Intelligent Energy Management Method, Device and Storage Medium for Emergency Energy Vehicles

Abstract

The present invention relates to the technical field of energy management, and particularly to an intelligent energy management method, device and storage medium for an emergency energy vehicle. The method includes: determining the environmental risk level and generating a power protection strategy according to the environmental risk level; judging the cable state and updating the power protection strategy according to the cable state; optimizing the judgment process of the cable state; jointly analyzing the dust concentration in the battery compartment and the temperature difference inside and outside the battery compartment collected within the monitoring period to determine the battery compartment anomaly index, monitoring the impedance of the battery compartment to determine the impedance anomaly index, combining the battery compartment anomaly index and the impedance anomaly index to judge the battery state, and generating a discharge protection strategy according to the battery state; comprehensively analyzing the environmental risk level, cable state and battery state of each monitoring period within the management period to determine the safety state of the emergency energy vehicle and giving a safety warning to the user. The present invention effectively improves the energy management efficiency and safety of the emergency energy vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management, and particularly to an intelligent energy management method, device and storage medium for an emergency energy vehicle. Background Art

[0002] Traditional emergency energy vehicles mostly adopt static threshold monitoring and independent parameter control strategies, such as a single meteorological parameter exceeding the limit to trigger a protection mechanism, or only mechanical protection based on cable tension, etc. There are defects such as the lack of coupling analysis of environmental risks, rough evaluation of equipment status, and rigid protection strategies; in view of the above problems, there is an urgent need for an intelligent management method that integrates environmental-geological multi-source data coupling analysis, cable multi-physical field modeling, and battery progressive degradation evaluation to achieve accurate risk perception, three-dimensional diagnosis of equipment status, and dynamic optimization of protection strategies, ensuring the reliable operation and efficient power supply of emergency energy vehicles under complex working conditions.

[0003] Chinese Patent Publication No. CN119261595A discloses an efficient power and energy management system for an emergency energy vehicle, including a methanol reforming device, a hydrogen storage device, a fuel cell, and an energy management module. The methanol reforming device converts methanol into hydrogen and stores it in the hydrogen storage device. The hydrogen storage device is used to ensure the stable supply of hydrogen. The fuel cell converts hydrogen into electrical energy to drive the vehicle, and the excess electrical energy is stored in the battery pack. The energy management module optimizes energy distribution and supply according to the operating state and energy demand of the vehicle. The present invention significantly improves the energy efficiency and reliability of the vehicle through the integration of multi-energy supply units, dynamic adjustment of the intelligent management module, energy prediction and optimization, and the use of a regenerative braking system; it can be seen that this solution only covers basic parameters such as speed and acceleration, lacks in-depth monitoring of the health status of the power system, and has no safety coordination control logic, resulting in low energy management efficiency and low safety of emergency energy vehicles. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent energy management method, device and storage medium for an emergency energy vehicle to solve at least one of the problems existing in the prior art.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions:

[0006] An intelligent energy management method for an emergency energy vehicle includes:

[0007] Performing coupling analysis on the meteorological data and geological data collected within a monitoring period to determine the environmental risk level, and generating a power protection strategy according to the environmental risk level;

[0008] Collecting cable data within the monitoring period, and constructing a force-thermal coupling model according to the collected cable data to judge the cable state, and updating the power protection strategy according to the cable state;

[0009] Jointly analyze the dust concentration in the battery compartment and the temperature difference inside and outside the battery compartment collected during the monitoring period to determine the abnormal index of the battery compartment, monitor the impedance of the battery compartment to determine the impedance abnormal index, combine the abnormal index of the battery compartment and the impedance abnormal index to judge the battery state, and generate a discharge protection strategy according to the battery state.

[0010] Optionally, the method for evaluating the environmental risk level includes: coupling and analyzing the environmental wind speed v0 and rainfall intensity j0 collected during the monitoring period to construct a meteorological influence factor;

[0011] The construction process of the meteorological influence factor is as follows:

[0012] Compare and analyze the environmental wind speed v0 and the wind speed discrimination factor v1 collected during the monitoring period. If the environmental wind speed v0 is greater than or equal to the wind speed discrimination factor v1, the expression of the wind speed factor Fy is Fy = ln{[(v0 - v1) / v0] 2 + 1}, if the environmental wind speed v0 is less than the wind speed discrimination factor v1, the expression of the wind speed factor Fy is Fy = 0;

[0013] Couple and analyze the wind speed factor Fy and the rainfall intensity j0 to construct a meteorological influence factor Q. The expression of the meteorological influence factor Q is Q = Fy×(j0 / J), where J is the rainfall intensity threshold.

[0014] Optionally, the method for evaluating the environmental risk level further includes: constructing a geological influence factor according to the geological data collected during the monitoring period, and coupling and analyzing the surface micro-vibration frequency z0 and terrain slope p0 collected during the monitoring period to construct a geological influence factor Zy. The expression of Zy is Zy = x1×(z0 / Z) 1.2 + x2×ln[3×p0 / P + 1] / ln4, where Z is the vibration frequency threshold, P is the slope threshold, x1 is the vibration weight, and x2 is the slope weight.

[0015] Optionally, the method for evaluating the environmental risk level further includes: coupling and analyzing the meteorological influence factor and the geological influence factor to determine the environmental risk level, and generating a power protection strategy according to the environmental risk level;

[0016] Construct a risk index FX according to the meteorological influence factor Q and the geological influence factor Zy. The expression of FX is FX = w1×Q + w2×Zy, where w1 is the meteorological weight and w2 is the geological weight;

[0017] Compare the risk index FX with the risk discrimination factors y1 and y2 to determine the environmental risk level. When the risk index FX is less than the risk discrimination factor y1, the environmental risk level is determined to be level one, and no power protection strategy is adopted. When the environmental risk index FX is greater than or equal to the risk discrimination factor y1 and less than or equal to the risk discrimination factor y2, the environmental risk level is determined to be level two, and dynamic counterweight is enabled. When the risk index FX is greater than the risk discrimination factor y2, the environmental risk level is determined to be level three, and the engine output power is limited to α of the rated power, where α is the limiting index.

[0018] Optionally, the construction process of the force-heat coupling model is as follows:

[0019] S = {T load ×[1 + lg(t / te + 1)] / T max}×[1.2×(θ / θy) 2 ;

[0020] The S is the force-heat coupling index, the T max is the rated breaking tension, the T load is the cable tension, the t is the core temperature, the te is the temperature threshold, the θ is the swing angle, and the θy is the angle threshold;

[0021] Compare the force-heat coupling index S with the adjustment discrimination factor c to judge the cable state. If the force-heat coupling index S is greater than the adjustment discrimination factor c, it is determined that the cable state is abnormal in the current monitoring period. Otherwise, it is determined that the cable state is normal in the current monitoring period. When the cable state is abnormal and the risk level is level three, the limiting index is updated to α1.

[0022] Optionally, it further includes: comparing the cable swing frequency collected within the monitoring period with the frequency discrimination factor to optimize the judgment process of the cable state; when optimizing the judgment process of the cable state, compare the cable swing frequency bp collected within the monitoring period with the frequency discrimination factor B. If the cable swing frequency is greater than the frequency discrimination factor B, optimize the judgment process of the cable state and optimize the adjustment discrimination factor to c1.

[0023] Optionally, the method for generating the discharge protection strategy includes:

[0024] Jointly analyze the dust concentration in the battery compartment and the temperature difference inside and outside the battery compartment collected within the monitoring period to determine the battery compartment anomaly index;

[0025] Monitor the impedance of the battery compartment to determine the impedance anomaly index;

[0026] Fuse the battery compartment anomaly index and the impedance anomaly index to judge the battery state, and generate a discharge protection strategy according to the battery state;

[0027] When determining the abnormal index of the battery compartment, the dust concentration n0, the dust discrimination factor n1, the temperature difference △t between the inside and outside of the battery compartment, and the temperature difference discrimination factor △ty in the battery compartment are used to construct the abnormal index k of the battery compartment. The expression of k is k = ln{[(n0 / n1) × lg(△t / △ty)]^2 + 1} / ln2;

[0028] When determining the impedance abnormal index, the battery compartment is evenly divided into n regions, and the impedance of the i-th region is denoted as Gi; the impedance abnormal index ZY is constructed according to the impedance of each region. The expression of ZY is ; where △Z is the fluctuation discrimination threshold;

[0029] When generating the discharge protection strategy, data fusion is performed on the abnormal index k of the battery compartment and the impedance abnormal index ZY to determine the battery state index ZT. The ZT is the sum of the product of the battery compartment weight L1 and the abnormal index k of the battery compartment and the product of the impedance weight L2 and the impedance abnormal index ZY;

[0030] The battery state index ZT is compared with the battery state discrimination factor zt. When the battery state index ZT is greater than the battery state discrimination factor zt, it is determined that the battery state is abnormal in the current monitoring period. Otherwise, it is determined that the battery state is normal in the current monitoring period. When the battery state is abnormal, the discharge rate is reduced to β × the rated discharge rate, where β is the discharge limit factor.

[0031] Optionally, it further includes: comprehensively analyzing the environmental risk level, cable status, and battery status in each monitoring period within the management cycle to determine the safety status of the emergency energy vehicle and giving a safety warning to the user; when determining the status of the emergency energy vehicle, comprehensively analyzing the environmental risk level, cable status, and battery status in each monitoring period within the management cycle to determine the safety index R, comparing the safety index R with the safety discrimination factor R0 to determine the safety status of the emergency energy vehicle. If the safety index R is greater than or equal to the safety discrimination factor R0, it is determined that the safety status of the emergency energy vehicle is abnormal in the current management cycle. Otherwise, it is determined that the safety status of the emergency energy vehicle is normal in the current management cycle. When the safety status of the emergency energy vehicle is abnormal, a safety warning is given to the user.

[0032] According to another aspect of the present application, there is provided an intelligent energy management device for an emergency energy vehicle, including:

[0033] A power protection unit for performing coupled analysis on the meteorological data and geological data collected within the monitoring period to determine the environmental risk level and generating a power protection strategy according to the environmental risk level;

[0034] A cable monitoring unit is used to collect cable data within a monitoring period, construct a force-thermal coupling model based on the collected cable data to judge the cable status, and update the power protection strategy according to the cable status;

[0035] A frequency monitoring unit is used to compare the cable swing frequency and the frequency discrimination factor collected within a monitoring period to optimize the process of judging the cable status;

[0036] A discharge protection unit is used to jointly analyze the dust concentration in the battery compartment and the temperature difference inside and outside the battery compartment collected within a monitoring period to determine the battery compartment anomaly index, monitor the impedance of the battery compartment to determine the impedance anomaly index, combine the battery compartment anomaly index and the impedance anomaly index to judge the battery status, and generate a discharge protection strategy according to the battery status;

[0037] A safety warning unit is used to comprehensively analyze the environmental risk level, cable status and battery status of each monitoring period within a management period to determine the safety status of the emergency energy vehicle and give a safety warning to the user.

[0038] According to another aspect of the present application, there is provided a computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, wherein the computer program is used to control the electronic device where the computer-readable storage medium is located to execute the intelligent energy management method of the emergency energy vehicle when running.

[0039] The beneficial effects of the present invention are as follows: Through multi-source data collaborative analysis and intelligent decision-making mechanism, a closed-loop management of the entire chain of the emergency energy vehicle is constructed. At the environmental perception layer, coupling meteorological and geological data breaks through the limitations of isolated risk assessment, and the inclusion of terrain slope and micro-vibration significantly improves the ability to predict secondary disasters such as landslides and rockfalls. At the equipment status layer, the force-thermal-mechanical coupling model and the battery multi-dimensional diagnosis technology work together to achieve an upgrade from single-fault alarm to potential risk tracing; at the control strategy layer, the dynamic weight adjustment and non-linear threshold optimization algorithm make the protection measures more accurately adapted to the actual working conditions, avoiding energy waste caused by excessive intervention; the global safety index calculation combined with historical data analysis supports the extension from real-time emergency response to preventive maintenance. The overall solution is upgraded through intelligentization, strengthening the equipment reliability in extreme environments, optimizing the energy utilization efficiency, and providing multi-dimensional data support for operation and maintenance decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0041] Figure 1 It is a schematic flow chart of the intelligent energy management method for the emergency energy vehicle in this embodiment.

[0042] Figure 2 It is a schematic flow chart of the evaluation method for the environmental risk level in this embodiment.

[0043] Figure 3 It is a schematic flow chart of the method for generating the discharge protection strategy in this embodiment.

[0044] Figure 4 It is a schematic structural diagram of the intelligent energy management device for the emergency energy vehicle in this embodiment.

[0045] Figure 5 It is a schematic structural diagram of the electronic device in this embodiment. Detailed implementation manners

[0046] To more clearly illustrate the present invention, the present invention will be further described below in conjunction with preferred embodiments and the accompanying drawings. Similar components in the drawings are denoted by the same reference numerals. Those skilled in the art should understand that the content specifically described below is illustrative rather than restrictive, and should not be used to limit the protection scope of the present invention.

[0047] It should be noted that although terms such as first, second, and third may be used in the embodiments of the present application for description, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, without departing from the scope of the embodiments of the present application, the first may also be referred to as the second, and similarly, the second may also be referred to as the first.

[0048] Please refer to Figure 1 As shown, it is a schematic flow chart of the intelligent energy management method for the emergency energy vehicle in this embodiment, including:

[0049] Step S101, performing coupled analysis on the meteorological data and geological data collected within the monitoring period to determine the environmental risk level, and generating a power protection strategy according to the environmental risk level, where the meteorological data includes environmental wind speed and rainfall intensity, and the geological data includes surface micro-vibration frequency and terrain slope.

[0050] Specifically, by coupling wind speed, rainfall, geological micro-vibration, and slope data, a multi-dimensional risk assessment model is constructed. Compared with traditional single meteorological assessment, it can simultaneously perceive geological hidden dangers and dynamically generate hierarchical protection strategies. For example, when the potential impact of slope on vehicle stability and heavy rainfall form a collaborative risk, higher-level counterweight adjustment and power limitation can be triggered to improve the adaptability to complex working conditions.

[0051] Specifically, the evaluation method for the environmental risk level is as Figure 2 shown, including:

[0052] Step S201, perform a coupling analysis on the ambient wind speed and rainfall intensity collected during the monitoring period to construct a meteorological impact factor.

[0053] Specifically, the construction process of the meteorological impact factor is as follows:

[0054] Compare and analyze the ambient wind speed v0 and the wind speed discrimination factor v1 collected during the monitoring period. If the ambient wind speed v0 is greater than or equal to the wind speed discrimination factor v1, the expression of the wind speed factor Fy is Fy = ln{[(v0 - v1) / v0] 2 + 1}, if the ambient wind speed v0 is less than the wind speed discrimination factor v1, the expression of the wind speed factor Fy is Fy = 0;

[0055] Perform a coupling analysis on the wind speed factor Fy and the rainfall intensity j0 to construct a meteorological impact factor Q. The expression of the meteorological impact factor Q is Q = Fy×(j0 / J), where J is the rainfall intensity threshold.

[0056] Specifically, through the non-linear coupling modeling of the ambient wind speed and rainfall intensity, the comprehensive impact of meteorological extreme events can be effectively quantified. Compared with the traditional single-parameter threshold method, it can identify the wind-rain synergistic effect and avoid misjudgment of isolated parameters.

[0057] Step S202, construct a geological impact factor based on the geological data collected during the monitoring period.

[0058] Specifically, in step S202, perform a coupling analysis on the surface micro-vibration frequency z0 and the terrain slope p0 collected during the monitoring period to construct a geological impact factor Zy. The expression of Zy is Zy = x1×(z0 / Z) 1.2 + x2×ln[3×p0 / P + 1] / ln4, where Z is the vibration frequency threshold, P is the slope threshold, x1 is the vibration weight, x2 is the slope weight, and x1 + x2 = 1.

[0059] Specifically, by combining the weighted calculation of the surface micro-vibration frequency and slope index, geological hazards (such as enhanced micro-vibrations in the precursor of landslides) can be keenly perceived. Through dynamic weight allocation, the contribution differences of different geological risks are highlighted.

[0060] Step S203, perform a coupling analysis on the meteorological impact factor and the geological impact factor to determine the environmental risk level, and generate a power protection strategy according to the environmental risk level.

[0061] Specifically, in step S203, construct a risk index FX based on the meteorological impact factor Q and the geological impact factor Zy. The expression of FX is FX = w1×Q + w2×Zy, where w1 is the meteorological weight, w2 is the geological weight, and w1 + w2 = 1;

[0062] Compare the risk index FX with the risk discrimination factors y1 and y2 to determine the environmental risk level. When the risk index FX is less than the risk discrimination factor y1, the environmental risk level is determined to be level one, and no power protection strategy is adopted. When the environmental risk index FX is greater than or equal to the risk discrimination factor y1 and the environmental risk index FX is less than or equal to the risk discrimination factor y2, the environmental risk level is determined to be level two, and dynamic counterweight is enabled. When the risk index FX is greater than the risk discrimination factor y2, the environmental risk level is determined to be level three, and the engine output power is limited to α of the rated power, where α is the limit index.

[0063] Specifically, the risk index through multi-factor fusion supports a hierarchical response strategy. Only data records are made for level one risk to reduce the energy consumption of unnecessary protection actions. For level two risk, dynamic counterweight adjustment can balance the vehicle's stability and mobility, avoiding the inefficiency of traditional fixed counterweights. For level three risk, the elastic limit of the engine power takes into account both the emergency power supply demand and equipment protection to ensure sustainable operation under extreme conditions.

[0064] Exemplarily, the environmental wind speed is collected in real time by an ultrasonic anemometer, with the best range covering 0 - 50 m / s. The wind speed discrimination factor v1 is recommended to be set to 60% - 70% of the safe wind speed range (e.g., taking 12 m / s). The rainfall intensity is dynamically monitored by a tipping bucket rain gauge or a laser disdrometer. The rainfall intensity threshold J is set according to historical meteorological data (e.g., taking the heavy rain warning standard of 50 mm / h). The surface micro-vibration frequency can deploy triaxial geological vibration sensors, and the vibration frequency threshold Z can be set to 10 - 20 Hz. The terrain slope can be measured by a high-precision inclinometer or lidar terrain scanning, and the slope threshold P can be set to 18° according to the vehicle's anti-rollover design parameters. The vibration weight and slope weight can be allocated according to 6:4, and the meteorological weight and geological weight can be allocated according to 5.5:4.5. The risk discrimination factors y1 and y2 can be set to 0.2 and 0.4, and the limit index α can be set to 0.6. In this embodiment, no specific limitations are imposed on the acquisition methods and threshold values of the above data, and those skilled in the art can freely set them according to actual needs.

[0065] Exemplarily, when the emergency energy vehicle is performing a power supply task in the mountainous area, the collected environmental wind speed v0 = 15 m / s, which exceeds the wind speed discrimination factor v1 = 12 m / s, triggering the calculation of the wind speed factor. The wind speed factor Fy = ln{[(15 - 12) / 15]² + 1} = 0.039. At the same time, the collected rainfall intensity j0 = 60 mm / h, and the rainfall intensity threshold is 50 mm / h, then the meteorological influence factor Q = Fy×(j0 / J) = 0.0468. The wind-rain synergy effect is quantified through a non-linear model to avoid misjudgment of isolated parameters.

[0066] Collect geological data. The surface micro-vibration frequency z0 = 18 Hz is collected, and the vibration frequency threshold Z is 15 Hz, indicating a potential landslide risk. The terrain slope p0 = 20° is collected, and the slope threshold P = 18°, significantly increasing the probability of vehicle rollover. Construct the geological influence factor Zy = 0.6×(18 / 15)^1.2 + 0.4×ln[3×20 / 18 + 1] / ln4 = 1.332;

[0067] The risk index FX = 0.55×0.0468 + 0.45×1.332 = 0.625. Judgment: FX = 0.625 is greater than y2 = 0.4, triggering a level-three risk. Limit the engine power to α = 0.6 of the rated value to balance the power supply demand and equipment protection under extreme conditions and avoid system collapse caused by overload.

[0068] Please continue to refer to Figure 1 As shown, the intelligent energy management method for the emergency energy vehicle further includes:

[0069] Step S102, collect cable data during the monitoring period, and construct a force-thermal coupling model based on the collected cable data to judge the cable status, and update the power protection strategy according to the cable status. The cable data includes cable tension, core temperature, and swing angle.

[0070] Specifically, the construction process of the force-thermal coupling model is as follows:

[0071] S={T load ×[1 + lg(t / te + 1)] / T max}×[1.2×(θ / θy) 2 ;

[0072] The S is the force-thermal coupling index, the T max is the rated breaking tension, the T load is the cable tension, the t is the core temperature, the te is the temperature threshold, the θ is the swing angle, and the θy is the angle threshold;

[0073] Compare the force-thermal coupling index S with the adjustment discriminant factor c to judge the cable status. If the force-thermal coupling index S is greater than the adjustment discriminant factor c, it is determined that the cable status is abnormal in the current monitoring period. Otherwise, it is determined that the cable status is normal in the current monitoring period. When the cable status is abnormal and the risk level is level three, update the limit index to α1, and set α1 = α×exp[c - S].

[0074] Specifically, introduce a tension-temperature-swing coupling model, conduct a correlation analysis of mechanical stress and thermal effects. Compared with independently monitoring single parameters, it can identify the abnormal cable status in advance (such as material creep caused by local overheating), and intelligently optimize the power limit strategy in combination with the risk level to avoid overload or fatigue fracture.

[0075] Exemplarily, the cable tension can be used to monitor the real-time tension change through a fiber Bragg grating sensor. The rated breaking tension is based on the rated value of the cable's tensile strength (for example, for a steel core cable, 80% of the breaking tension is taken as the threshold). The temperature of the wire core can be collected through a distributed optical fiber temperature measurement system or a thermistor patch. The temperature threshold te can be set to 90°C (under normal conditions) + 5°C redundancy. The swing angle can be dynamically detected through an inertial measurement unit (IMU). The angle threshold θy is set according to the cable's expansion and contraction margin, such as being set to 15°. The adjustment discrimination factor can be set to 0.68. In this embodiment, the collection methods and threshold values of the above data are not specifically limited, and those skilled in the art can freely set them according to actual needs.

[0076] Exemplarily, the collected cable data is 80 kN, the wire core temperature is 95°C, the swing angle is 12°, the rated breaking tension is 100 kN, the temperature threshold te is 95°C, and the angle threshold θy is 15°. And a force-thermal coupling index S is constructed, S = {80×[1 + lg(95 / 90 + 1))] / 100}×[1.2×(12 / 15)²] = 0.628, which is less than the adjustment discrimination factor c = 0.68. There is no need to additionally limit the power. Since the cable state is normal and the environmental risk level is three, the original limit index α = 0.6 is maintained.

[0077] Please continue to refer to Figure 1 as shown, the intelligent energy management method for the emergency energy vehicle further includes:

[0078] Step S103, comparing the cable swing frequency collected within the monitoring period with the frequency discrimination factor to optimize the judgment process of the cable state.

[0079] Specifically, when optimizing the judgment process of the cable state, the cable swing frequency bp collected within the monitoring period is compared with the frequency discrimination factor B. If the cable swing frequency is greater than the frequency discrimination factor B, the judgment process of the cable state is optimized, and the adjustment discrimination factor is optimized to c1. The expression of c1 is c1 = c×{1 - exp[3×(bp - B) / (B + bp) - 3]}.

[0080] Specifically, by adding the monitoring of the cable swing frequency and optimizing the discrimination logic, the monitoring sensitivity is improved through frequency domain feature recognition (such as resonance risk).

[0081] Exemplarily, the swing frequency can be collected through a vibration acceleration sensor, and the threshold B can be set to 2.5 Hz. In this embodiment, the collection methods and threshold values of the above data are not specifically limited, and those skilled in the art can freely set them according to actual needs.

[0082] Exemplarily, the collected cable swing frequency bp = 3 Hz, which is greater than the frequency discrimination factor B = 2.5 Hz. It is necessary to optimize and adjust the discrimination factor. The calculation of the optimized discrimination factor c1 is as follows:

[0083] c1 = c × {1 - exp[3 × (bp - B) / (B + bp) - 3]} = 0.68 × {1 - exp[3 × (3 - 2.5) / (2.5 + 3) - 3]} = 0.636; Since S = 0.628 is less than c1 = 0.636, it is determined that the cable status is normal and no additional power restriction is required. At the same time, since the cable status is normal and the environmental risk level is still level three, the original restriction index α = 60% is maintained. When the swing frequency exceeds the threshold, the discrimination logic is dynamically adjusted through an exponential function to reduce the false alarm rate.

[0084] Please continue to refer to Figure 1 As shown, the intelligent energy management method of the emergency energy vehicle further includes:

[0085] Step 104, jointly analyze the dust concentration in the battery compartment and the temperature difference inside and outside the battery compartment collected during the monitoring period to determine the battery compartment anomaly index, monitor the impedance of the battery compartment to determine the impedance anomaly index, combine the battery compartment anomaly index and the impedance anomaly index to judge the battery status, and generate a discharge protection strategy according to the battery status.

[0086] Specifically, by fusing dust concentration, temperature difference and impedance heterogeneity data, a mapping relationship between the environment and electrical performance is established. Through multi-dimensional anomaly detection, the risk of battery aging or short circuit is accurately judged, and a differentiated derating strategy is generated to ensure power supply continuity and avoid sudden failures.

[0087] Please refer to Figure 3 As shown, the method for generating the discharge protection strategy includes:

[0088] Step S301, jointly analyze the dust concentration in the battery compartment and the temperature difference inside and outside the battery compartment collected during the monitoring period to determine the battery compartment anomaly index.

[0089] Specifically, when determining the battery compartment anomaly index, the dust concentration n0 in the battery compartment, the dust discrimination factor n1, the temperature difference Δt inside and outside the battery compartment, and the temperature difference discrimination factor Δty are used to construct the battery compartment anomaly index k. The expression of k is k = ln{[(n0 / n1) × lg(Δt / Δty) 2 + 1} / ln2.

[0090] Specifically, through logarithmic correlation modeling of dust concentration and temperature difference, the potential correlation between environmental pollution and thermal failure is revealed (such as dust accumulation leading to a decrease in heat dissipation efficiency). Compared with single-threshold alarm, it can identify progressive anomalies and provide data support for preventive maintenance.

[0091] Please continue to refer toFigure 3 As shown in the figure, the method for generating the discharge protection strategy further includes:

[0092] Step S302: Monitor the impedance of the battery compartment to determine the impedance anomaly index.

[0093] Specifically, when determining the impedance anomaly index, the battery compartment is evenly divided into n regions, and the impedance of the i-th region is denoted as Gi; an impedance anomaly index ZY is constructed based on the impedances of each region, and the expression of ZY is ; where △Z is the fluctuation discrimination threshold.

[0094] Specifically, by locating local defects of the battery pack (such as sudden increase in impedance caused by poor contact of single cells), compared with the overall impedance detection, the sensitivity to early faults, such as imbalance between battery clusters, is significantly improved.

[0095] Exemplarily, the dust concentration can be collected by a light scattering dust sensor deployed at the air inlet of the battery compartment. The dust discrimination factor n1 is set according to the conductive risk of particulate matter, such as set to 5 mg / m³. The temperature difference △t can be monitored by a dual-redundancy infrared temperature measurement module for the internal and external temperature difference. The temperature difference discrimination factor △ty is dynamically adjusted according to the heat dissipation performance, such as set to 20 °C; in this embodiment, the collection methods and threshold values of the above data are not specifically limited, and those skilled in the art can freely set them according to actual needs.

[0096] Exemplarily, in this embodiment, the collection method of the impedance of each region is not specifically limited, and those skilled in the art can freely set it according to requirements. Among them, when collecting the impedance of the i-th region, a carbon nanotube conductive layer can be coated on the inner wall of the battery compartment, and it is divided into n regions by the orthogonal grid division method. The impedance of each region is collected by a four-wire patch electrode and a temperature compensation sensor. The fluctuation discrimination threshold can be set to 0.3; in this embodiment, the collection methods and threshold values of the above data are not specifically limited, and those skilled in the art can freely set them according to actual needs.

[0097] Please continue to refer to Figure 3 As shown in the figure, the method for generating the discharge protection strategy further includes:

[0098] Step S303: Perform data fusion on the battery compartment anomaly index and the impedance anomaly index to judge the battery state, and generate a discharge protection strategy according to the battery state.

[0099] Specifically, when generating the discharge protection strategy, data fusion is performed on the battery compartment anomaly index k and the impedance anomaly index ZY to determine the battery state index ZT. ZT is the sum of the product of the battery compartment weight L1 and the battery compartment anomaly index k and the product of the impedance weight L2 and the impedance anomaly index ZY;

[0100] Compare the battery state index ZT with the battery state discrimination factor zt. When the battery state index ZT is greater than the battery state discrimination factor zt, it is determined that the battery state is abnormal in the current monitoring period; otherwise, it is determined that the battery state is normal in the current monitoring period. When the battery state is abnormal, the discharge rate is derated to β × the rated discharge rate, where β is the discharge limit factor.

[0101] Specifically, the battery state index based on multi-source data fusion supports refined derating control, adjusts the derating ratio in abnormal states, avoids the power supply interruption risk of traditional "full shutdown", and extends the power supply duration on the premise of safety.

[0102] Exemplarily, the weight of the battery compartment can be set to 0.7, the weight of the impedance can be set to 0.3, the discharge limit factor can be set to 0.7, and the battery state discrimination factor can be set to 0.2; in this embodiment, no specific limitations are made on the values of the above thresholds, and those skilled in the art can freely set them according to actual needs.

[0103] Exemplarily, the battery compartment is divided into 10 regions, and the impedance data (unit: Ω):

[0104] 50, 52, 51, 49, 50, 70, 48, 50, 53, 49, and the calculated ZY = 0.374;

[0105] The collected dust concentration n0 = 6 mg / m³, the temperature difference Δt = 25 °C, and the dust discrimination factor n1 = 5 mg / m³ and the temperature difference discrimination factor △ty = 20 °C are set. The calculated battery compartment abnormal index k = ln{[(6 / 5) × lg(25 / 20)]² + 1} / 0.693 = 0.019. Based on ZY and k, the battery state index ZT = 0.7 × 0.019 + 0.3 × 0.374 = 0.1255, which is less than the battery state discrimination factor zt = 0.2, and the discharge rate is not derated to β = 0.7 times the rated value.

[0106] Please continue to refer to Figure 1 As shown, the intelligent energy management method for the emergency energy vehicle further includes:

[0107] Step S105, comprehensively analyze the environmental risk level, cable state, and battery state in each monitoring period within the management cycle to determine the safety state of the emergency energy vehicle and give a safety warning to the user.

[0108] Specifically, when determining the status of the emergency energy vehicle, a comprehensive analysis is performed on the environmental risk level, cable status, and battery status in each monitoring period within the management cycle to determine the safety index R. The expression of R is R = m1×d1 / D + m2×d2 / D + m3×d3 / D, where m1 is the environmental risk weight, m2 is the cable weight, m3 is the battery weight, m1 + m2 + m3 = 1, d1 is the number of monitoring periods with an environmental risk level of level 2 or 3 within the management cycle, d2 is the number of monitoring periods with abnormal cable status within the management cycle, d3 is the number of monitoring periods with abnormal battery status within the management cycle, and D is the number of monitoring periods within the management cycle;

[0109] Compare the safety index R with the safety discrimination factor R0 to determine the safety status of the emergency energy vehicle. If the safety index R is greater than or equal to the safety discrimination factor R0, it is determined that the safety status of the emergency energy vehicle in the current management cycle is abnormal; otherwise, it is determined that the safety status of the emergency energy vehicle in the current management cycle is normal. When the safety status of the emergency energy vehicle is abnormal, a safety warning is sent to the user.

[0110] Specifically, through the cumulative risk ratio analysis within the management cycle, the global safety index R can identify the long-term equipment degradation trend. The multi-weight composite analysis supports the priority division and can match the operation and maintenance focus of different scenarios, significantly improving the active protection ability of the system.

[0111] Exemplarily, the environmental risk weight can be set to 0.4, the cable weight can be set to 0.2, the battery weight can be set to 0.4, and the safety discrimination factor can be set to 0.24; in this embodiment, no specific limitations are imposed on the above threshold values, and those skilled in the art can freely set them according to actual needs.

[0112] Exemplarily, in this embodiment, the monitoring period can be set to 1 min, and the management cycle can be set to 1 h. In this embodiment, no specific limitations are imposed on the settings of the monitoring period and the management cycle, and those skilled in the art can freely set them.

[0113] Exemplarily, data within the management cycle (1 hour, monitored once per minute): environmental risk level: 3 times at level 3 and 2 times at level 2; cable status abnormal: 4 times; battery status abnormal: 5 times; total monitoring period D = 60;

[0114] Calculation of the safety index R: R = 0.4×(5 / 60) + 0.2×(4 / 60) + 0.4×(5 / 60) = 0.079; Judgment: safety threshold R0 = 0.24, R = 0.079 is less than R0 = 0.24, the safety status is normal, no warning is required, and through historical data analysis, the long-term equipment degradation trend is identified to support preventive maintenance.

[0115] Please continue to refer to Figure 4 as shown, which is a schematic structural diagram of an intelligent energy management device for an emergency energy vehicle, including:

[0116] A power protection unit 501, configured to perform coupled analysis on meteorological data and geological data collected within a monitoring period to determine an environmental risk level, and generate a power protection strategy according to the environmental risk level;

[0117] A cable monitoring unit 502, configured to collect cable data within a monitoring period, construct a force-thermal coupling model based on the collected cable data to judge the cable state, and update the power protection strategy according to the cable state;

[0118] A frequency monitoring unit 503, configured to compare the cable swing frequency and the frequency discrimination factor collected within a monitoring period to optimize the judgment process of the cable state;

[0119] A discharge protection unit 504, configured to perform joint analysis on the dust concentration in the battery compartment and the temperature difference inside and outside the battery compartment collected within a monitoring period to determine the battery compartment anomaly index, monitor the impedance of the battery compartment to determine the impedance anomaly index, combine the battery compartment anomaly index and the impedance anomaly index to judge the battery state, and generate a discharge protection strategy according to the battery state;

[0120] A safety warning unit 505, configured to perform comprehensive analysis on the environmental risk level, cable state, and battery state of each monitoring period within a management period to determine the safety state of the emergency energy vehicle and give a safety warning to the user.

[0121] The embodiment of the present application further provides an electronic device, which is used to execute the intelligent energy management method for the emergency energy vehicle. As Figure 5 shown, the electronic device includes: a processor 601, a memory 602, a communication interface 603, and a system bus 604. The processor includes at least one of a central processing unit (CPU), a graphics processing unit (GPU), or a field programmable gate array (FPGA), and is configured to call computer programs and data stored in the memory and generate control instructions; the memory includes a random access memory (RAM) and / or a non-volatile memory (NVM), and the NVM includes flash memory, a solid state drive (SSD), or a combination thereof, and is used to store computer programs, intermediate processing data, and a historical data set; the communication interface includes a wired communication module and a wireless communication module, the wired communication module supports Ethernet or RS-485 protocols and is used to connect to a sensor network; the wireless communication module supports LoRa, 5G, or satellite communication protocols and is used to transmit processing results to a remote server; the system bus adopts a PCI Express or AXI bus architecture to achieve high-speed data interaction and clock synchronization between the processor, the memory, and the communication interface.

[0122] An embodiment of the present application further provides a computer-readable storage medium, on which computer program code is stored. When the program code is loaded into a processor through an integrated circuit carrier board and written into a memory via a system bus.

[0123] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, rather than limitations on the implementation manners of the present invention. For those of ordinary skill in the art, other different forms of changes or variations can be made based on the above description. It is impossible to list all the implementation manners here. Any obvious changes or variations derived from the technical solutions of the present invention still fall within the protection scope of the present invention.

Claims

1. An intelligent energy management method for emergency energy vehicles, characterized in that: include: Conduct coupled analysis on meteorological data and geological data collected during the monitoring period to determine the environmental risk level, and generate power protection strategies based on the environmental risk level; Collect cable data within the monitoring period, and build a mechanical-thermal coupling model based on the collected cable data to determine the cable status and update the power protection strategy based on the cable status; The dust concentration in the battery compartment and the temperature difference inside and outside the battery compartment collected during the monitoring period are jointly analyzed to determine the battery compartment abnormality index. The impedance of the battery compartment is monitored to determine the impedance abnormality index. The battery status is judged by combining the battery compartment abnormality index and the impedance abnormality index, and a discharge protection strategy is generated according to the battery status. The generation method of the discharge protection strategy includes: Combined analysis of dust concentration in the battery compartment and temperature difference inside and outside the battery compartment collected during the monitoring period is performed to determine the abnormal index of the battery compartment; Monitor the impedance of the battery compartment to determine the impedance abnormality index; The battery compartment abnormality index and the impedance abnormality index are integrated to determine the battery status and generate a discharge protection strategy based on the battery status; When determining the battery compartment abnormality index, the battery compartment abnormality index k is constructed by combining the dust concentration n0 in the battery compartment, the dust discrimination factor n1, the temperature difference △t inside and outside the battery compartment, and the temperature difference discrimination factor △ty. The expression of k is k=ln{[(n0 / n1)×lg(△t / △ty)]2+1} / ln2; When determining the impedance anomaly index, the battery compartment is evenly divided into n areas, and the impedance of the i-th area is recorded as Gi; the impedance anomaly index ZY is constructed according to the impedance of each area, and the expression of ZY is: ; The △Z is the fluctuation discrimination threshold; When generating a discharge protection strategy, data fusion is performed on the battery compartment abnormality index k and the impedance abnormality index ZY to determine the battery state index ZT, where ZT is the sum of the product of the battery compartment weight L1 and the battery compartment abnormality index k and the product of the impedance weight L2 and the impedance abnormality index ZY; The battery state index ZT and the battery state discrimination factor zt are compared. When the battery state index ZT is greater than the battery state discrimination factor zt, it is determined that the battery state of the current monitoring cycle is abnormal. Otherwise, it is determined that the battery state of the current monitoring cycle is normal. When the battery state is abnormal, the discharge rate is reduced to β×rated discharge rate, where β is the discharge limiting factor.

2. The intelligent energy management method for emergency energy vehicles according to claim 1 is characterized in that: The assessment method of environmental risk level includes: coupling analysis of environmental wind speed v0 and rainfall intensity j0 collected during the monitoring period to construct meteorological impact factors; The construction process of the meteorological impact factor is as follows: Compare and analyze the ambient wind speed v0 and wind speed discrimination factor v1 collected during the monitoring period. If the ambient wind speed v0 is greater than or equal to the wind speed discrimination factor v1, the expression of the wind speed factor Fy is Fy=ln{[(v0-v1) / v0] 2 +1}, if the ambient wind speed v0 is less than the wind speed discrimination factor v1, the expression of the wind speed factor Fy is Fy=0; The wind speed factor Fy and the rainfall intensity j0 are coupled and analyzed to construct the meteorological impact factor Q. The expression of the meteorological impact factor Q is Q=Fy×(j0 / J), where J is the rainfall intensity threshold.

3. The intelligent energy management method for emergency energy vehicles according to claim 2 is characterized in that: The environmental risk level assessment method further includes: constructing a geological impact factor based on geological data collected during the monitoring period, and performing coupling analysis on the surface micro-vibration frequency z0 and the terrain slope p0 collected during the monitoring period to construct a geological impact factor Zy, where the expression of Zy is Zy=x1×(z0 / Z) 1.2 +x2×ln[3×p0 / P+1] / ln4, Z is the vibration frequency threshold, P is the slope threshold, x1 is the vibration weight, and x2 is the slope weight.

4. The intelligent energy management method for emergency energy vehicles according to claim 3 is characterized in that: The environmental risk level assessment method further includes: performing coupling analysis on meteorological influencing factors and geological influencing factors to determine the environmental risk level, and generating a power protection strategy according to the environmental risk level; The risk index FX is constructed according to the meteorological influence factor Q and the geological influence factor Zy. The expression of FX is FX=w1×Q+w2×Zy, where w1 is the meteorological weight and w2 is the geological weight. The risk index FX is compared with the risk discrimination factors y1 and y2 to determine the environmental risk level. When the risk index FX is less than the risk discrimination factor y1, the environmental risk level is determined to be level one, and no power protection strategy is adopted. When the environmental risk index FX is greater than or equal to the risk discrimination factor y1 and the environmental risk index FX is less than or equal to the risk discrimination factor y2, the environmental risk level is determined to be level two, and dynamic weighting is enabled. When the risk index FX is greater than the risk discrimination factor y2, the environmental risk level is determined to be level three, and the engine output power is limited to α of the rated power, where α is the limiting index.

5. The intelligent energy management method for emergency energy vehicles according to claim 4 is characterized in that: The construction process of the mechanical-thermal coupling model is as follows: S={T load ×[1+lg(t / te+1)] / T max }×[1.2×(θ / θy) 2 ]; S is the mechanical-thermal coupling index, T max is the rated breaking tension, the T load is the cable tension, t is the core temperature, te is the temperature threshold, θ is the swing angle, and θy is the angle threshold; The force-thermal coupling index S and the adjustment discrimination factor c are compared to determine the cable status. If the force-thermal coupling index S is greater than the adjustment discrimination factor c, the cable status in the current monitoring period is determined to be abnormal. Otherwise, the cable status in the current monitoring period is determined to be normal. When the cable status is abnormal and the risk level is level three, the limit index is updated to α1.

6. The intelligent energy management method for emergency energy vehicles according to claim 5 is characterized in that: Also includes: Compare the cable swing frequency collected during the monitoring period with the frequency discrimination factor to optimize the cable status judgment process; When optimizing the cable status judgment process, the cable swing frequency bp collected during the monitoring period is compared with the frequency discrimination factor B. If the cable swing frequency is greater than the frequency discrimination factor B, the cable status judgment process is optimized and the adjustment discrimination factor is optimized to c1.

7. The intelligent energy management method for emergency energy vehicles according to claim 6 is characterized in that: Also includes: Comprehensively analyze the environmental risk level, cable status, and battery status of each monitoring period within the management cycle to determine the safety status of the emergency energy vehicle and provide safety warnings to users; When determining the status of the emergency energy vehicle, a comprehensive analysis is conducted on the environmental risk level, cable status and battery status of each monitoring period within the management period to determine the safety index R. The safety index R and the safety judgment factor R0 are compared to determine the safety status of the emergency energy vehicle. If the safety index R is greater than or equal to the safety judgment factor R0, the safety status of the emergency energy vehicle in the current management period is determined to be abnormal. Otherwise, the safety status of the emergency energy vehicle in the current management period is determined to be normal. When the safety status of the emergency energy vehicle is abnormal, a safety warning is issued to the user.

8. An intelligent energy management device for emergency energy vehicles, characterized in that: include: The power protection unit is used to perform coupled analysis on the meteorological data and geological data collected during the monitoring period to determine the environmental risk level, and generate a power protection strategy based on the environmental risk level; The cable monitoring unit is used to collect cable data within the monitoring period and build a mechanical-thermal coupling model based on the collected cable data to determine the cable status and update the power protection strategy based on the cable status; The frequency monitoring unit is used to compare the cable swing frequency collected during the monitoring period with the frequency discrimination factor to optimize the cable status judgment process; The discharge protection unit is used to jointly analyze the dust concentration in the battery compartment and the temperature difference between the inside and outside of the battery compartment collected during the monitoring period to determine the battery compartment abnormality index, monitor the battery compartment impedance to determine the impedance abnormality index, judge the battery state by combining the battery compartment abnormality index and the impedance abnormality index, and generate a discharge protection strategy according to the battery state; The safety warning unit is used to conduct a comprehensive analysis of the environmental risk level, cable status and battery status of each monitoring period within the management period to determine the safety status of the emergency energy vehicle and provide safety warnings to users.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program is used to control the electronic device where the computer-readable storage medium is located to execute the intelligent energy management method for emergency energy vehicles according to any one of claims 1-7 during operation.

Images