A prefabricated charging integrated device and its control method

Abstract

This invention relates to the field of prefabricated charging technology, and more particularly to a prefabricated charging integrated device and its control method. The method includes the following steps: acquiring multi-source operational data based on a data acquisition unit installed within the prefabricated compartment; when any data item in the multi-source operational data is abnormal, the charging compartment controller triggers the charging equipment to execute a power-off interlock, and transmits the power-off interlock signal to the multi-source data decision fusion stage; performing integrated analysis on the multi-source operational data and the power-off interlock signal, and generating a decision result including fault type and risk level through the multi-source data decision fusion stage; based on the risk level and fault type in the decision result, the charging compartment controller controls the equipment installed within the compartment as an execution carrier to perform corresponding graded handling operations, and receives execution status data fed back by each execution carrier; achieving rapid response and safe handling of abnormal states within the charging compartment, and ensuring traceability through full-process data collection.

Description

Technical Field

[0001] This invention relates to the field of prefabricated charging technology, and in particular to a prefabricated charging integrated device and its control method. Background Technology

[0002] Traditional charging equipment often employs a decentralized layout and manual management model, resulting in limited monitoring capabilities, slow response times, and untimely handling of anomalies. This can easily increase charging safety risks, such as equipment overload, abnormal temperatures, battery malfunctions, and fire hazards. Furthermore, it is difficult to obtain and trace complete charging operation records, making it impossible to track and assign responsibility for abnormal events throughout the entire process. While existing centralized management systems achieve remote monitoring, they typically rely on a single data source and lack the ability to perform multi-dimensional data analysis on environmental information, battery status, and the charging process, making it difficult to quickly identify and categorize abnormal situations within the charging compartment. Real-time acquisition, processing, and correlation analysis of multi-source data present technical challenges; differences in time series, formats, and accuracy between different data sources can affect the reliability of decisions, leading to omissions or delays in anomaly assessment. Summary of the Invention

[0003] Therefore, it is necessary to provide a prefabricated charging integrated device and its control method to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a prefabricated charging integrated control method includes:

[0005] Step S1: Based on the data acquisition unit set in the prefabricated cabin, multi-source operation data is acquired. When any data item in the multi-source operation data is abnormal, the abnormal signal is uploaded to the charging cabin controller. The controller triggers the charging equipment to perform power-off interlock and transmits the power-off interlock signal to the multi-source data decision fusion stage.

[0006] Step S2: Perform integrated analysis on multi-source operating data and power failure interlocking signals, and generate decision results including fault type and risk level through multi-source data decision fusion.

[0007] Step S3: Based on the risk level and fault type in the decision results, control the equipment installed in the cabin as the execution carrier to perform the corresponding graded disposal operation, and receive the execution status data fed back by each execution carrier;

[0008] Step S4: Collect and record multi-source operational data, decision results, and execution status data to form a full-process operational dataset to achieve process traceability.

[0009] Optionally, a prefabricated charging integration device includes:

[0010] Prefabricated cabin, and charging equipment, fire-fighting equipment, ventilation equipment, lighting equipment, video surveillance equipment and various sensors integrated into the cabin;

[0011] Charging equipment, fire-fighting equipment, ventilation equipment, and various sensors serve as data acquisition units for multi-source data decision fusion and as execution carriers for tiered disposal operations.

[0012] The prefabricated cabin is equipped with iris recognition, facial recognition, and voiceprint recognition components for collecting biometric information.

[0013] The charging compartment controller is used to centrally manage various integrated devices within the prefabricated compartment, including charging equipment, fire-fighting equipment, ventilation equipment, lighting equipment, video surveillance equipment, and various sensors.

[0014] The beneficial effects of this invention are as follows: Multi-source operational data is acquired in real time through a data acquisition unit within the prefabricated cabin. When any data item shows an anomaly, the charging equipment is immediately triggered to execute a power-off interlock, ensuring the safety of the electrical equipment within the cabin. Simultaneously, the operational data and power-off interlock signals are integrated and analyzed through a multi-source data decision fusion process, generating decision results that include fault type and risk level, enabling scientific judgment of abnormal states. Based on the decision results, graded handling operations are performed on the equipment within the cabin, and the operational status data of the execution carrier is continuously received, allowing for graded control and dynamic management of various anomalies. Throughout the process, multi-source operational data, decision results, and execution status data are collected and recorded to form a full-process operational dataset, achieving safe and controllable operation and traceability throughout the charging cabin operation, providing comprehensive data support and assurance for cabin safety management and fault handling. Attached Figure Description

[0015] Figure 1 A schematic diagram of the steps in a prefabricated charging integrated control method;

[0016] Figure 2 This is a schematic diagram of the structural distribution of fire protection equipment;

[0017] Figure 3 This is a picture of the actual ventilation equipment.

[0018] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0020] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0021] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0022] To achieve the above objectives, please refer to Figures 1 to 3 A prefabricated charging integrated control method includes the following steps:

[0023] Step S1: Based on the data acquisition unit set in the prefabricated cabin, multi-source operation data is acquired. When any data item in the multi-source operation data is abnormal, the abnormal signal is uploaded to the charging cabin controller. The controller triggers the charging equipment to perform power-off interlock and transmits the power-off interlock signal to the multi-source data decision fusion stage.

[0024] Step S2: Perform integrated analysis on multi-source operating data and power failure interlocking signals, and generate decision results including fault type and risk level through multi-source data decision fusion.

[0025] Step S3: Based on the risk level and fault type in the decision results, control the equipment installed in the cabin as the execution carrier to perform the corresponding graded disposal operation, and receive the execution status data fed back by each execution carrier;

[0026] Step S4: Collect and record multi-source operational data, decision results, and execution status data to form a full-process operational dataset to achieve process traceability.

[0027] In one embodiment, a data acquisition unit deployed within the prefabricated cabin continuously acquires multi-source operational data from within the cabin. During the acquisition process, the data acquisition unit maintains real-time updates for each operational data item. When an abnormal state is detected during the update of any operational data item, the data acquisition unit uploads the abnormal signal to the charging cabin controller. The charging cabin controller then issues a power-off interlock command to the charging equipment and generates a power-off interlock signal, causing the charging equipment to enter a power-off interlock state. Simultaneously, after the power-off interlock is triggered, a corresponding power-off interlock signal is generated and synchronously transmitted along with the multi-source operational data to the multi-source data decision fusion stage.

[0028] In the multi-source data decision fusion stage, the charging compartment controller integrates and analyzes the incoming multi-source operational data and power failure interlock signals. During the analysis, data from different sources are processed sequentially, and after integration, a corresponding decision result is generated based on the integrated analysis results. The decision result includes the fault type and risk level determined by the integrated analysis.

[0029] Based on the risk level and fault type in the decision-making results, equipment for performing tiered response operations is selected from the pre-configured equipment within the cabin as the execution vehicles. Subsequently, tiered response operation control commands matching the risk level are issued to each execution vehicle, enabling each vehicle to execute the corresponding tiered response operation. During the execution of the tiered response operation, the system continuously receives execution status data from each execution vehicle.

[0030] The collected multi-source operational data, decision results, and execution status data are uniformly aggregated, and the aggregated data is recorded in the order of operation to form a full-process operational dataset for subsequent tracing of the cabin operation process.

[0031] In another embodiment, a data acquisition unit installed within the prefabricated cabin continuously collects multi-source operational data within the cabin. During the data acquisition process, the data acquisition unit performs real-time evaluation of each data item. If an anomaly is detected during the evaluation of any data item, a trigger command is immediately sent to the charging equipment, causing it to perform a power-off interlock action. After the power-off interlock action begins, a power-off interlock signal is generated and transmitted along with the multi-source operational data to the multi-source data decision fusion stage. In the multi-source data decision fusion stage, the real-time incoming multi-source operational data and the power-off interlock signal are integrated and analyzed according to a predetermined process. The integrated analysis processes the data from various acquisition units uniformly, and after processing, outputs a decision result containing the fault type and risk level, serving as the basis for subsequent handling. Based on the risk level and fault type in the decision result, the equipment within the cabin used to perform tiered handling operations is determined as the execution carrier. The system issues handling commands corresponding to the decision result to each execution carrier, enabling the execution carrier to perform tiered handling operations. During the execution of the disposal operation, the system synchronously receives execution status data returned by each execution vehicle to ensure continuous monitoring of the disposal process. Multi-source operational data, obtained decision results, and collected execution status data are uniformly summarized and recorded in chronological order to ultimately form a full-process operational dataset for process traceability.

[0032] Preferably, step S1 includes:

[0033] Based on the data acquisition unit inside the prefabricated cabin, environmental information, charging information and battery information are acquired in real time as multi-source operation data;

[0034] When any data item in the multi-source operation data is in an abnormal state, the abnormal signal is uploaded to the charging compartment controller, which then sends a power-off interlock command to the charging equipment, triggering the charging equipment to perform a power-off interlock operation. During the power-off interlock execution, the multi-source operation data is continuously collected in real time.

[0035] After performing a power-off interlock operation, the charging compartment controller generates a power-off interlock signal and synchronously transmits the power-off interlock signal to the multi-source data decision fusion stage.

[0036] In one embodiment, a data acquisition unit installed within the prefabricated cabin acquires environmental, charging, and battery information in real time, using this information as multi-source operational data. The data acquisition unit continuously monitors the internal environment and equipment operating status of the cabin to ensure the continuity and integrity of the multi-source operational data. When any data item in the multi-source operational data exhibits an abnormal state, the data acquisition unit uploads the abnormal signal to the charging cabin controller, which then issues a power-off interlock command to the charging equipment. During the power-off interlock execution, the data acquisition unit continues to collect multi-source operational data uninterruptedly to record the continuous changes in abnormal states and provide complete operational information for subsequent decision-making. After completing the power-off interlock operation, the charging cabin controller generates a power-off interlock signal and synchronously transmits this signal to the multi-source data decision fusion stage. Upon receiving the power-off interlock signal, the multi-source data decision fusion stage uses it, along with the real-time acquired multi-source operational data, for subsequent integrated analysis and decision generation.

[0037] In another embodiment, a data acquisition unit within the prefabricated cabin continuously acquires environmental, charging, and battery information, integrating the collected information to form multi-source operational data. This multi-source operational data is continuously recorded in chronological order during acquisition to reflect the real-time operational status within the cabin. When any data item in the multi-source operational data detects an abnormal state, an abnormal signal is sent to the charging cabin controller, which then triggers the charging equipment to perform a power-off interlock operation to cut off the power supply and prevent further risks. During the power-off interlock execution, the data acquisition unit maintains real-time acquisition of environmental, charging, and battery information to ensure that the entire abnormal state process is recorded. After the power-off interlock is completed, the charging cabin controller generates a power-off interlock signal and transmits it to the multi-source data decision fusion stage. The power-off interlock signal, together with the real-time acquired multi-source operational data, forms the data input for integrated analysis and fault risk assessment in the multi-source data decision fusion stage.

[0038] Of particular importance, step S1 also includes:

[0039] While the data acquisition unit continuously acquires multi-source operational data, it uses iris recognition, face recognition, and voiceprint recognition elements configured in the prefabricated cabin to collect biometric information and then desensitizes the biometric information.

[0040] Based on the desensitized biometric information set, the system performs authorization management for charging equipment use and generates operation records. The authorization results and operation records are then converted into data and transmitted to the multi-source data decision fusion stage.

[0041] In one embodiment, while continuously acquiring multi-source operational data, the data acquisition unit uses iris recognition, facial recognition, and voiceprint recognition elements installed within the prefabricated cabin to collect biometric information from the operator. During the collection process, the iris recognition element acquires the operator's iris features, the facial recognition element acquires the operator's facial features, and the voiceprint recognition element acquires the operator's voice features. The collected biometric information undergoes desensitization processing after collection to remove sensitive information that can directly identify an individual, retaining only the feature data that can be used for authorization management and operation records.

[0042] Based on the desensitized biometric information set, the charging device performs usage authorization management, including verifying the operator's access rights and generating operation records to reflect the entire operation process. The authorization results and operation records are converted into data after generation and transmitted in real time to the multi-source data decision fusion stage, so that they can participate in integrated analysis and decision generation together with multi-source operational data.

[0043] In another embodiment, while continuously collecting multi-source operational data, the data acquisition unit collects biometric information of operators entering the cabin using iris recognition, facial recognition, and voiceprint recognition elements configured within the prefabricated cabin. After collection, the acquired iris images, facial images, and voiceprint signals are desensitized to remove directly identifiable information, and a desensitized biometric information set is generated.

[0044] Based on the aforementioned desensitized biometric information set, the system performs authorization management for the charging equipment, confirms whether the operator has the necessary permissions, and records the operation process to generate an operation log. Subsequently, the generated authorization results and operation logs are converted into data and transmitted to the multi-source data decision fusion stage, where they are used together with other multi-source operational data for subsequent decision analysis and risk assessment.

[0045] Of particular importance is that, in step S1, when any data item in the multi-source operating data becomes abnormal, the charging equipment is immediately triggered to perform a power-off interlock operation, and during the execution of the power-off interlock, continuous real-time acquisition of the multi-source operating data includes:

[0046] Continuous monitoring of multi-source operational data; during the monitoring process, determine whether any data item in the multi-source operational data is in an abnormal state.

[0047] When the monitoring results indicate that any data item is in an abnormal state, the abnormal signal is uploaded to the charging compartment controller, which then sends a power-off lockout command to the charging equipment, causing the charging equipment to enter the power-off lockout operation state.

[0048] During the power-off interlocking operation of the charging equipment, the real-time acquisition of multi-source operating data is maintained without interruption.

[0049] In one embodiment, a data acquisition unit installed within the prefabricated charging compartment continuously monitors multi-source operational data, including environmental information, charging information, and battery information. During monitoring, the charging compartment controller receives and analyzes the status of each data item in real time to determine if any abnormal state exists. When the analysis result of the charging compartment controller indicates that any data item in the multi-source operational data has an abnormal state, the charging compartment controller immediately issues a power-off interlock command to the charging equipment, causing the charging equipment to enter a power-off interlock operation state. During the execution of the power-off interlock operation, the data acquisition unit, under the scheduling of the charging compartment controller, continuously collects multi-source operational data in real time to ensure uninterrupted data acquisition. In this way, the charging compartment controller can continuously acquire various types of operational data within the compartment while the charging equipment enters the power-off interlock state, providing complete data support for subsequent multi-source data decision fusion analysis, and realizing timely response and full-process recording of abnormal states within the charging compartment.

[0050] Preferably, step S2 includes:

[0051] Receive the generated multi-source operating data and power failure interlock signal, and put the data into the data processing buffer;

[0052] Multi-source operational data is classified and organized, and environmental information, charging information, battery information, and power failure interlock signals are stored in order of data type and acquisition time.

[0053] The multi-source data decision fusion process is invoked to perform integrated analysis on the processed multi-source operating data and the power failure interlock signal generated by the charging compartment controller. This includes making unified judgments on abnormal states, signal fluctuations and equipment response states, and generating integrated analysis results.

[0054] Based on the integrated analysis results, preliminary fault identification and risk level assessment are generated, forming a decision result dataset that includes fault type and risk level.

[0055] In one embodiment, the charging compartment controller receives multi-source operational data generated by the data acquisition unit and power-off interlock signals generated after the charging equipment performs a power-off interlock operation, and uniformly categorizes the received data into a data processing buffer. In the buffer, the multi-source operational data is categorized and organized, storing environmental information, charging information, battery information, and power-off interlock signals according to data type and acquisition time order to ensure data integrity and temporal consistency. The multi-source data decision fusion step is invoked to perform integrated analysis on the organized multi-source operational data and the power-off interlock signals generated by the charging compartment controller. During the integrated analysis, the system uniformly judges the abnormal states, signal fluctuations, and response states of each device in the multi-source data, identifies existing anomalies, and generates corresponding integrated analysis results. Based on the integrated analysis results, the system further generates preliminary fault identification and risk level judgment, and organizes the analysis results into a decision result dataset containing fault type and risk level. This decision result dataset can be used for subsequent graded handling operations of the carrier.

[0056] In another embodiment, the data processing module receives multi-source operational data from the data acquisition unit within the prefabricated cabin, as well as power-off interlock signals generated after the charging equipment performs a power-off interlock operation under abnormal conditions. The received data is uniformly categorized into a data processing buffer for centralized management. Within the data processing buffer, the multi-source operational data is classified and organized, storing environmental information, charging information, battery information, and power-off interlock signals sequentially according to data type and acquisition time, ensuring the temporal integrity and traceability of each type of data. After organization, the system invokes the multi-source data decision fusion step to perform integrated analysis on the categorized data. During the analysis, the system uniformly determines abnormal states, signal fluctuations, and equipment response states in the multi-source operational data and generates integrated analysis results.

[0057] After completing the integrated analysis, the system generates preliminary fault identification and risk level judgment based on the integrated analysis results, and organizes the identification results and risk levels into a decision result dataset containing fault type and risk level, providing a basis for subsequent graded handling.

[0058] Preferably, the multi-source data fusion decision-making process includes:

[0059] The processed multi-source operating data and power failure interlocking signals are timestamped. After the timestamping is completed, the data is formatted and normalized.

[0060] The processed data is then transferred to a unified data processing buffer.

[0061] Cross-validation is performed by the charging compartment controller, which compares the abnormal characteristics of different data sources within the same time period, identifies abnormal states that are missed by a single data source, and generates cross-validation results.

[0062] The charging compartment controller performs correlation analysis to analyze the changes in environmental safety information, air quality information, and battery information, and generates correlation analysis results.

[0063] Based on the cross-validation results and correlation analysis results, the abnormal conditions of multi-source operational data are matched and compared to form an integrated analysis result that includes fault type and risk level.

[0064] In one embodiment, after receiving the processed multi-source operating data and power-off interlock signal, the charging pod controller performs timestamp alignment on various data types to ensure that the acquisition time of all data is compared on a unified timeline. After timestamp alignment, the data format is standardized and normalized, enabling data from different sources and of different types to be analyzed and judged under the same standard. After processing, all standardized data is transmitted to a unified data processing buffer for subsequent analysis by the charging pod controller. The charging pod controller performs cross-validation in the buffer, comparing the abnormal characteristics of different data sources within the same time period, identifying abnormal states missed by a single data source, and generating cross-validation results.

[0065] The charging compartment controller performs correlation analysis, analyzing the changing relationships between environmental safety information, air quality information, and battery information, and generating correlation analysis results. Based on the cross-validation results and correlation analysis results, it matches and compares anomalies in multi-source operational data, and generates integrated analysis results including fault types and risk levels, providing a basis for subsequent graded response.

[0066] In another embodiment, the processed multi-source operational data and power-off interlock signals are timestamped by the charging cabin controller to ensure that all types of data correspond and are compared in a unified time sequence. After timestamping, the data from different sources undergoes format unification and normalization to ensure unified judgment and calculation in subsequent analysis. The processed data is transmitted to a unified data processing buffer for centralized management and analysis by the charging cabin controller. The charging cabin controller performs cross-validation in the buffer, comparing the abnormal characteristics of different data sources within the same time period to identify and record abnormal states not detected by a single data source, forming cross-validation results. The charging cabin controller performs correlation analysis to analyze the changes in environmental safety information, air quality information, and battery information, forming correlation analysis results. Based on the cross-validation results and correlation analysis results, abnormal states in the multi-source operational data are matched and compared, ultimately forming an integrated analysis result including fault type and risk level, which serves as the basis for subsequent graded handling operations by the control unit within the cabin.

[0067] Preferably, step S3 includes:

[0068] Based on the risk level and fault type in the decision-making results, determine the graded handling operation requirements for the equipment inside the cabin, and identify the equipment that requires graded handling operation as the execution carrier.

[0069] The system sends control commands corresponding to the risk level to the execution vehicle, which then performs the corresponding graded disposal operations according to the control commands.

[0070] During the execution of the carrier-level disposal operation, the operational response of the carrier is tracked and the execution status data is output.

[0071] In one embodiment, the charging compartment controller receives decision results generated by the multi-source data decision fusion process, including fault type and risk level. Based on the risk level and fault type in the decision results, it determines the tiered handling operation requirements for each device within the compartment and identifies devices that meet the handling requirements as execution carriers. Subsequently, the charging compartment controller sends control commands corresponding to the risk level to each execution carrier, causing the execution carrier to perform the corresponding tiered handling operation according to the control commands. During the tiered handling operation, the charging compartment controller tracks the operating status of each execution carrier, collects the response information of the execution carrier, and generates execution status data. Finally, the execution status data of all execution carriers is recorded in real time for subsequent status monitoring and analysis of operations within the compartment, ensuring the integrity and traceability of the tiered handling operation.

[0072] In another embodiment, a decision result containing fault type and risk level, generated by a multi-source data decision fusion process, is received. Based on the decision result, the charging compartment controller identifies the tiered handling operation requirements of each device within the compartment and determines the devices requiring tiered handling operations as execution carriers. The charging compartment controller then issues control commands matching their corresponding risk levels to all execution carriers, enabling each execution carrier to perform the corresponding tiered handling operation according to the control commands. During execution, the charging compartment controller continuously monitors and tracks the operational response of the execution carriers, obtains execution feedback, and generates corresponding execution status data. By recording the execution status data, the operation of the execution carriers within the compartment can be tracked throughout the entire process, providing data support for subsequent decision adjustments and operational optimization, and ensuring that all tiered handling operations are traceable and verifiable.

[0073] Preferably, the tiered treatment operation includes:

[0074] When the decision indicates a low risk level, the ventilation equipment is switched to forced ventilation to accelerate airflow in the cabin, and the indicator lighting is switched to warning mode to alert that there is an abnormal situation in the cabin.

[0075] When the risk level is medium, after completing the basic handling operations, a control command to reduce the charging power or suspend charging is issued to the charging equipment, and the ventilation equipment is switched to the early warning standby state.

[0076] When the risk level is high, the charging compartment controller directly sends a power-off interlock command to the charging equipment, controls the ventilation equipment to switch to maximum power exhaust, and sends an emergency alarm message to the remote monitoring platform.

[0077] In one embodiment, when the decision result generated by the multi-source data decision fusion process indicates a low risk level, the charging cabin controller controls the ventilation equipment to enter forced ventilation mode to accelerate airflow within the cabin. Simultaneously, the indicator lighting is switched to warning mode to alert the cabin to any abnormal situation. When the risk level is medium, in addition to completing the low-level tiered handling procedures, a control command is issued to the charging equipment to reduce charging power or suspend charging, and the ventilation equipment is switched to a higher operating speed to enhance heat dissipation within the cabin. When the risk level is high, the charging cabin controller directly issues a power-off interlock command to the charging equipment and controls the ventilation equipment to switch to maximum power exhaust to quickly remove harmful gases or smoke from the cabin. Simultaneously, an emergency alarm message is sent to the remote monitoring platform to ensure timely handling of emergencies within the cabin.

[0078] In another embodiment, the risk level is determined based on the decision results generated by the multi-source data decision fusion process. If the risk level is low, the charging cabin controller instructs the ventilation equipment to enter forced ventilation mode to accelerate airflow within the cabin and switches the indicator lighting equipment to warning mode to indicate an abnormal state within the cabin. When the risk level is medium, after completing the low-level tiered handling operations, the charging cabin controller issues a control command to the charging equipment to reduce charging power or suspend charging, while simultaneously switching the ventilation equipment to a pre-warning standby state to ensure a tiered response to cabin safety measures. When the risk level is high, the charging cabin controller directly issues a power-off and interlock command to the charging equipment and switches the ventilation equipment to maximum power exhaust mode to quickly evacuate the air within the cabin, and sends an emergency alarm message to the remote monitoring platform, achieving rapid response to high-risk states within the cabin and linkage with remote monitoring.

[0079] Please refer to [link / reference needed] for further information. Figure 3 Above the main equipment inside the cabin, on both sides, is an exhaust fan. In low-risk situations, the fans activate to provide forced ventilation, accelerating airflow and dispersing accumulated heat or suspicious gases. In high-risk situations (such as fire), the fans operate at maximum power to rapidly expel high-temperature smoke and toxic flammable gases from the cabin to the outside.

[0080] Preferably, the tiered treatment process further includes:

[0081] When the decision indicates that the risk level is low, the fire-fighting equipment is kept in standby monitoring mode and its self-test program is initiated to confirm that the equipment itself is in an operational state, and the fire-fighting equipment is switched to the early warning standby state.

[0082] When the risk level is medium, after completing the basic response procedures, activate the local early warning spray of the fire-fighting equipment to locally suppress potential risk points.

[0083] When the risk level is high, the charging compartment controller directly sends power-off and interlock commands to the charging equipment and full-area activation commands to the fire-fighting equipment, controlling the fire-fighting equipment to release full-flow extinguishing agent for comprehensive fire suppression, and sending emergency alarm information to the remote monitoring platform.

[0084] In one embodiment, when the decision indicates a low risk level, the charging chamber controller controls the fire-fighting equipment to maintain a basic standby monitoring state and initiates its self-test program to automatically detect the extinguishing agent level, pipeline pressure, and nozzle status. After confirming that the equipment is operational, the controller switches the fire-fighting equipment from standby to early warning standby state. At this time, the equipment drive circuit is powered on, the spray mechanism completes its preparatory actions, and is in a state of readiness for immediate activation. When the risk level rises to medium, based on the aforementioned low-level handling operations, the charging chamber controller issues a partial activation command to the fire-fighting equipment, initiating its partial early warning spraying program. This program controls specific zone nozzles or release devices to spray small doses of inert gas or extinguishing agent at potential risk points (such as individual charging piles or battery packs) indicated by abnormal sensor data, in order to achieve early suppression and control the spread of risk. When the risk level is determined to be high, the charging chamber controller simultaneously reconfirms with the charging equipment and issues a power-off interlock command, while also issuing a full-area activation command to the fire-fighting equipment. The command triggers the fire suppression system to release full-flow extinguishing agent, which then comprehensively extinguishes the fire in the entire protected area of ​​the cabin through spraying or diffused application. Simultaneously, the system immediately sends an emergency alarm message to the remote monitoring platform, including location, risk level, and status of the incident, requesting external support and initiating the emergency response process.

[0085] Please refer to [link / reference needed] for further information. Figure 2 This is a three-dimensional protective space from the inside out and from top to bottom. The top is equipped with a fire sprinkler system that covers the entire area, achieving fire suppression and cooling from top to bottom. The upper two sides of the space are equipped with automatic fire extinguishing devices with pure water-based fire extinguishing agents for key areas such as the battery compartment, for precise and targeted fire suppression. The middle and lower two sides of the space are equipped with environmental parameter sensors to monitor data such as temperature, smoke, and combustible gases in real time, forming a sensory network. Auxiliary fire extinguishing equipment is provided in the lower part of the space, forming a closed-loop safety system of perception, early warning, automatic suppression, and manual backup. This demonstrates the multi-source data fusion and hierarchical handling mechanism of the prefabricated charging integrated equipment.

[0086] Preferably, step S3 further includes:

[0087] When multiple risk levels are triggered simultaneously, determine the target execution level and coordinate the timing of different response actions.

[0088] Before issuing the graded handling instructions, the charging compartment controller performs a pre-check on the current status of each execution device, and only starts the corresponding handling process after confirming that the device is in an executable state.

[0089] An overtime monitoring mechanism is set up for the handling actions. When the executing device fails to provide an execution confirmation signal within a preset time, a corresponding red alarm signal is triggered, and the status of each executing device is continuously monitored.

[0090] In one embodiment, when the decision result generated by the multi-source data decision fusion process shows that multiple risk levels are triggered simultaneously within the charging cabin, the charging cabin controller analyzes all triggered risk levels to determine the target execution level, which guides the priority of subsequent graded handling actions. Based on the determined target execution level, different handling actions are coordinated in sequence to ensure that each execution vehicle performs the corresponding graded handling operation in a predetermined order.

[0091] Before issuing tiered response instructions, the charging cabin controller performs a pre-check on the current status of each execution device. Only after confirming that the equipment is in an executable state will the corresponding response process be initiated to ensure the instructions can be executed correctly. During execution, a timeout monitoring mechanism is set up for each response action. If a certain execution device fails to provide an execution confirmation signal within a preset time, the charging cabin controller triggers the corresponding red alarm signal and continuously monitors the operating status of all execution devices to ensure the reliability and traceability of risk response within the cabin.

[0092] In another embodiment, when the decision result output by the multi-source data decision fusion stage indicates that multiple risk levels are triggered simultaneously within the cabin, the charging cabin controller judges the severity of each risk level, determines the target execution level, and plans the time sequence of each graded disposal action to coordinate the operation sequence between different execution carriers. Before issuing graded disposal instructions to the execution carriers, the charging cabin controller checks the current status of each execution carrier. After confirming that the equipment is in an executable state, it initiates the corresponding disposal process to ensure that each disposal action can be implemented as planned. For disposal actions, the charging cabin controller sets up a timeout monitoring mechanism. When the execution carrier fails to return an execution confirmation signal within a specified time, the corresponding red alarm signal is immediately triggered. At the same time, the controller continuously monitors the status changes of all execution carriers to ensure timely response and status traceability of safe disposal operations within the cabin.

[0093] Of particular importance is that, in step S3, based on the risk level and fault type in the decision results, the required graded handling operations for the equipment within the cabin are determined, and the equipment requiring graded handling operations is identified as the execution carrier, including:

[0094] Based on the risk level and fault type in the decision-making results, the equipment inside the cabin is matched one by one to determine the handling function of each piece of equipment in the graded handling operation.

[0095] Based on the matching results and the requirements of the graded disposal operation, the equipment inside the cabin that will perform the graded disposal operation is selected and the equipment is identified as the execution carrier.

[0096] Clearly define the specific operational items and execution sequence for each execution vehicle in the tiered disposal process.

[0097] In one embodiment, the charging compartment controller receives decision results generated by a multi-source data decision fusion process, which include risk level and fault type information. Based on the risk level and fault type in the decision results, the charging compartment controller matches each device within the compartment, determining the specific functions and roles each device can perform in the tiered response operation. According to the matching results and the requirements of the tiered response operation, the charging compartment controller filters out the devices that need to perform the tiered response operation and identifies these devices as execution carriers. After identifying the execution carriers, the charging compartment controller further clarifies the specific operation items and execution order of each execution carrier in the tiered response operation and establishes a corresponding relationship in the execution carrier list for subsequent sending of control commands and monitoring of operational status. This embodiment achieves precise scheduling of devices within the compartment to perform tiered response operations based on risk level and fault type, ensuring that all devices within the charging compartment work collaboratively according to a predetermined strategy.

[0098] Preferably, step S4 includes:

[0099] The charging compartment controller receives multi-source operating data from various data acquisition units, power-off interlock signals from charging equipment, decision results of graded handling, and execution status data fed back by various execution carriers.

[0100] The received data is classified, organized, and time-series corrected.

[0101] The processed multi-source operational data, power failure interlock signals, decision results, and execution status data are sequentially collected into a unified operational data storage unit, and an index is created.

[0102] After data recording is completed, a full-process operation dataset is formed. This dataset is uploaded to the remote management platform in real time. The platform monitors and analyzes the status based on the data and has the authority to remotely issue control commands to the charging compartment controller, realizing full-process traceability and tracking management of all operations within the charging compartment.

[0103] In one embodiment, the charging compartment controller receives multi-source operational data from various data acquisition units within the prefabricated compartment. It also receives power-off interlock signals generated after the charging equipment performs a power-off interlock operation, as well as feedback data on the execution status of tiered handling instructions issued based on decision results. Upon receiving these data types, the charging compartment controller categorizes and organizes the data, grouping environmental information, charging information, battery information, power-off interlock signals, tiered handling decision results, and execution status data into different data groups. Subsequently, the charging compartment controller performs time-series correction on the various data types to ensure that the data acquisition time sequence matches the storage sequence, guaranteeing the accuracy of subsequent analysis.

[0104] After data processing, the charging compartment controller sequentially aggregates multi-source operational data, power-off interlock signals, decision results, and execution status data into a unified operational data storage unit, and establishes an index within the storage unit for rapid retrieval and management. Once data recording is complete, a full-process operational dataset is formed and uploaded to the remote management platform in real time. The remote management platform monitors and performs in-depth analysis of the uploaded data, and also has the authority to remotely issue control commands (such as parameter adjustments and equipment start / stop) to the charging compartment controller, thereby achieving full-process traceability, tracking, and cloud-based collaborative management of all operations within the charging compartment.

[0105] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A prefabricated cabin type charging integrated control method, characterized by, Comprise the following steps: Step S1: based on the data acquisition unit arranged in the prefabricated cabin obtains multi-source operation data, when any data item in the multi-source operation data appears abnormal, the abnormal signal is uploaded to the charging cabin controller, the controller triggers the charging equipment to execute power-off locking, and the power-off locking signal is transmitted to the multi-source data decision fusion link; wherein, step S1 comprises: Real-time acquisition of environmental information, charging information and battery information based on the data acquisition unit in the prefabricated cabin as multi-source operation data; When any data item in the multi-source operation data appears abnormal state, the abnormal signal is uploaded to the charging cabin controller, the charging cabin controller issues power-off locking instruction to the charging equipment, triggers the charging equipment to execute power-off locking operation, and continuously collects real-time multi-source operation data during power-off locking execution; The charging cabin controller generates a power-off locking signal after executing the power-off locking operation, and synchronously transmits the power-off locking signal to the multi-source data decision fusion link; Step S2: integrated analysis is carried out on multi-source operation data and power-off locking signal, and decision result containing fault type and risk level is generated through multi-source data decision fusion link; Step S3: according to the risk level and fault type in the decision result, the equipment arranged in the cabin is controlled as the execution carrier to execute the corresponding hierarchical disposal operation, and the execution state data fed back by each execution carrier is received; wherein, step S3 comprises: According to the risk level and fault type in the decision result, the hierarchical disposal operation requirement of the equipment in the cabin is determined, and the equipment needing hierarchical disposal operation is determined as the execution carrier; wherein, the hierarchical disposal operation comprises: When the decision result indicates that the risk level is low, control the ventilation equipment to enter the forced ventilation state, speed up the air flow in the cabin, and switch the indicating lighting equipment to the warning mode to prompt that there is an abnormal situation in the cabin; When the risk level is medium, on the basis of completing the low-level disposal operation, the control instruction of reducing the charging power or suspending the charging is issued to the charging equipment, and the ventilation equipment is switched to the early warning standby state; When the risk level is high, the charging cabin controller directly issues the power-off locking instruction to the charging equipment, controls the ventilation equipment to switch to the maximum power exhaust, and sends the emergency alarm information to the remote monitoring platform; Send the control instruction corresponding to the risk level to the execution carrier, and the execution carrier executes the corresponding hierarchical disposal operation according to the control instruction; During the hierarchical disposal operation of the execution carrier, the operation response of the execution carrier is tracked, and the execution state data is outputted; Step S4: collect and record the multi-source operation data, decision result and execution state data to form the whole process operation data set to realize process tracing.

2. The prefabricated cabin type charging integrated control method according to claim 1, characterized in that, Step S2 comprises: Receive the generated multi-source operation data and power-off locking signal, and put the data into the data processing buffer; Classify and arrange the multi-source operation data, store the environmental information, charging information, battery information and power-off locking signal according to the data type and collection time sequence; The multi-source data decision fusion link is called to perform integrated analysis on the collated multi-source operation data and the power-off locking signal generated by the charging cabin controller, including unified judgment on abnormal states, signal fluctuations and device response states, to generate an integrated analysis result; According to the integrated analysis result, preliminary fault identification and risk level judgment are generated, and a decision result data set containing fault types and risk levels is formed.

3. The prefabricated cabin type charging integrated control method according to claim 2, characterized in that, The multi-source data decision fusion link includes: After the collated multi-source operation data and the power-off locking signal are timestamped, format unification and normalization processing are performed on each type of data; The processed data is transmitted to a unified data processing buffer; The charging cabin controller performs cross-validation to compare abnormal features of different data sources in the same period, identifies abnormal states missed by a single data source, and generates a cross-validation result; The charging cabin controller performs correlation analysis to analyze the change relationship between environmental safety information, air quality information and battery information, and generates a correlation analysis result; According to the cross-validation result and the correlation analysis result, the abnormal conditions of the multi-source operation data are matched and compared to form an integrated analysis result containing fault types and risk levels.

4. The prefabricated cabin type charging integrated control method according to claim 1, characterized in that, The hierarchical disposal operation further includes: When the decision result indicates that the risk level is low, the control fire-fighting equipment remains in standby monitoring state, and starts its self-checking program to confirm that the device itself is in a runnable state, and switches the fire-fighting equipment to a pre-alarm standby state; When the risk level is medium, on the basis of completing the low-level disposal operation, start the local pre-alarm injection of the fire-fighting equipment to locally suppress potential risk points; When the risk level is high, the charging cabin controller directly issues a power-off locking instruction to the charging equipment and a global start instruction to the fire-fighting equipment to control the fire-fighting equipment to release full-flow extinguishing agent for covering fire extinguishing, and sends an emergency alarm information to the remote monitoring platform.

5. The prefabricated cabin type charging integrated control method according to claim 1, characterized in that, Step S3 further includes: When multiple risk levels are triggered at the same time, determine the target execution level, and time-coordinate different disposal actions; Before issuing the hierarchical disposal instruction, the charging cabin controller pre-inspects the current state of each execution device to confirm that the device is in an executable state before starting the corresponding disposal process; For disposal actions, a timeout monitoring mechanism is set, when the execution device does not feedback the execution confirmation signal within the preset time, the corresponding red alarm signal is triggered, and the state of each execution device is continuously monitored.

6. The prefabricated cabin type charging integrated control method according to claim 1, characterized in that, Step S4 includes: The charging cabin controller receives multi-source operation data from each data acquisition unit, power-off locking signals of the charging equipment, decision results of hierarchical disposal, and execution state data feedback from each execution carrier; Classify and arrange the received data and correct the time sequence; The collated multi-source operation data, power-off locking signal, decision result and execution state data are sequentially collected into a unified operation data storage unit, and an index is established; After the data recording is completed, a whole-process operation data set is formed, the data set is uploaded to a remote management platform in real time, the platform performs state monitoring and analysis based on the data, has the right to remotely issue control instructions to the charging cabin controller, and realizes whole-process traceability and tracking management of each operation in the charging cabin.

7. A prefabricated cabin type charging integrated device characterized by, The prefabricated cabin type charging integrated control method is used to execute any one of claims 1-6, comprising: a prefabricated cabin body, and charging equipment, fire-fighting equipment, ventilation equipment, lighting equipment, video monitoring equipment and various sensors integrated in the cabin body; The charging equipment, fire-fighting equipment, ventilation equipment and various sensors are respectively used as data acquisition units for multi-source data decision fusion and as execution carriers for hierarchical disposal operations; The prefabricated cabin body is configured with iris recognition elements, face recognition elements and voiceprint recognition elements for executing biological feature information acquisition; A charging cabin controller is used to centrally manage various integrated equipment in the prefabricated cabin body, including charging equipment, fire-fighting equipment, ventilation equipment, lighting equipment, video monitoring equipment and various sensors.

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