room workstation after the heating and pressurization tasks of the sterilizer are completed; and the opening of the roller shutter door and the start-up of the incubation chamber can only be triggered after the inoculation room operation is completed. Safety constraints refer to operational restrictions set to avoid conflicts between equipment actions and process risks. Safety constraints include spatial isolation constraints, time interval constraints, and status confirmation constraints. Spatial isolation constraints prohibit unrelated material handling equipment from entering the same area when operations are performed in the inoculation room. Time interval constraints require that the cooling process of the sterilizer must reach a specified time before subsequent operations can proceed. Status confirmation constraints require that the AGV can only be triggered to enter the target workstation after the roller shutter door is fully open. By combining dependencies and safety constraints, an execution logic chain is formed between process equipment and material handling equipment to ensure the smooth progress of production tasks under conditions of process continuity and biosafety.

[0118] S304: Transform dependencies and security constraints into interlocking rules to generate a collaborative interlocking matrix.

[0119] Specifically, interlock rules refer to logical control rules transformed from dependencies and safety constraints. Interlock rules are used to limit the execution conditions and triggering sequence of process equipment and material handling equipment. When converting dependencies into interlock rules, the completion confirmation status of the preceding equipment serves as a prerequisite for the start-up of the subsequent equipment. For example, after the sterilizer completes sterilization and cooling confirmation, an interlock rule is generated, allowing the automated guided vehicle (AGV) to enter the inoculation room. After the inoculation room completes operation confirmation, an interlock rule is generated, allowing the roller shutter door to open and triggering the incubation chamber to run. When converting safety constraints into interlock rules, spatial isolation constraints, time interval constraints, and status confirmation constraints serve as barrier conditions for triggering equipment actions. For example, during inoculation room operation, an interlock rule is generated to prohibit non-associated AGVs from entering the inoculation area; during sterilizer cooling, an interlock rule is generated to prohibit subsequent material loading tasks; and before the roller shutter door is fully opened, an interlock rule is generated to prohibit the AGV from entering the target workstation. Finally, all interlocking rules are organized into a matrix structure to form a collaborative interlocking matrix. The collaborative interlocking matrix is ​​a two-dimensional logical table arranged according to task number, equipment type, and triggering conditions, used to uniformly constrain the execution sequence of multiple types of process equipment and material handling equipment.

[0120] In one embodiment, step S40, which involves distributing control execution instructions to various types of process equipment and material handling equipment, further includes:

[0121] S401: Add an idempotent flag to the control execution instruction to obtain a control execution instruction with an idempotent flag.

[0122] Specifically, an idempotent identifier is a unique marker appended to control execution instructions to ensure that the same action is not executed twice by the equipment when repeatedly triggered. The idempotent identifier consists of an instruction number, a target equipment identifier, a task timestamp, and a batch identifier. The instruction number distinguishes multiple control execution instructions under the same production task, the target equipment identifier distinguishes different process equipment and material handling equipment, the task timestamp marks the generation time of the control execution instruction, and the batch identifier corresponds to a specific production batch. After generating the control execution instruction, the idempotent identifier is appended to the metadata field of the instruction message, forming a control execution instruction with an idempotent identifier. When a control execution instruction with an idempotent identifier is issued to sterilizers, inoculation rooms, roller shutters, access control systems, and automated guided vehicles, the target equipment parses the idempotent identifier to confirm whether the instruction has already been executed. If it has been executed, the duplicate instruction is discarded; otherwise, the normal execution process begins. Through the function of the idempotent identifier, the uniqueness and traceability of the target equipment's actions are ensured even in the event of communication delays, repeated triggering, or manual intervention.

[0123] S402: Control execution instructions with idempotent flags are queued in the order determined by the cooperative interlock matrix, and a pre-state verification is performed based on the equipment feedback status information and material flow information before issuance to obtain control execution instructions that pass the verification.

[0124] Specifically, pre-execution status verification refers to the process of determining whether the target equipment meets the execution conditions based on equipment feedback status information and material flow information before the control execution command is issued. Control execution commands with idempotent flags are sorted using a collaborative interlock matrix to form a queue sequence. The queue sequence is arranged according to the execution order of process equipment and the running time of material handling equipment. For example, after the sterilization task in the sterilizer is completed, the queue sequence triggers the automated guided vehicle to transport materials to the inoculation room, and then triggers the opening of the roller shutter door and the operation of the inoculation room. Subsequently, pre-execution status verification is performed on each control execution command in the queue sequence. The verification content includes whether the equipment operating status reflected in the equipment feedback status information and the material arrival status reflected in the material flow information meet the constraints in the collaborative interlock matrix. For example, if the automated guided vehicle has not yet arrived at the inoculation room station or the air cleanliness of the inoculation room does not meet the standard, the corresponding control execution command is determined to have failed the pre-execution status verification and is not allowed to be issued. When both the equipment feedback status information and the material flow information meet the constraints, the corresponding control execution command is marked as a verified control execution command. A verified control execution command is a control execution command that has been confirmed to meet the execution conditions through pre-state verification and can be sent to the target equipment for execution, ensuring that process operations and material handling operations are carried out safely and orderly according to the established logic.

[0125] S403: Send the verified control execution command to the corresponding target device, and identify the execution confirmation flag based on the device feedback status information within the preset confirmation time limit to obtain the confirmation result.

[0126] Specifically, the preset confirmation time limit refers to the maximum time interval allowed for the target equipment to return execution feedback after the control execution command is issued. The preset confirmation time limit is set according to the equipment type and the execution cycle of the process. For example, the confirmation time limit for the heating stage of a sterilizer is several minutes, and the confirmation time limit for the opening action of a roller shutter door is several seconds. The execution confirmation identifier refers to the status flag extracted from the equipment feedback status information to indicate whether the target equipment has completed the command execution. Examples include the temperature status code after a sterilizer reaches and maintains the set temperature, the cleanliness status code after the air cleanliness of the inoculation room reaches the set level, the limit signal of the roller shutter door reaching the fully open position, the feedback signal of the access control unlocking completion, and the position confirmation code after the automated guided vehicle reaches the target workstation. The confirmation result refers to the result of judging the execution status of the target equipment based on the execution confirmation identifier. When a valid execution confirmation identifier is identified within the preset confirmation time limit, the confirmation result indicates that the control execution command has been successfully completed. When no execution confirmation identifier is identified within the preset confirmation time limit, the confirmation result indicates that the control execution command has not been completed as required.

[0127] S404: When the confirmation result indicates that the execution was unsuccessful, the control execution command is resent according to the preset number of retries and retry interval. If the retry still fails, a rollback command is generated according to the preset rollback table and sent to the corresponding device to restore the state to the safe baseline.

[0128] Specifically, the number of retry attempts refers to the maximum number of times the control execution command can be resent if the confirmation result indicates unsuccessful execution. The retry interval is the time interval between two adjacent retry attempts. The number of retry attempts and the retry interval are set according to the response characteristics of the target equipment and the production cycle time. For example, the number of retry attempts for a roller shutter door is three, and the retry interval is two seconds; the number of retry attempts for a sterilization cabinet is two, and the retry interval is five minutes. A rollback table is a pre-established table that records the action paths and safety parameters that can be reversed in the event of execution failure. The rollback table is configured according to the equipment type and process step. For example, the rollback table for an inoculation room includes parameters for restoring the air purification system to its initial cleanliness level, and the rollback table for an automated guided vehicle includes the path back to the previous safe workstation. A rollback command is a control command generated based on the rollback table to drive the target equipment back to a safe baseline state. The safety baseline state refers to the operational state that ensures process equipment and material handling equipment do not pose a risk to the production process. For example, sterilizers are closed and cooling down, roller shutters and access control systems are locked, the inoculation room is in air purification operation, and automated guided vehicles are parked at safe workstations. In practice, when the confirmation result indicates unsuccessful execution, the control execution command is resent according to the number of retries and the retry interval. If the retry still fails, a rollback command is generated based on the rollback table and sent to the target equipment to restore it to the safety baseline state, ensuring the safety and controllability of the production line operation.

[0129] In one embodiment, step S50, namely, performing a consistency check on the equipment feedback status information and material flow information to obtain the consistency check result, includes:

[0130] S501: Based on equipment feedback status information and material flow information, extract batch identifier, pallet identifier, container identifier, workstation identifier and time slice identifier to obtain five related elements.

[0131] Specifically, batch identifiers are unique identification numbers used to identify production batches of agricultural microbial preparations. These batch identifiers are assigned by the production planning system and remain consistent with equipment feedback status information and material flow information. Pallet identifiers are unique identification numbers used to identify pallets containing materials. Pallet identifiers are associated with the handling records of automated guided vehicles (AGVs). Container identifiers are unique identification numbers used to identify containers storing culture media or inoculum. Container identifiers correspond to both sterilization records in the sterilizer and operation logs in the inoculation room. Workstation identifiers are identification numbers used to distinguish different process locations on the production line. Workstation identifiers include sterilizer workstations, inoculation room workstations, culture chamber workstations, and access workstations corresponding to roller shutters and access control systems. Time slice identifiers are time period numbers generated according to a fixed sampling cycle during process operations and material handling, used to ensure the comparability of different equipment operating states and material locations in the time dimension. The five-element correlation element refers to a data set composed of batch identifiers, pallet identifiers, container identifiers, workstation identifiers, and time slice identifiers, used to establish the correspondence between equipment feedback status information and material flow information.

[0132] S502: Combine the five elements of the association to establish a five-element association key.

[0133] Specifically, the five-element association key refers to a composite index composed of batch identifier, pallet identifier, container identifier, workstation identifier, and time slice identifier combined in a preset order. It is used to uniquely identify the correspondence between equipment feedback status information and material flow information. In practice, the batch identifier is first used as the top-level field to ensure that all records within the same batch are categorized. Then, the pallet identifier and container identifier are added sequentially under the batch identifier to form a unique combination of material units. Next, the workstation identifier is added to distinguish the execution status of materials in different process locations such as sterilization cabinets, inoculation rooms, incubation chambers, or roller shutter doors and access control systems. Finally, the time slice identifier is added to ensure that the execution records of the same material unit remain distinguishable at different time periods.

[0134] S503: Under the constraint of the five-element correlation key, the corresponding records of equipment feedback status information and material flow information are compared to generate a consistency verification result.

[0135] Specifically, the consistency verification result refers to the conformity judgment result obtained by comparing the equipment feedback status information and material flow information based on the five-element association key. In the specific implementation, the five-element association key is first used as an index to retrieve the corresponding records of equipment feedback status information and material flow information in the database. For the same batch identifier, pallet identifier, and container identifier combination, it is checked whether it exists in both the equipment feedback status information and the material flow information under the same workstation identifier and the same time slice identifier. If the status of the two types of information records is consistent under the corresponding workstation identifier and time slice identifier, for example, the sterilization completion mark in the equipment feedback status information after the sterilization cabinet completes sterilization matches the exit record in the material flow information, or the operation end mark in the equipment feedback status information after the inoculation room operation is completed matches the pallet flow record in the material flow information, then the consistency verification result is generated as consistent. If only one record exists or there is a difference under the same five-element association key, for example, the roller shutter door feedback status shows closed but the material flow information record shows that the automatic guide car has passed, or the automatic guide car feedback shows that it has not arrived but the material flow record shows that the material has entered the inoculation room, then the consistency verification result is generated as inconsistent. The final consistency check result is output in either consistent or inconsistent form, providing a basis for the subsequent generation of control event information.

[0136] In one embodiment, in step S50, when a manual modification is detected, a revision audit chain corresponding to the control event information is generated, including:

[0137] S504: When a manual modification is detected, obtain the manual modification association information corresponding to the manual modification to obtain the manual modification record.

[0138] Specifically, manual modification-related information refers to the contextual information generated during production line operation when operators manually adjust equipment feedback status information or material flow information through a human-machine interface or control console. This information includes the value before modification, the value after modification, operator identification information, operation timestamp, and modification location identifier. Manual modification records are structured data compiled from manual modification-related information according to batch and workstation dimensions, used to characterize the occurrence of human intervention during production line operation. In practice, when the air cleanliness parameters in the inoculation room are manually overwritten by the operator, or the temperature and pressure data of the sterilizer are modified, or the opening and closing status of the roller shutter door and access control is manually changed, or the running position of the automated guided vehicle is adjusted, the system automatically collects the corresponding manual modification-related information after detecting the manual modification and writes it to the database, forming a manual modification record. The manual modification record is indexed by batch identifiers and workstation identifiers, used for subsequent generation of revision audit chains and binding with the target batch gene lineage.

[0139] S505: Based on the production task instructions corresponding to the target batch, extract the correspondence between the target batch and process equipment, sterilization cycle and inoculation room ledger, and construct the target batch gene lineage.

[0140] Specifically, the target batch genealogy refers to a hierarchical link constructed based on the process flow path of the target batch in the production task instruction, which includes the correspondence between process equipment, sterilization cycle, and inoculation room ledger. This is used to characterize the traceability relationship of the target batch across stages in the production process. In practice, firstly, based on the production task instruction corresponding to the target batch, the process equipment information associated with that batch is extracted, including sterilizers, inoculation rooms, culture chambers, and related roller shutters and access control systems. Then, the sterilization cycle information of the target batch in the sterilization process is extracted, including the sterilization temperature curve, pressure holding time, and cooling time, and the sterilization cycle is associated with the sterilizer equipment number. Further, the inoculation room ledger information is extracted, including inoculation operation time, operator number, air cleanliness test results, and material batch flow records, and the inoculation room ledger is bound to the inoculation room equipment number. Finally, the process equipment information, sterilization cycle information, and inoculation room ledger information are integrated using the target batch identifier as an index to construct the target batch gene lineage. The target batch gene lineage represents the execution trajectory of the target batch in different process links in the form of directed links, providing a basis for subsequent revision of the audit chain binding and cross-link traceability.

[0141] S506: Bind the manually modified records to the gene lineage of the target batch to obtain the bound modification traceability information.

[0142] Specifically, modification traceability information refers to composite traceability data formed by binding manual modification records with the target batch's genetic lineage. This information characterizes the correspondence between manual intervention and the target batch's execution trajectory across different stages. In practice, firstly, based on the batch identifier, workstation identifier, and operation timestamp fields in the manual modification records, the corresponding process equipment nodes and stages in the target batch's genetic lineage are retrieved. Examples include the sterilization cycle node of the sterilizer, the operation record node of the inoculation room, and the access nodes of the roller shutter door and access control system. Then, the values ​​before and after modification, operator identity information, and modification time information in the manual modification records are bound to the retrieved genetic lineage nodes, forming modification annotations corresponding to the target batch's execution trajectory. Finally, all binding results are integrated according to the execution order of the target batch to obtain modification traceability information. This information, in a chain-like format, reflects the role and scope of human intervention in the cross-stage flow of the target batch, providing a data foundation for the subsequent generation of the revision audit chain.

[0143] S507: Write the bound modification traceability information sequentially into the revision audit chain to generate a revision audit chain corresponding to the control event information.

[0144] Specifically, the revision audit chain refers to a traceability record stored in a sequential linked list structure, used to fully record and subsequently verify the manual modification process. In its implementation, the abnormal scenarios to be associated are first determined based on control event information, such as the sterilizer temperature being manually covered, the inoculation room operation log being modified, the status of the roller shutter door or access control being adjusted, or the automated guided vehicle's trajectory being tampered with. Then, the bound modification traceability information is sequentially written into the revision audit chain nodes according to timestamp order. Each node includes a batch identifier, workstation identifier, the value before modification, the value after modification, the operator's identity information, and the modification time information. A hash digest algorithm is used to calculate a verification value to prevent tampering. After generation, the revision audit chain is bound to the corresponding control event information, forming a complete traceable event chain, thereby enabling the manual intervention of the target batch in different process stages to be verified and traced back level by level.

[0145] In one embodiment, in step S60, when the running status information or control event information triggers a preset abnormal threshold, a handling control command is generated, including:

[0146] S601: Extract deviation information within a continuous time slice based on the running status information to obtain a deviation information sequence.

[0147] Specifically, deviation information refers to the numerical quantification result in the operational status information reflecting the degree of difference between the target parameter and the set value. Deviation information is obtained by calculating the difference between the operational parameter and the baseline set value. A deviation information sequence is a set of deviation information arranged chronologically within a continuous time slice, used to reflect the deviation trend of the target equipment within a certain time range. In practice, key operational parameters are first extracted from the operational status information, including the temperature and pressure of the sterilizer, the air cleanliness of the inoculation room, the opening and closing position of the roller shutter door, the unlocking status of the access control system, the position coordinates of the automated guided vehicle, and the humidity and temperature of the incubation chamber. Then, corresponding baseline set values ​​are determined for each type of operational parameter, such as the target temperature and pressure set values ​​for the sterilizer, the target cleanliness level of the inoculation room, the threshold values ​​for the fully open and fully closed states of the roller shutter door, and the target workstation coordinates of the automated guided vehicle. The deviation information for each time slice is obtained by calculating the difference between the actual operational parameter value and the corresponding baseline set value. Finally, the deviation information for all time slices is arranged chronologically to form a deviation information sequence, which is used for subsequent anomaly detection and handling control command generation.

[0148] S602: Obtain historical control event information.

[0149] Specifically, historical control event information refers to the set of historical data related to control events recorded by the system during production line operation, reflecting past anomaly handling and human intervention. Historical control event information includes the batch identifier, workstation identifier, event type, handling control instruction, execution confirmation result, modification traceability information, and corresponding timestamp. In practice, the database is first searched for historical control event information records related to the target batch based on the batch identifier, extracting the corresponding equipment operation anomaly events and material flow anomaly events. For sterilization cabinets, historical control event information includes handling control instructions and rollback records generated when temperature or pressure did not meet standards; for inoculation rooms, it includes operation interruption records and human modification traces when air cleanliness did not meet standards; for roller shutters and access control systems, it includes abnormal shutdown events when actions were not performed according to instructions; and for automated guided vehicles, it includes anomaly events such as deviation from the path or failure to reach the target workstation. Obtaining historical control event information provides a reference for subsequent spectral anomaly statistics and dynamic anomaly threshold correction.

[0150] S603: Based on the target batch gene lineage and historical control event information corresponding to the target batch, statistical information on lineage anomalies is obtained.

[0151] Specifically, pedigree anomaly statistics refer to the statistical results formed by classifying and quantifying abnormal events based on the pedigree of the target batch and historical control event information. This information is used to characterize the distribution of anomalies in different process stages of the target batch. In practice, the target batch pedigree is first used as the main index, and its corresponding historical control event records are matched one by one, establishing a correspondence between abnormal events and process equipment nodes and stage nodes within the pedigree. For example, in the sterilizer stage, the frequency of temperature non-compliance events and pressure anomalies is counted; in the inoculation room stage, the number of air cleanliness non-compliance events and manual modification events is counted; in the roller shutter and access control stage, the number of anomalies of not opening or closing according to instructions is counted; in the automated guided vehicle stage, anomalies of path deviation and arrival failure are counted; and in the culture chamber stage, anomalies of temperature and humidity exceeding standards are counted. Then, according to the process equipment category and stage sequence, the abnormal events are counted and weighted to obtain pedigree anomaly statistics that include batch, stage, and anomaly type dimensions.

[0152] S604: The task release score and the spectrum anomaly statistics are weighted and synthesized to obtain the threshold correction factor information, and the preset anomaly threshold is corrected accordingly to obtain the dynamic anomaly threshold information.

[0153] Specifically, the threshold correction factor information refers to the correction coefficient obtained by weighted synthesis based on task release scores and phenotypic anomaly statistics, used to dynamically adjust the preset anomaly threshold. The dynamic anomaly threshold information refers to the anomaly judgment threshold after correction by the threshold correction factor information, used to adapt to the actual operating status under different batches and process conditions. In practice, the task release score is first standardized to the 0–1 range, and then weighted and synthesized with the anomaly frequency and severity index from the phenotypic anomaly statistics to form the threshold correction factor information. The weighted synthesis uses the following calculation formula:

[0154] Where Fadj represents the threshold correction factor information, Srel represents the standardized value of the task release score, and Ast represents the weighted anomaly index of the phylogenetic anomaly statistics. and For the weighting coefficients, satisfying To ensure dimensional consistency, the threshold correction factor information is then applied to the preset anomaly threshold to obtain dynamic anomaly threshold information. The calculation formula is as follows:

[0155] Where Tdyn represents the dynamic anomaly threshold information, and Tpre represents the preset anomaly threshold. Using this calculation method, if the spectral anomaly statistics indicate frequent anomalies or a low task release score, the threshold correction factor is increased, lowering the dynamic anomaly threshold information and making it easier to trigger anomaly detection. Conversely, if anomalies are few and the task release score is high, the dynamic anomaly threshold information is increased to avoid over-triggering. The dynamic anomaly threshold information serves as the benchmark threshold for subsequent deviation information sequence comparison, ensuring that the anomaly detection process matches the actual risk level of the target batch.

[0156] S605: Accumulate the deviation information sequence within the sliding time slice to obtain the abnormal accumulation degree information, compare the abnormal accumulation degree information with the dynamic abnormal threshold information to obtain the abnormal judgment result, and generate the handling control instruction based on the abnormal judgment result.

[0157] Specifically, the anomaly accumulation information refers to the metric obtained by accumulating the deviation information sequence within a sliding time slice, reflecting the degree of deviation accumulation of the target equipment within a certain time range. The anomaly determination result refers to the judgment conclusion obtained by comparing the anomaly accumulation information with the dynamic anomaly threshold information, used to characterize whether the target equipment or process has entered an abnormal state. In practice, the deviation information sequence is first calculated using a weighted accumulation method within the sliding time slice, as shown in the following formula:

[0158] Where Dcum(t) represents the anomaly accumulation information at time slice t. This represents the deviation information for the i-th time slice, where wi represents the time decay factor to ensure that recent deviations have a higher weight in the cumulative calculation, and n represents the sliding time slice length. Then, the anomaly accumulation information is compared with the dynamic anomaly threshold information. When the anomaly accumulation information is greater than the dynamic anomaly threshold information, an anomaly judgment result is generated, indicating an abnormal state; otherwise, it is judged as a normal state. Finally, based on the anomaly judgment result, a handling control command is generated. For example, a temperature rollback or shutdown command is generated in the sterilizer stage, an air purification enhancement command is generated in the inoculation room stage, a passage prohibition command is generated in the roller shutter and access control stage, a path reversal command is generated in the automated guided vehicle stage, and a humidity control or shutdown command is generated in the culture chamber stage. The handling control command, as the output of anomaly management, is used to trigger different levels of handling measures.

[0159] In one embodiment, step S60 involves executing corresponding exception handling operations based on the handling control instructions at different exception levels, including:

[0160] S606: Based on the handling control command and the collaborative interlock matrix, identify the equipment action dependencies related to the anomaly to obtain the handling coverage information.

[0161] Specifically, the disposal coverage information refers to the set of affected equipment and its execution scope formed by the equipment action dependencies identified based on disposal control commands and the collaborative interlock matrix during abnormal disposal. In practice, the target links and target equipment in the disposal control commands are first parsed, such as sterilizer shutdown commands, inoculation room air purification enhancement commands, roller shutter door closing commands, access control locking commands, or automated guided vehicle (AGV) path reversal commands. Then, the equipment action dependencies related to the target equipment are retrieved from the collaborative interlock matrix. For example, inoculation room operations depend on sterilizer exit confirmation, AGV entry and exit depend on roller shutter door and access control status, and culture chamber operation depends on inoculation room completion confirmation. Through dependency expansion, multi-level equipment links that may be affected by disposal control commands are obtained. For example, sterilizer shutdown not only affects the sterilizer itself but also subsequent operations in the inoculation room and material handling by the AGV. Finally, all affected equipment and action scopes are aggregated and boundary conditions are identified to generate disposal coverage information. This disposal coverage information, indexed by batch identifiers and workstation identifiers, represents the entire range of equipment that needs to be constrained when executing disposal control commands.

[0162] S607: Based on the coverage information, the disposal control instructions are processed by shrinking the scope and rearranging the timing to obtain the shrunken disposal control instructions.

[0163] Specifically, the contracted disposal control instructions refer to the optimized set of disposal instructions generated through scope contraction and temporal reordering under the constraints of disposal coverage information. This set is used to reduce irrelevant equipment actions and ensure a reasonable execution order during abnormal disposal processes. In practice, firstly, all affected equipment actions are identified based on the disposal coverage information, such as sterilizer shutdown, enhanced air purification in the inoculation room, roller shutter closing, access control locking, and automated guided vehicle (AGV) reversal. Then, scope contraction is performed on this set of equipment actions, removing actions that are not directly related to the current abnormal disposal or overlap with it. For example, in the sterilizer shutdown abnormal disposal scenario, actions unrelated to the culture chamber are removed. Subsequently, the remaining equipment actions are temporally reordered according to the dependencies in the collaborative interlocking matrix. For example, the inoculation operation abortion action is delayed until the air purification action in the inoculation room is completed, and the AGV reversal action is executed only after the roller shutter door is closed. Finally, the equipment actions that have undergone scope shrinking and timing rearrangement are recombined into a complete set of handling control instructions, resulting in the shrunken handling control instructions. These shrunken handling control instructions serve as the input basis for subsequent anomaly level judgment and handling execution.

[0164] S608: The compressed disposal control command is sent to the corresponding process equipment and material handling equipment to obtain disposal information.

[0165] Specifically, disposal information refers to the execution feedback information returned by process equipment and material handling equipment after receiving the condensed disposal control command, used to characterize the actual execution status of abnormal disposal actions. In practice, the condensed disposal control command is first assigned to the corresponding target equipment according to equipment category. For example, the sterilization shutdown command is issued to the sterilization cabinet control unit, the air purification enhancement command is issued to the inoculation room environmental control device, the shutdown command is issued to the roller shutter door and access control system, the reversal command is issued to the automated guided vehicle (AGV) navigation system, and the shutdown or adjustment command is issued to the culture chamber monitoring unit. Subsequently, within a preset confirmation time limit, the feedback status information from each device is collected, and confirmation markers corresponding to the disposal actions are extracted, such as the sterilization cabinet shutdown confirmation signal, the inoculation room cleanliness restoration signal, the roller shutter door and access control system closure signal, the AGV arrival signal, and the culture chamber environmental parameter restoration signal. Disposal information is generated by organizing and timestamping the feedback status information from each device. The disposal information is stored using batch identifiers and workstation identifiers as indexes, and is used for subsequent abnormality level processing and the writing of revised audit chains.

[0166] S609: When the abnormality level is not an emergency stop level, the graded handling actions shall be implemented in sequence according to the phased execution strategy, and the handling coverage information shall be updated based on the equipment feedback status information and material flow information after the completion of each phase.

[0167] Specifically, a phased execution strategy refers to dividing the handling process into several consecutive phases in abnormal scenarios that are not at the emergency stop level. Each phase corresponds to handling actions of different intensities and ranges. Graded handling actions refer to handling measures that are progressively stricter according to the phased execution strategy, such as speed limits, partial shutdowns, and complete shutdowns. In practice, the phased execution strategy is first selected based on the abnormality level determination. For example, when the air cleanliness in the inoculation room deviates slightly, the first phase implements speed-limited ventilation, the second phase implements partial shutdown, and the third phase implements a complete shutdown. When the automated guided vehicle (AGV) deviates slightly from its path, the first phase implements speed-limited travel, the second phase implements partial stopping, and the third phase implements full stopping. Subsequently, after each phase of handling action is completed, equipment feedback status information and material flow information are collected to verify the effectiveness of the current handling action. The handling coverage information is updated based on the new operating status; for example, after the sterilizer stops, the constraint range of related actions in the inoculation room and culture chamber is expanded, and after the roller shutter door closes, the range of related actions of the AGV is contracted. In this way, a dynamic and iterative handling process is formed, which allows the handling coverage information to be gradually adjusted according to the phased execution strategy, ensuring that the graded handling actions are consistent with the production line operation status.

[0168] S610: When the anomaly level is the emergency stop level, global handling is executed based on the contracted handling control instructions, and the handling information is bound with the control event information to obtain the bound handling information. The bound handling information is written into the revision audit chain for the purpose of correcting the target batch gene lineage.

[0169] Specifically, the bound disposal information refers to a composite record formed by mapping disposal information to control event information in an emergency stop-level abnormal scenario. This record characterizes the correlation between global disposal actions and abnormal events. In practice, disposal actions are first executed globally based on a contracted disposal control command, including immediate shutdown of the sterilizer, complete cessation of inoculation room operations, forced closure of roller shutters and access control systems, emergency stop of automated guided vehicles, and shutdown of the culture chamber. Then, feedback statuses from various process equipment and material handling equipment during the emergency stop process are collected to form disposal information, and the corresponding action confirmation time and status marker are recorded in the database. Next, the disposal information and control event information are bound according to batch identifiers and workstation identifiers to generate bound disposal information, ensuring that each disposal action can be traced back to the corresponding abnormal event. Finally, the bound disposal information is written into the revision audit chain in a sequential node manner and updated in association with the target batch genotype, enabling the target batch genotype to reflect the occurrence of the emergency stop in the traceability chain, thereby ensuring the integrity of cross-stage anomaly management and traceability.

[0170] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0171] In one embodiment, a multi-equipment collaborative management and control system for an agricultural microbial preparation production line is provided. This system corresponds one-to-one with the multi-equipment collaborative management and control method for agricultural microbial preparation production lines described in the above embodiments. For example... Figure 4 As shown, the multi-equipment collaborative management and control system for the agricultural microbial preparation production line includes an operation status assessment module, a task release judgment module, a collaborative interlock construction module, an instruction distribution and execution module, a consistency verification and event management module, and an anomaly handling module.

Claims

1. A method for collaborative management and control of multiple equipment in an agricultural microbial preparation production line, characterized in that, The method for coordinated management and control of multiple equipment in the agricultural microbial preparation production line includes: Obtain the operating status information of the target equipment in the production line, and calculate the equipment health score based on the operating status information; The process of acquiring the operating status information of the target equipment in the production line and calculating the equipment health score based on the operating status information includes: Obtain the corresponding time series of operating parameters from the operating status information, and calculate the fluctuation trend index that characterizes the stability of equipment operation based on the time series of operating parameters; Based on the operating parameters in the operating status information that characterize the collaborative relationship between the target equipment and other process equipment, calculate the collaborative coupling deviation value of the target equipment; The fluctuation trend index is combined with the collaborative coupling deviation value, and a weighted calculation is performed based on the risk sensitivity factors corresponding to the target process link to obtain the equipment health score; Obtain the biological risk index of the target batch, combine the equipment health score with the biological risk index to generate a task release score, and generate a production task instruction when the task release score meets the preset release threshold. Based on production task instructions, a collaborative interlock matrix is ​​constructed between various types of process equipment and material handling equipment. The collaborative interlock matrix is ​​used to constrain the execution sequence between multiple devices. When multiple types of process equipment are in an executable state and material handling equipment has reached the designated position and is in a safe state, a control execution instruction is generated. The control execution instructions are distributed to various types of process equipment and material handling equipment to drive the execution of process operations and material handling operations, and the equipment feedback status information and material flow information are obtained during the execution of process operations and material handling operations, respectively. Perform consistency checks on equipment feedback status information and material flow information to obtain consistency check results. When the consistency check results indicate that there is a deviation between the equipment feedback status information and the material flow information, generate control event information. When manual modification is detected, generate a revision audit chain corresponding to the control event information. When the running status information or control event information triggers the preset abnormal threshold, a handling control instruction is generated, and the corresponding abnormal handling operation is executed according to the handling control instruction under different abnormal levels.

2. The method for collaborative management and control of multiple equipment in an agricultural microbial preparation production line according to claim 1, characterized in that, Obtain the bio-risk index of the target batch, and combine the equipment health score with the bio-risk index to generate a task release score, including: The risk parameters of the target batch are standardized to obtain a risk feature vector; The risk feature vector of the target batch is weighted and calculated based on the risk sensitivity factors corresponding to the target process steps to obtain the biological risk index. When the biological risk index is higher than the preset risk threshold, the equipment health score is corrected according to the preset inhibition rules to obtain the corrected equipment health score. The revised equipment health score and the biological risk index of the target batch are weighted and combined according to the calculation rules of the mission release score to obtain the mission release score.

3. The method for collaborative management and control of multiple equipment in an agricultural microbial preparation production line according to claim 1, characterized in that, Based on production task instructions, a collaborative interlocking matrix is ​​constructed between various types of process equipment and material handling equipment, including: Analyze production task instructions and extract the corresponding process execution requirements and material handling requirements; Determine the execution sequence of various process equipment according to process execution requirements, and determine the operating location and timing of material handling equipment according to material handling requirements. Based on the execution sequence of various types of process equipment and the operating position and timing of material handling equipment, establish the dependencies and safety constraints between equipment actions; Dependencies and security constraints are transformed into interlocking rules, generating a collaborative interlocking matrix.

4. The method for collaborative management and control of multiple equipment in an agricultural microbial preparation production line according to claim 1, characterized in that, The process of distributing control execution instructions to various types of process equipment and material handling equipment also includes: By appending an idempotent flag to the control execution instructions, control execution instructions with an idempotent flag are obtained. Control execution instructions with idempotent flags are queued in the order determined by the collaborative interlock matrix, and a pre-state verification is performed based on the equipment feedback status information and material flow information before issuance to obtain control execution instructions that pass the verification. The control execution command that has passed the verification is sent to the corresponding target device, and the execution confirmation flag is identified based on the device feedback status information within the preset confirmation time limit to obtain the confirmation result; When the confirmation result indicates that the execution was unsuccessful, the control execution command is resent according to the preset number of retries and retry interval. If the retry still fails, a rollback command is generated according to the preset rollback table and sent to the corresponding device to restore the state to the safe baseline.

5. The method for collaborative management and control of multiple equipment in an agricultural microbial preparation production line according to claim 1, characterized in that, Perform consistency checks on the equipment status information and material flow information to obtain the consistency check results, including: Based on equipment feedback status information and material flow information, batch identifier, pallet identifier, container identifier, workstation identifier and time slice identifier are extracted to obtain five related elements; Combine the five elements of association to establish a five-element association key; Under the constraint of the five-element correlation key, the corresponding records of equipment feedback status information and material flow information are compared to generate a consistency verification result.

6. The method for coordinated management and control of multiple equipment in an agricultural microbial preparation production line according to claim 1, characterized in that, When human modification is detected, a revision audit chain corresponding to the control event information is generated, including: When a manual modification is detected, the associated information of the manual modification is obtained to get the manual modification record. Based on the production task instructions corresponding to the target batch, the correspondence between the target batch and process equipment, sterilization cycle and inoculation room ledger is extracted, and the gene lineage of the target batch is constructed. By linking manually modified records with the gene lineage of the target batch, the modification traceability information after binding is obtained; The bound modification traceability information is sequentially written into the revision audit chain to generate a revision audit chain corresponding to the control event information.

7. The method for collaborative management and control of multiple equipment in an agricultural microbial preparation production line according to claim 1, characterized in that, When the operating status information or control event information triggers a preset abnormal threshold, a handling control instruction is generated, including: Based on the running status information, deviation information within a continuous time slice is extracted to obtain a deviation information sequence; Obtain historical control event information; Based on the anomaly occurrence information of the gene lineage and historical control event information corresponding to the target batch, the lineage anomaly statistics are obtained. The task release score and the spectrum anomaly statistics are weighted and synthesized to obtain threshold correction factor information, and the preset anomaly threshold is corrected accordingly to obtain dynamic anomaly threshold information. The deviation information sequence is accumulated within a sliding time slice to obtain the abnormal accumulation degree information. The abnormal accumulation degree information is compared with the dynamic abnormal threshold information to obtain the abnormal judgment result. Based on the abnormal judgment result, a handling control instruction is generated.

8. The method for collaborative management and control of multiple equipment in an agricultural microbial preparation production line according to claim 1, characterized in that, Based on the handling control instructions, execute the corresponding exception handling operations at different exception levels, including: Based on the control commands and the collaborative interlock matrix, the device action dependencies related to the anomalies are identified to obtain the control coverage information. Based on the coverage information, the disposal control instructions are processed by scope narrowing and timing rearrangement to obtain the narrowed disposal control instructions; The compressed disposal control command is sent to the corresponding process equipment and material handling equipment to obtain disposal information; When the abnormality level is not an emergency stop level, the graded handling actions are carried out in sequence according to the phased execution strategy, and the handling coverage information is updated based on the equipment feedback status information and material flow information after the completion of each phase. When the anomaly level is an emergency stop level, global handling is executed based on the contracted handling control instructions, and the handling information is bound with the control event information to obtain the bound handling information. The bound handling information is written into the revision audit chain for the purpose of correcting the target batch gene lineage.

9. A multi-equipment collaborative control system for agricultural microbial preparation production lines, characterized in that: The multi-equipment collaborative control system for the agricultural microbial preparation production line includes: The operation status assessment module is used to acquire the operation status information of target equipment in the production line and calculate the equipment health score based on the operation status information; The operational status assessment module includes: Obtain the corresponding time series of operating parameters from the operating status information, and calculate the fluctuation trend index that characterizes the stability of equipment operation based on the time series of operating parameters; Based on the operating parameters in the operating status information that characterize the collaborative relationship between the target equipment and other process equipment, calculate the collaborative coupling deviation value of the target equipment; The fluctuation trend index is combined with the collaborative coupling deviation value, and a weighted calculation is performed based on the risk sensitivity factors corresponding to the target process link to obtain the equipment health score; The task release judgment module is used to obtain the biological risk index of the target batch, combine the equipment health score with the biological risk index to generate a task release score, and generate a production task instruction when the task release score meets the preset release threshold. The collaborative interlocking construction module is used to build a collaborative interlocking matrix between various types of process equipment and material handling equipment based on production task instructions. The collaborative interlocking matrix is ​​used to constrain the execution sequence between multiple devices. When multiple types of process equipment are in an execution-allowed state and material handling equipment has reached the designated position and is in a safe state, control execution instructions are generated. The instruction distribution and execution module is used to distribute control execution instructions to various types of process equipment and material handling equipment, drive the execution of process operations and material handling operations, and obtain equipment feedback status information and material flow information during the execution of process operations and material handling operations, respectively. The consistency verification and event management module is used to perform consistency verification on the equipment feedback status information and material flow information, and obtain the consistency verification result. When the consistency verification result indicates that there is a deviation between the equipment feedback status information and the material flow information, control event information is generated. When manual modification is detected, a revision audit chain corresponding to the control event information is generated. The exception handling module is used to generate handling control instructions when the running status information or control event information triggers the preset exception threshold, and to perform corresponding exception handling operations according to the handling control instructions under different exception levels.

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