A thermal runaway safety monitoring system for electrochemical energy storage systems in distribution networks

Abstract

The present invention relates to the technical field of energy storage system safety monitoring, and specifically to a thermal runaway safety monitoring system for an electrochemical energy storage system in a distribution network. The system includes a task classification module, a priority scoring module, a task allocation module, a resource locking module, a thermal runaway monitoring module, and an abnormality control module. In the present invention, by carefully managing the urgency of tasks and resource requirements, timely adjusting resource allocation and task priorities, significantly improving the efficiency and response speed of the energy storage system, continuously monitoring the dynamic matching of resource status and task requirements, effectively avoiding operational delays due to resource shortages, tracking node temperatures and current parameters in real time, quickly identifying and intervening in processing potential thermal runaway risk nodes, and avoiding serious accidents, this optimization of resource and risk management not only enhances the safety of the system, but also ensures efficient use of energy and the system's resistance to failures, greatly improving the overall stability and reliability of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage system safety monitoring, and in particular to a thermal runaway safety monitoring system for an electrochemical energy storage system in a distribution network. Background Art

[0002] The field of energy storage system safety monitoring technology involves the application of various methods and technologies to ensure the safety of energy storage equipment such as battery packs during operation. The technical field pays special attention to identifying and preventing various factors that cause safety accidents, such as overheating, overcharging, electrical runaway and chemical instability. The safety monitoring system collects and analyzes data from the energy storage system in real time, and monitors key performance indicators such as temperature, pressure, voltage, current, etc. When the parameters exceed the normal operating range, the monitoring system will trigger an alarm and take measures to avoid failure or damage, such as automatically disconnecting the power supply or starting the cooling system. The technical field also includes the development of advanced diagnostic tools to detect potential failures in advance and predict system life, thereby improving overall operating efficiency and safety.

[0003] Among them, the thermal runaway safety monitoring system for electrochemical energy storage systems in distribution networks focuses on real-time monitoring and prevention of thermal runaway events in electrochemical energy storage devices, such as battery packs. Thermal runaway refers to the uncontrolled increase in temperature of energy storage devices due to internal faults or external factors, further causing fire or explosion. The main purpose of this monitoring system is to prevent such extreme events by continuously monitoring key parameters (such as temperature, voltage, current, etc.) to ensure the safe operation of the system. This monitoring system is essential to improving the safety and stability of the power distribution network, including when it comes to electrochemical energy storage devices with high energy density. Effective monitoring can significantly reduce safety risks and protect the equipment and its surrounding environment.

[0004] Existing technologies have limitations in monitoring and response mechanisms, mainly in that responses to abnormal situations are not timely or accurate enough, especially when high-energy density equipment is running, and preventive measures for serious accidents such as thermal runaway are limited. This is mainly because traditional monitoring systems fail to fully integrate advanced analytical tools in data processing and risk assessment, resulting in the inability to quickly take effective measures in high-risk situations. For example, when temperature or current is abnormal, existing systems fail to detect or identify it as a high-risk state in real time, thereby delaying the implementation of prevention and control measures. Existing technologies are relatively static in resource allocation and lack the ability to make dynamic adjustments based on real-time data. This can easily lead to insufficient or excessive resources in an environment where resource demand changes frequently, affecting the overall efficiency and safety of the system, making it impossible for the system to effectively protect the equipment and its surrounding environment in the face of emergencies or sudden events, increasing the risk of accidents and potential losses. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings existing in the prior art and to propose a thermal runaway safety monitoring system for an electrochemical energy storage system in a distribution network.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: A thermal runaway safety monitoring system for an electrochemical energy storage system in a distribution network comprises:

[0007] The task classification module extracts task urgency parameters based on the task requirements in chemical energy storage, filters task values, arranges tasks by resource usage, records task types and priorities, and generates a task resource usage list;

[0008] The priority scoring module, based on the task resource occupancy list, calls the priority identifier to quantify the task urgency, extracts the resource consumption index to assign a weight value, weights and sorts the task urgency and resource weight, and generates a priority task weight table;

[0009] The task allocation module extracts the current resource status based on the priority task weight table, matches the priority task resource demand, verifies the resource pool in real time, screens out tasks with insufficient resources, and generates a scheduling allocation list;

[0010] The resource locking module extracts the resource requirements of the priority tasks based on the scheduling allocation list, locks the matching resources, sets locking rules and records the task resource allocation, and generates a resource locking status table;

[0011] The thermal runaway monitoring module extracts the temperature and current parameters of the energy storage system nodes based on the resource locking status table, detects the temperature exceeding the limit nodes, records the exceeding limit status, lists the qualified nodes in the abnormal risk list, and generates a thermal runaway risk list;

[0012] Based on the thermal runaway risk list, the abnormal control module analyzes the node abnormality frequency and the thermal runaway cumulative status, marks the risk level of the abnormal node, implements risk intervention, and generates a thermal runaway graded control plan.

[0013] As a further solution of the present invention, the task resource occupancy list includes task urgency, resource occupancy ranking, and task type records; the priority task weight table includes priority identification, weight assignment, and task ranking records; the scheduling allocation list includes resource matching status, resource verification results, and priority task screening; the resource locking status table includes resource locking operations, locking rule settings, and resource allocation records; the thermal runaway risk list includes node temperature monitoring, current parameter analysis, and abnormal node records; and the thermal runaway hierarchical control scheme includes risk level marking, implementation of intervention measures, and operation mode adjustment.

[0014] As a further solution of the present invention, the task classification module includes:

[0015] The node parameter arrangement submodule extracts the urgency values ​​of tasks one by one based on the task requirements in chemical energy storage, classifies the tasks by analyzing the values ​​in the urgency field, and arranges the urgency of each task to generate a task urgency parameter table;

[0016] The task screening submodule screens the urgency values ​​of the tasks in order based on the task urgency parameter table, determines the tasks with urgency values, marks the task types, screens out low-priority tasks one by one, identifies the task types, and generates a task priority list;

[0017] The resource occupancy recording submodule is based on the task priority list, sorts the tasks in order according to the resource requirement values, allocates resources according to the task requirements, records the resource occupancy values ​​and task order of the tasks, arranges them in order of priority, and generates a task resource occupancy list.

[0018] As a further solution of the present invention, the priority scoring module includes:

[0019] The urgency quantification submodule reads the priority identification of each task based on the task resource occupation list, performs numerical conversion, converts the content of the priority identification into numerical form, processes and records the priority identification information of the task item by item, and generates a task urgency table;

[0020] The resource weight assignment submodule extracts the resource consumption value of each task based on the task urgency table, performs weighted processing on the resource consumption, combines the task urgency with the resource consumption, assigns corresponding weight values ​​item by item, and generates a weighted task list;

[0021] The dynamic priority sorting submodule performs weighted calculation on the urgency and resource consumption weight of the task based on the weighted task list, arranges the weighted values ​​of the tasks in sequence, sorts the tasks according to the weighted values, records the priority values ​​of the sorted tasks, and generates a priority task weight table.

[0022] As a further solution of the present invention, the task allocation module includes:

[0023] The resource status extraction submodule reads the resource type and current occupied value of each task item by item based on the priority task weight table, matches the resource type, resource demand and remaining amount by scanning the available resource quantity of the energy storage system resource pool, records the current value of the resource, summarizes and organizes it, and obtains the current resource status table;

[0024] The resource demand matching submodule retrieves the task resource demand based on the current resource status table, and eliminates the tasks with insufficient resources by comparing the demand with the current available quantity item by item, and retains the tasks that meet the resource demand in the screening list, thereby obtaining a list of qualified tasks;

[0025] The resource priority allocation submodule arranges the task list according to priority based on the qualified task list, adds the tasks to the scheduling list according to the priority, records the type and quantity of resources allocated to the tasks, updates the task order in the scheduling list, and obtains the scheduling allocation list.

[0026] As a further solution of the present invention, the resource locking module includes:

[0027] The resource requirement analysis submodule reads the resource requirements of the priority tasks based on the scheduling allocation list, identifies the resource types, verifies the resource requirement values ​​of each task, and generates a task resource requirement table by recording the allocatable values ​​of each resource and the task requirements;

[0028] The exclusive locking submodule sets the exclusive state of the matching resources based on the task resource requirement table, marks the resources as exclusive locks by calling the priority of the tasks, arranges the allocation records according to the usage duration and priority of the tasks, and locks and stores the resource allocation of each task according to the resource requirements and usage time limits of the tasks in the requirement table, and generates a locked resource list;

[0029] The status update submodule is based on the locked resource list and adopts an adaptive resource allocation optimization algorithm to monitor the usage status of the allocated resources in real time, check and update the current status of each resource, update the availability of the resource pool by recording the locking time and usage status of the resource, classify the resource locking information into the status table, compile the current resource locking status, and generate a resource locking status table.

[0030] As a further solution of the present invention, the formula of the adaptive resource allocation optimization algorithm is as follows:

[0031]

[0032] Calculate the real-time lock value of the resource pool and generate a resource lock status table, where: Indicates the real-time lock value of the resource pool. Real-time data representing the length of time a resource has been locked. Statistics representing the current utilization of resources, Indicates the total number of available resources in the current resource pool. Represents the capacity of the resource pool. Represents the locking weight coefficient of a single resource, is the adjustment coefficient.

[0033] As a further solution of the present invention, the thermal runaway monitoring module includes:

[0034] The node parameter sorting submodule extracts the temperature and current values ​​of the nodes based on the resource locking state table, identifies the value type of the nodes and sorts them by category, arranges the temperature and current data in the order of the nodes, and generates a node parameter table;

[0035] The node detection submodule checks the temperature value of the node based on the node parameter table, determines whether the temperature value exceeds the safety limit, marks the abnormal state of the node that exceeds the limit, records the temperature deviation value, verifies the real-time temperature and deviation value of the node, lists the node that exceeds the limit in the abnormal list, sorts out the exceeding parameters, and generates an abnormal node list;

[0036] Based on the abnormal node list, the risk recording submodule uses time series analysis technology to record the abnormal frequency of the nodes, count the number of abnormalities by node and time sequence, archive each time point of exceeding the limit, summarize the accumulated abnormal data of each node, and generate a thermal runaway risk list.

[0037] As a further solution of the present invention, the formula of the time series analysis technology is as follows:

[0038] Calculate the thermal runaway risk assessment value of the node and generate a thermal runaway risk list, where: represents the thermal runaway risk assessment value, Represents the weight coefficient of the node where the abnormal event occurs, Indicates the number of abnormalities of the node. represents the corresponding time interval of the nodes in the time series, It is the adjustment coefficient used to smooth abnormal fluctuations.

[0039] As a further solution of the present invention, the abnormality control module includes:

[0040] Based on the thermal runaway risk list, the risk assessment submodule checks the abnormal frequency records of the nodes one by one, calculates the cumulative risk value of each node, classifies the nodes according to the risk size, puts the risk nodes in the priority processing list, and marks the risk level of each node, corresponds the node number to the risk level, and generates a node risk level table;

[0041] The intervention measures submodule locates risk nodes based on the node risk level table, formulates intervention measures of cooling and load reduction according to the risk level, applies them to the corresponding nodes, monitors the implementation status of each intervention measure, records and organizes the node status of the executed measures, and generates risk intervention records;

[0042] Based on the risk intervention record, the mode adjustment submodule monitors the operating status of the node after the intervention, collects the numerical changes of the node after the intervention, optimizes the operating mode, adjusts the parameters of the energy storage system according to the node feedback, and applies it to the node environment, summarizes the adjusted data, and generates a thermal runaway hierarchical control plan.

[0043] Compared with the prior art, the advantages and positive effects of the present invention are:

[0044] In the present invention, by finely managing the urgency and resource requirements of each task, resource allocation and task priority are adjusted in real time, and the operating efficiency and response speed of the energy storage system are significantly improved. By continuously monitoring the resource status and dynamically matching it with task requirements, resource allocation errors can be effectively prevented, and the risk of delays due to insufficient resources can be reduced. By real-time tracking of node temperature and current parameters, risk nodes that cause thermal runaway can be identified in time, so that early intervention can be made to prevent serious accidents. This comprehensive optimization of resource use and risk management enhances the safety of the system, while ensuring the maximum utilization of energy and avoiding waste of resources. By dividing the risk levels of abnormal nodes and implementing intervention measures in a timely manner, the system's ability to resist potential failures is greatly enhanced, and the overall stability and reliability of the system are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is a system flow chart of the present invention;

[0046] Figure 2 It is a schematic diagram of the system framework of the present invention;

[0047] Figure 3 It is a flowchart of the task classification module in the present invention;

[0048] Figure 4 is a flow chart of the priority scoring module in the present invention;

[0049] Figure 5 It is a flowchart of the task allocation module in the present invention;

[0050] Figure 6 It is a flow chart of the resource locking module in the present invention;

[0051] Figure 7 This is a flow chart of the thermal runaway monitoring module in the present invention;

[0052] Figure 8 It is a flow chart of the abnormal control module in the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0054] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0055] Embodiment 1

[0056] See also Figure 1 to Figure 2 , a thermal runaway safety monitoring system for an electrochemical energy storage system in a distribution network comprises:

[0057] The task classification module extracts the urgency parameters of the tasks based on the task requirements in chemical energy storage, performs numerical screening on the tasks, arranges the tasks in sequence according to the resource occupancy of the energy storage system, records the type parameters and priority identification of each task, and generates a task resource occupancy list;

[0058] The priority scoring module uses the energy storage system’s priority identifier to quantify the task urgency based on the task resource occupancy list, extracts resource consumption indicators and assigns weight values, sorts tasks by weighted calculation of urgency and resource weights, records the dynamic priority values ​​of tasks, and generates a priority task weight table.

[0059] The task allocation module extracts the current resource status of the energy storage system based on the priority task weight table, matches the resource demand of the priority tasks, verifies the real-time resource quantity of the resource pool, screens and removes tasks with insufficient resources, sorts the qualified priority tasks into the scheduling list, and generates a scheduling allocation list;

[0060] The resource locking module extracts the resource requirements of priority tasks in chemical energy storage based on the scheduling allocation list, sets the matching resources to an exclusive locking state, sets locking rules and records the resource allocation of each task, updates the status of locked resources in real time, and generates a resource locking status table;

[0061] The thermal runaway monitoring module extracts the temperature and current parameters of the energy storage system nodes based on the resource lock status table, detects the nodes with temperature exceeding the limit in the energy storage system, records the node's over-limit status, lists the nodes that meet the conditions in the abnormal risk list, records the abnormal frequency of the nodes, and generates a thermal runaway risk list;

[0062] Based on the thermal runaway risk list, the abnormal control module analyzes the abnormal frequency of nodes in chemical energy storage and the cumulative status of thermal runaway risks, marks the risk level of abnormal nodes, implements risk intervention measures for risk nodes, adjusts the operating mode of the energy storage system, and generates a thermal runaway graded control plan.

[0063] The task resource occupancy list includes task urgency, resource occupancy ranking, and task type records. The priority task weight table includes priority identification, weight assignment, and task ranking records. The scheduling allocation list includes resource matching status, resource verification results, and priority task screening. The resource locking status table includes resource locking operations, locking rule settings, and resource allocation records. The thermal runaway risk list includes node temperature monitoring, current parameter analysis, and abnormal node records. The thermal runaway hierarchical control plan includes risk level marking, implementation of intervention measures, and operation mode adjustment.

[0064] Specifically, if Figure 2 , 3 As shown, the task classification module includes:

[0065] The node parameter arrangement submodule extracts the urgency values ​​of tasks one by one based on the task requirements in chemical energy storage, classifies the tasks by analyzing the values ​​in the urgency field, and arranges the urgency of each task. The execution process of generating the task urgency parameter table is as follows;

[0066] Based on the task requirements in chemical energy storage, the urgency values ​​of the tasks are extracted item by item. The urgency field in the task information is read to parse its value, convert it into a continuous value set, and map and classify it according to the task classification. To ensure data accuracy, the values ​​contained in the task field are normalized, abnormal values ​​or null value data are removed, and preprocessing is performed on multiple types of tasks. The task classification process is sorted from high to low according to the urgency to construct a priority list, which contains the unique identifier of each task, task category and urgency value. After the data table is generated, the priority of each type of task is calculated and output in the form of a list, including the level division and sorting position of the urgency. The final generated task urgency parameter table will serve as the basis for task priority judgment.

[0067] The task screening submodule screens the urgency values ​​of the tasks in order based on the task urgency parameter table, determines the tasks with urgency values, marks the task types, screens out low-priority tasks one by one, identifies the task types, and generates the task priority list. The execution process is as follows;

[0068] By analyzing the task urgency parameter table, tasks are sorted item by item according to the urgency value, high-priority tasks are screened, low-priority tasks are gradually eliminated, and differentiated tasks are classified after the task types are identified to ensure that key information is not missed during the task screening process. The screening process quantitatively compares the urgency of differentiated task types and generates screening rules based on actual needs, including task priority setting, task type identification and hierarchical arrangement, and uses this to determine whether to include it in the task list. The screened low-priority tasks are removed from the output list, and the screening results are arranged by priority to generate a task priority list.

[0069] The resource occupancy record submodule is based on the task priority list, sorted in order according to the resource requirement values, allocated resources according to the task requirements, recorded the resource occupancy values ​​and task order of the tasks, and arranged them in order of priority. The execution process of generating the task resource occupancy list is as follows;

[0070] In the above content, according to the resource requirement values ​​in the task priority list, the task resource requirements are sorted and allocated in sequence, and the resource occupancy value and sorting position of each task are calculated. The resource allocation is based on the formula:

[0071]

[0072] Calculate the total resource usage of the task, where: Represents the total resource usage value, For the The priority value of the task, is the urgency value of the task, is the number of tasks;

[0073] Suppose there are 3 tasks, and their urgency values ​​( ) and the priority value ( )as follows:

[0074] Task 1: , ;

[0075] Task 2: , ;

[0076] Task 3: , ;

[0077] Substitute the formula to calculate the total resource usage value :

[0078]

[0079] Calculate each term:

[0080] For Task 1: ;

[0081] For Task 2: ;

[0082] For Task 3: ;

[0083] Calculate the total resource usage :

[0084]

[0085] The result shows that the total resource occupancy value of the task is 8.42, indicating the total resource occupancy requirement under the current priority and urgency.

[0086] Specifically, if Figure 2 , 4 As shown, the priority scoring module includes:

[0087] The urgency quantification submodule reads the priority identification of each task based on the task resource occupancy list, performs numerical conversion, converts the content of the priority identification into numerical form, processes and records the priority identification information of the task item by item, and generates the task urgency table. The execution process is as follows;

[0088] Based on the task resource occupancy list, read the priority identification of each task, convert its content into numerical value, and parse and convert different types of priority identification into numerical form in turn to facilitate subsequent calculation and processing. During the conversion process, each numerical data is stored in the system database one by one to form a unified numerical format. The priority identification information of all tasks is recorded numerically, and a task urgency table containing the priority value of each task is generated to provide a data basis for subsequent modules.

[0089] The resource weight assignment submodule extracts the resource consumption value of each task based on the task urgency table, and performs weighted processing on the resource consumption. It combines the task urgency with the resource consumption, assigns corresponding weight values ​​to each task, and generates the execution process of the weighted task list as follows;

[0090] Based on the task urgency table, the resource consumption value of each task is extracted, the data is read in sequence, and the read resource consumption data is weighted item by item. The urgency value is combined with the resource consumption, and the weight value of each task is calculated according to the weighted rules. In the weighting process of each task, the urgency and resource consumption values ​​of the task are weighted and synthesized through the weight calculation formula to generate a weighted task list containing tasks, urgency, resource consumption and corresponding weight values. The weight value of each task in the table is recorded for subsequent module calls.

[0091] The dynamic priority sorting submodule is based on the weighted task list, which performs weighted calculation on the urgency and resource consumption weight of the task, arranges the weighted values ​​of the tasks in sequence, sorts the tasks according to the weighted values, records the priority values ​​of the sorted tasks, and generates the priority task weight table. The execution process is as follows;

[0092] The dynamic prioritization submodule performs weighted calculations on the urgency and resource consumption weights of tasks based on the weighted task list, according to the formula:

[0093]

[0094] Calculate the weighted priority value of each task and sort it, where: Indicates the task The weighted priority value of For the task The urgency value of is the resource consumption weight;

[0095] Suppose there are 3 tasks, and their urgency values ​​( ) and resource consumption weight ( )as follows:

[0096] Task 1: , ;

[0097] Task 2: , ;

[0098] Task 3: , ;

[0099] Substitute the formula to calculate the weighted priority value of each task :

[0100]

[0101]

[0102]

[0103] Sorting calculation results: Sorted according to weighted priority values, the ranking results are task 2 (18), task 1 (8), task 3 (7.5);

[0104] The results show that Task 2 has the highest weighted priority value of 18, and is ranked highest in priority, followed by Task 1 and Task 3.

[0105] Specifically, if Figure 2 , 5 As shown, the task allocation module includes:

[0106] The resource status extraction submodule reads the resource type and current occupancy value of each task item by item based on the priority task weight table, scans the available resources in the energy storage system resource pool, matches the resource type, resource demand and remaining quantity, records the current value of the resource, summarizes and organizes it, and obtains the execution process of the current resource status table as follows;

[0107] Based on the priority task weight table, the resource type and current occupancy value of the task are read one by one. By obtaining the task resource occupancy status one by one, it is ensured that the reading of resource type and quantity is synchronized with the resource value in the energy storage system resource pool. During the reading process, the resource type field of each task is extracted to match resources in a structured manner, and the energy storage system resource pool is scanned again to obtain the current available resource quantity, and the remaining resource value of the energy storage pool is compared according to the task requirements. In the case where the remaining resource amount is lower than the task demand, a low priority mark is set for recording to further ensure the accurate matching of resource type, demand and remaining amount. The system records the updated resource value, and dynamically adjusts the resource occupancy and release status. It summarizes and analyzes the task resource type and remaining resource status, and finally outputs the current resource status table.

[0108] The resource demand matching submodule retrieves the task resource demand based on the current resource status table, and eliminates the tasks with insufficient resources by comparing the demand with the current available quantity item by item, and retains the tasks that meet the resource demand in the screening list. The execution process of obtaining the list of qualified tasks is as follows;

[0109] Based on the current resource status table, the required amount of resources for each task is gradually retrieved, and the resource demand is compared with the remaining amount of resources in the resource status table. In the case where the task demand exceeds the available resources, the tasks that do not meet the demand are eliminated one by one, and the matching logic is further optimized through the resource type field. On the basis of eliminating invalid tasks, the task records that meet the resource demand are retained, and the filter list is updated to ensure that the tasks in the filter list meet the resource demand standards, and finally a list of qualified tasks is obtained.

[0110] The resource priority allocation submodule arranges the task list according to the priority based on the qualified task list, adds the task to the scheduling list according to the priority, records the type and quantity of resources allocated to the task, updates the task order in the scheduling list, and obtains the execution process of the scheduling allocation list as follows;

[0111] Prioritize your to-do list according to the formula:

[0112]

[0113] Calculate the priority scheduling task ranking score, where, represents the task prioritization score, Represents the priority weight of the task, represents the resource requirement coefficient of the task;

[0114] According to the numerical calculation of each parameter based on the actual task priority and resource demand, the priority weight of task A is set to 3 and the resource demand coefficient is set to 1.5. Then, we substitute the formula to obtain: ,This score indicates that the priority score of task A in resource scheduling is 4.5, and a higher score means a higher priority for resource allocation.

[0115] Specifically, if Figure 2 , 6 As shown, the resource locking module includes:

[0116] The resource requirement analysis submodule reads the resource requirements of the priority tasks based on the scheduling allocation list, identifies the resource types, verifies the resource requirement values ​​of each task, and generates the task resource requirement table by recording the allocatable values ​​of each resource and the task requirements. The execution process is as follows;

[0117] Based on the scheduling allocation list, read the resource requirements of the priority tasks, obtain the resource type data of the tasks item by item and analyze them, and obtain the resource allocation information of all priority tasks in the current scheduling allocation list by judging the specific type and value of the resource requirements corresponding to each task. When reading the resource requirements, the system determines the specific type of task resources through the resource type field to ensure the accuracy of the resource type identification process, and further verifies the resource requirement values ​​of each task item by item, constructs the task resource requirement table into a task-by-task resource requirement list, ensures that the resource requirement allocation requirements are met, and finally outputs the resource requirement table as a clear display of the current task resource requirements.

[0118] The exclusive lock submodule sets the exclusive status of matching resources based on the task resource requirement table, marks the resources as exclusive lock by calling the priority of the task, arranges the allocation records according to the usage time and priority of the task, and locks and stores the resource allocation of each task according to the resource requirements and usage time limit of the task in the requirement table. The execution process of generating the locked resource list is as follows;

[0119] Based on the task resource requirement table, the resource requirement and usage time information of each task are read, and the resource allocation of the task is exclusively locked. By introducing the priority parameter, each matching resource is marked as an exclusive state, and the exclusivity of the resource allocation lock is ensured. In the process of matching resources, resources are allocated to each task one by one. According to the task demand, demand time window and priority order, the resource requirements that meet the priority order are retained in the allocation record, and further exclusive locking operations are provided for the resource allocation of subsequent tasks. The resource requirements, usage time limits, etc. of the tasks in the task requirement table are locked and recorded, and the task resource allocation list is updated in sequence to obtain a locked resource list.

[0120] The status update submodule uses an adaptive resource allocation optimization algorithm based on the locked resource list to monitor the usage status of the allocated resources in real time, check and update the current status of each resource, update the availability of the resource pool by recording the resource locking time and usage status, and classify the resource locking information into the status table. The current resource locking status is compiled and the execution process of generating the resource locking status table is as follows;

[0121] The formula of the adaptive resource allocation optimization algorithm is as follows:

[0122]

[0123] Calculate the real-time lock value of the resource pool and generate a resource lock status table, where: Indicates the real-time lock value of the resource pool. Real-time data representing the length of time a resource has been locked. Statistics representing the current utilization of resources, Indicates the total number of available resources in the current resource pool. Represents the capacity of the resource pool. Represents the locking weight coefficient of a single resource, is the adjustment coefficient;

[0124] Detailed explanation of the formula and the process of formula calculation and derivation:

[0125] parameter : The actual data of resource lock duration, which is recorded by the resource monitoring system in real time since the resource was allocated. It is calculated by the timestamp difference. The lock duration is set to 4 hours. This value is obtained by collecting and calculating data from the server's resource allocation log. The value is 4 hours. This value is converted to 240 minutes to be consistent with the time unit;

[0126] parameter : Statistical data of the current resource usage rate, which is collected and calculated by the resource monitoring module through the load balancing system. The CPU and memory usage rates of the current resources are set to 60% and 50% respectively. The current resource usage rate is obtained by combining the usage rates of different dimensions through the weighted average method. The calculation method is: ,The current resource utilization rate is calculated through load monitoring data and the value is 0.54. This data is processed by continuous data collection and averaging to ensure real-time and accuracy;

[0127] parameter : The real-time total number of available resources in the resource pool is counted in real time by the resource management system. The actual available resources in the current resource pool are set to 120. The data is collected from the system's real-time monitoring of the number of resources and is obtained through the analysis of actual resource status data;

[0128] parameter : The maximum capacity of the resource pool represents the total upper limit of the system resource pool. The maximum resource pool capacity allowed by the system is set to 150. It is determined by the system configuration parameters and does not need to be changed. The value is 150;

[0129] parameter : The locking weight coefficient of a single resource. This coefficient is quantified based on the importance and priority of the resource. To ensure the flexibility and accuracy of the resource locking strategy, the coefficient is set to 1.2. Through standardized adjustment of the weight coefficient, the locking occupancy balance of a single resource is guaranteed;

[0130] parameter : Adjustment coefficient, which adjusts the impact of lock duration, resource utilization, available number of resource pools and lock weight respectively;

[0131] : Lock duration weight, set to 0.5, is used to adjust the impact of the lock time and control the proportion of the time length in the overall resource lock status;

[0132] : Utilization weight, set to 0.7, adjusts the impact of resource utilization to make the current load state more significant in the calculation;

[0133] : The weight of available resources is set to 0.4, which adjusts the impact of available quantity on the locked state and reduces the impact of resource quantity fluctuations with a smaller weight;

[0134] : The adjustment coefficient of the lock weight is set to 1.5 to balance the final strength of the lock effect and control the output influence of the lock condition on the overall state through the weight;

[0135] Substitute the above values ​​into the formula for calculation:

[0136] The internal calculation process of the formula:

[0137] calculate ;

[0138] calculate ;

[0139] calculate ;

[0140] Calculate the sum of the numerator parts ;

[0141] Numerator and denominator operations:

[0142] Denominator calculation ;

[0143] Calculate the final result ;

[0144] The result shows that the real-time lock value of the resource pool is 94.33, which indicates the degree of resource lock under the current resource status. This result is used in resource pool management to monitor resource allocation and usage efficiency in real time and help make decisions on resource unlocking or allocation adjustment.

[0145] Specifically, if Figure 2 , 7 As shown, the thermal runaway monitoring module includes:

[0146] The node parameter sorting submodule extracts the temperature and current values ​​of the nodes based on the resource locking state table, and identifies the value type of the nodes and classifies and sorts them according to the influencing factors of chemical energy storage and air pressure instability, arranges the temperature and current data in node order, and generates the node parameter table. The execution process is as follows;

[0147] Based on the resource lock status table, the temperature and current numerical data of each node are first extracted to obtain the real-time parameters of each node. These data are collected from various monitoring nodes of the energy storage device and classified according to different types of energy storage devices. In order to cope with changes in environmental factors, it is necessary to consider external influencing factors such as chemical energy storage and unstable air pressure, especially the impact of these factors on battery performance and current and voltage fluctuations. When sorting the temperature and current data, it is first necessary to identify the value type of each node, that is, to determine whether these values ​​are within the normal operating range or abnormal values. By establishing a standardized parameter range, it is determined whether the value of each node is in the normal working range, and the temperature and current data are arranged in the order of nodes to form a complete node parameter table. The temperature and current data of each node will be organized in the form of a time series for subsequent analysis and monitoring. When classifying and sorting the parameters of the node, representative characteristic values ​​are extracted according to the specific functions, working status and environmental conditions of the equipment, and calibrated in combination with the historical data of the node. The generated node parameter table not only provides data support for subsequent state analysis, but also provides a clear parameter benchmark for anomaly detection, helping to identify potential risks.

[0148] The node detection submodule checks the temperature value of the node based on the node parameter table, determines whether the temperature value exceeds the safety limit, marks the abnormal state of the node that exceeds the limit, and records the temperature deviation value. Referring to the impact of charging and discharging on the temperature during battery maintenance, the real-time temperature and deviation value of the node are checked, and the nodes that exceed the limit are included in the abnormal list and the parameters that exceed the limit are sorted. The execution process of generating the abnormal node list is as follows;

[0149] Based on the node parameter table, first check the temperature value of the node, check the real-time temperature data of each node with the set safety limit, and determine whether it exceeds the safety limit. Temperature monitoring is very critical, because excessive temperature may cause equipment damage or performance degradation, or even safety hazards. During the detection process, the temperature value of each node needs to be compared with the pre-set safety threshold, which can be set based on the historical operation data of the equipment, industry standards and actual test results. For nodes that exceed the limit, they are marked as abnormal and their deviation values ​​are recorded. It is necessary to check the impact of charging and discharging on the temperature during battery maintenance, that is, during the charging and discharging process, the temperature may fluctuate to varying degrees, and check whether these fluctuations are within the normal range. When the temperature exceeds the limit, further check the deviation value of the node's real-time temperature from the previous period, calculate the size of the temperature deviation, and record the degree of abnormality. Pay special attention to those nodes whose temperature rises rapidly or drops suddenly. These situations are usually abnormalities caused by equipment failure or external factors. The nodes that exceed the limit will be included in the abnormal list, and the related parameters such as the temperature and current that exceed the limit will be sorted out. The generated abnormal node list will contain detailed information of each node, such as node location, degree of over-limit, time of abnormality, etc., to provide data support for subsequent fault diagnosis and maintenance. At the same time, these abnormal data can also be used to optimize equipment performance, ensuring that energy storage equipment always remains within a safe temperature range during operation, thereby effectively preventing safety accidents.

[0150] The risk recording submodule uses time series analysis technology based on the abnormal node list to record the abnormal frequency of the node, and calculates the thermal runaway risk caused by chemical energy storage and unstable gas pressure in combination with the experience value of AI. The number of abnormalities is counted by node and time sequence, and each time point of exceeding the limit is archived. The accumulated abnormal data of each node is summarized to generate the execution process of the thermal runaway risk list as follows;

[0151] The formula for the time series analysis technique is as follows:

[0152] Calculate the thermal runaway risk assessment value of the node and generate a thermal runaway risk list, where: represents the thermal runaway risk assessment value, Represents the weight coefficient of the node where the abnormal event occurs, Indicates the number of abnormalities of the node. represents the corresponding time interval of the nodes in the time series, is the adjustment coefficient used to smooth abnormal fluctuations;

[0153] Detailed explanation of the formula and the process of formula calculation and derivation:

[0154] Weight coefficient :The weight coefficient is used to adjust the importance of differentiated nodes. It depends on the abnormal level and impact range of the node. It is calculated through the historical data of abnormal events and the relative importance of each node in the system. For example, node 1 has a high frequency of abnormalities and its abnormal impact area is large, so it is given a higher weight value. The weight coefficient is set to be obtained from the node importance table, where the weight of node 1 is 2.5, the weight of node 2 is 1.8, and the weight of node 3 is 1.2;

[0155] Abnormal number : reflects the number of abnormalities that occur during the monitoring period. This value is directly obtained from the log data recorded by the abnormality monitoring system. The unit is the number of events. For example, the number of abnormalities for node 1 is 12, the number of abnormalities for node 2 is 8, and the number of abnormalities for node 3 is 5.

[0156] Time interval : Indicates the time interval of node anomaly records. It calculates the difference between the time when the current node anomaly occurs and the time when the previous anomaly occurs, and is used to balance the relationship between the frequency of anomalies and time changes. This data is recorded through the timestamp of the monitoring system. The time interval for node 1 is set to 2 days, the time interval for node 2 is set to 5 days, and the time interval for node 3 is set to 7 days.

[0157] Smoothing adjustment factor : It is used to adjust the smoothness of abnormal fluctuations to avoid the impact of extreme fluctuations of abnormal events on the results. This value is determined by analyzing the abnormal fluctuation amplitude of historical data and is set within a smaller range to reduce the extreme impact. The smoothing adjustment coefficient is set to 0.5 and is obtained through smoothing calculation;

[0158] Substitute the above parameters into the formula to calculate the thermal runaway risk assessment value of each node, and substitute the specific parameters for calculation:

[0159]

[0160] Step-by-step calculation:

[0161] Node 1 calculates:

[0162]

[0163] Node 2 calculates:

[0164]

[0165] Node 3 calculation:

[0166]

[0167] Add the results of the above nodes:

[0168]

[0169] Denominator calculation:

[0170]

[0171] Final calculation evaluation value :

[0172]

[0173] The result shows that the thermal runaway risk assessment value is 4.964, which is used to measure the thermal runaway risk level of the entire node set. The higher the value, the greater the risk of thermal runaway in the overall system. It is recommended to use this value to sort the risks of each node and determine the priority nodes to avoid the spread of system abnormalities.

[0174] Specifically, if Figure 2 , 8 As shown, the abnormal control module includes:

[0175] Based on the thermal runaway risk list, the risk assessment submodule checks the abnormal frequency records of the nodes one by one, calculates the cumulative risk value of each node, classifies the nodes according to the risk size, puts the risk nodes in the priority list, and marks the risk level of each node. The node number corresponds to the risk level, and the execution process of generating the node risk level table is as follows;

[0176] Based on the thermal runaway risk list, read the abnormal frequency records of each node one by one, determine the risk status of the node, convert the abnormal frequency values ​​of the node into risk accumulation values, divide the risk levels according to the abnormal accumulation situation and risk values, and further divide the nodes into high-risk, medium-risk and low-risk categories. By reading the abnormal frequency of the nodes one by one, the nodes that exceed the set risk value are included in the priority processing list, the risk level is marked and the node number is associated, the nodes of each risk level are classified in a table, the node number and the corresponding risk level are organized in a table, and the node risk level table is output to ensure a clear display of the risk situation of each node.

[0177] The intervention measures submodule locates risk nodes based on the node risk level table, formulates intervention measures such as cooling and load reduction according to the risk level, and applies them to the corresponding nodes, monitors the implementation status of each intervention measure, and records and organizes the node status of the implemented measures. The execution process of generating risk intervention records is as follows;

[0178] Based on the node risk level table, read the level of each risk node in turn, determine the intervention measures corresponding to each node through risk level analysis, divide the specific steps of cooling measures and load adjustment according to the risk level, gradually apply cooling and load reduction measures to each node, monitor the implementation of intervention measures and record the node status in real time, mark the executed intervention measures as completed, record and statistically organize the node's temperature, load and other indicators, summarize all execution status in the risk intervention record, and generate a risk intervention record table.

[0179] The mode adjustment submodule monitors the operating status of the node after the intervention based on the risk intervention record, collects the value changes of the node after the intervention, optimizes the operating mode, adjusts the parameters of the energy storage system according to the node feedback, and applies it to the node environment, summarizes the adjusted data, and generates the execution process of the thermal runaway hierarchical control scheme as follows;

[0180] Monitor the running status of the node after intervention, according to the formula:

[0181]

[0182] The thermal runaway risk classification control parameters of the computing node are: represents the node thermal runaway control parameter, represents the temperature change after intervention, represents the power load of the node, Indicates intervention energy expenditure;

[0183] In the node operation status monitoring, the control parameters are calculated based on the parameter changes after intervention. In the example, several predicted temperature changes Degrees, power load Watts, intervention energy consumption Joules, then calculate The result value represents the thermal runaway control level of the node. The lower the value, the more significant the temperature control effect of the node after intervention. This calculated value in the thermal runaway hierarchical control scheme is used for temperature control optimization of the node environment.

[0184] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A thermal runaway safety monitoring system for electrochemical energy storage systems in a distribution network, characterized in that: The system comprises: The task classification module extracts task urgency parameters based on the task requirements in chemical energy storage, filters task values, arranges tasks by resource usage, records task types and priorities, and generates a task resource usage list; The priority scoring module, based on the task resource occupancy list, calls the priority identifier to quantify the task urgency, extracts the resource consumption index to assign a weight value, weights and sorts the task urgency and resource weight, and generates a priority task weight table; The task allocation module extracts the current resource status based on the priority task weight table, matches the priority task resource demand, verifies the resource pool in real time, screens out tasks with insufficient resources, and generates a scheduling allocation list; The resource locking module extracts the resource requirements of the priority tasks based on the scheduling allocation list, locks the matching resources, sets locking rules and records the task resource allocation, and generates a resource locking status table; Based on the resource lock status table, the thermal runaway monitoring module extracts the current fluctuation analysis of the chemical energy storage node, identifies the thermal runaway risk caused by unstable air pressure, analyzes the thermal runaway risk caused by the charging and discharging behavior during battery maintenance, and predicts the potential risk level of differentiated nodes in combination with AI experience values. It monitors the temperature, current fluctuation and air pressure changes of the chemical energy storage node in real time, identifies and marks abnormal fluctuations, conducts risk assessment and lists risk nodes, and generates a thermal runaway risk list; The abnormal control module analyzes the node abnormality frequency and the thermal runaway cumulative status based on the thermal runaway risk list, marks the risk level of the abnormal node, implements risk intervention, and generates a thermal runaway hierarchical control plan; The resource locking module includes: The resource requirement analysis submodule reads the resource requirements of the priority tasks based on the scheduling allocation list, identifies the resource types, verifies the resource requirement values ​​of each task, and generates a task resource requirement table by recording the allocatable values ​​of each resource and the task requirements; The exclusive locking submodule sets the exclusive state of the matching resources based on the task resource requirement table, marks the resources as exclusive locks by calling the priority of the tasks, arranges the allocation records according to the usage duration and priority of the tasks, and locks and stores the resource allocation of each task according to the resource requirements and usage time limits of the tasks in the requirement table, and generates a locked resource list; The status update submodule is based on the locked resource list and adopts an adaptive resource allocation optimization algorithm to monitor the usage status of the allocated resources in real time, check and update the current status of each resource, update the availability of the resource pool by recording the locking time and usage status of the resource, classify the resource locking information into the status table, compile the current resource locking status, and generate a resource locking status table.

2. The thermal runaway safety monitoring system for electrochemical energy storage system in distribution network according to claim 1 is characterized by: The task resource occupancy list includes task urgency, resource occupancy ranking, and task type records; the priority task weight table includes priority identification, weight assignment, and task ranking records; the scheduling allocation list includes resource matching status, resource verification results, and priority task screening; the resource locking status table includes resource locking operations, locking rule settings, and resource allocation records; the thermal runaway risk list includes node temperature monitoring, current parameter analysis, and abnormal node records; and the thermal runaway hierarchical control scheme includes risk level marking, implementation of intervention measures, and operation mode adjustment.

3. The thermal runaway safety monitoring system for electrochemical energy storage system in distribution network according to claim 1 is characterized by: The task classification module includes: The node parameter arrangement submodule extracts the urgency values ​​of tasks one by one based on the task requirements in chemical energy storage, classifies the tasks by analyzing the values ​​in the urgency field, and arranges the urgency of each task to generate a task urgency parameter table; The task screening submodule screens the urgency values ​​of the tasks in order based on the task urgency parameter table, determines the tasks with urgency values, marks the task types, screens out low-priority tasks one by one, identifies the task types, and generates a task priority list; The resource occupancy recording submodule is based on the task priority list, sorts the tasks in order according to the resource requirement values, allocates resources according to the task requirements, records the resource occupancy values ​​and task order of the tasks, arranges them in order of priority, and generates a task resource occupancy list.

4. The thermal runaway safety monitoring system for electrochemical energy storage system in distribution network according to claim 1, characterized in that: The priority scoring module includes: The urgency quantification submodule reads the priority identification of each task based on the task resource occupation list, performs numerical conversion, converts the content of the priority identification into numerical form, processes and records the priority identification information of the task item by item, and generates a task urgency table; The resource weight assignment submodule extracts the resource consumption value of each task based on the task urgency table, performs weighted processing on the resource consumption, combines the task urgency with the resource consumption, assigns corresponding weight values ​​item by item, and generates a weighted task list; The dynamic priority sorting submodule performs weighted calculation on the urgency and resource consumption weight of the task based on the weighted task list, arranges the weighted values ​​of the tasks in sequence, sorts the tasks according to the weighted values, records the priority values ​​of the sorted tasks, and generates a priority task weight table.

5. The thermal runaway safety monitoring system for electrochemical energy storage system in distribution network according to claim 1, characterized in that: The task allocation module includes: The resource status extraction submodule reads the resource type and current occupied value of each task item by item based on the priority task weight table, matches the resource type, resource demand and remaining amount by scanning the available resource quantity of the energy storage system resource pool, records the current value of the resource, summarizes and organizes it, and obtains the current resource status table; The resource demand matching submodule retrieves the task resource demand based on the current resource status table, and eliminates the tasks with insufficient resources by comparing the demand with the current available quantity item by item, and retains the tasks that meet the resource demand in the screening list, thereby obtaining a list of qualified tasks; The resource priority allocation submodule arranges the task list according to priority based on the qualified task list, adds the tasks to the scheduling list according to the priority, records the type and quantity of resources allocated to the tasks, updates the task order in the scheduling list, and obtains the scheduling allocation list.

6. The thermal runaway safety monitoring system for electrochemical energy storage system in distribution network according to claim 1, characterized in that: The formula of the adaptive resource allocation optimization algorithm is as follows: Calculate the real-time lock value of the resource pool and generate a resource lock status table, where: Indicates the real-time lock value of the resource pool. Real-time data representing the length of time a resource has been locked. Statistics representing the current utilization of resources, Indicates the total number of available resources in the current resource pool. Represents the capacity of the resource pool. Represents the locking weight coefficient of a single resource, is the adjustment coefficient.

7. The thermal runaway safety monitoring system for electrochemical energy storage system in distribution network according to claim 1, characterized in that: The thermal runaway monitoring module comprises: The node parameter sorting submodule extracts the temperature and current values ​​of the nodes based on the resource locking state table, and identifies the value types of the nodes and sorts them according to the influencing factors of chemical energy storage and unstable air pressure, arranges the temperature and current data in node order, and generates a node parameter table; The node detection submodule checks the temperature value of the node based on the node parameter table, determines whether the temperature value exceeds the safety limit, marks the abnormal state of the node that exceeds the limit, and records the temperature deviation value. With reference to the impact of charging and discharging on the temperature during battery maintenance, the real-time temperature and deviation value of the node are checked, and the node that exceeds the limit is included in the abnormal list and the excessive parameters are sorted to generate an abnormal node list; Based on the abnormal node list, the risk recording submodule uses time series analysis technology to record the abnormal frequency of the nodes, and calculates the thermal runaway risk caused by chemical energy storage and unstable air pressure based on the AI's experience value. The number of abnormalities is counted by node and time sequence, and each time point of exceeding the limit is archived. The accumulated abnormal data of each node is summarized to generate a thermal runaway risk list.

8. The thermal runaway safety monitoring system for electrochemical energy storage system in distribution network according to claim 7, characterized in that: The formula for the time series analysis technique is as follows: Calculate the thermal runaway risk assessment value of the node and generate a thermal runaway risk list, where: represents the thermal runaway risk assessment value, Represents the weight coefficient of the node where the abnormal event occurs, Indicates the number of abnormalities of the node. represents the corresponding time interval of the nodes in the time series, An adjustment factor used to smooth abnormal fluctuations.

9. The thermal runaway safety monitoring system for electrochemical energy storage system in distribution network according to claim 1, characterized in that: The abnormality control module comprises: Based on the thermal runaway risk list, the risk assessment submodule checks the abnormal frequency records of the nodes one by one, calculates the cumulative risk value of each node, classifies the nodes according to the risk size, puts the risk nodes in the priority processing list, and marks the risk level of each node, corresponds the node number to the risk level, and generates a node risk level table; The intervention measures submodule locates risk nodes based on the node risk level table, formulates intervention measures of cooling and load reduction according to the risk level, applies them to the corresponding nodes, monitors the implementation status of each intervention measure, records and organizes the node status of the executed measures, and generates risk intervention records; Based on the risk intervention record, the mode adjustment submodule monitors the operating status of the node after the intervention, collects the numerical changes of the node after the intervention, optimizes the operating mode, adjusts the parameters of the energy storage system according to the node feedback, and applies it to the node environment, summarizes the adjusted data, and generates a thermal runaway hierarchical control plan.

Images