Intelligent energy management method and device for emergency energy vehicle and storage medium

Abstract

The invention relates to the technical field of energy management, in particular to an intelligent energy management method and device for an emergency energy vehicle and a storage medium, and the method comprises the steps: determining an environmental risk level, and generating a power protection strategy according to the environmental risk level; judging a cable state, and updating a power protection strategy according to the cable state; the judging process of the cable state is optimized; performing conjoint analysis on the dust concentration in the battery compartment and the temperature difference inside and outside the battery compartment collected in the monitoring period to determine the abnormal index of the battery compartment, monitoring the impedance of the battery compartment to determine the impedance abnormal index, judging the battery state by combining the abnormal index of the battery compartment and the impedance abnormal index, and generating a discharge protection strategy according to the battery state; and comprehensively analyzing the environmental risk level, the cable state and the battery state of each monitoring period in the management period to determine the safety state of the emergency energy vehicle, and performing safety early warning to a user. According to the invention, the energy management efficiency and safety of the emergency energy vehicle are effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of energy management technology, and in particular 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 over-limit triggering protection mechanism, mechanical protection based only on cable tension, etc., which have defects such as lack of environmental risk coupling analysis, extensive equipment status assessment and rigid protection strategy. To address 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 assessment, so as to achieve accurate risk perception, three-dimensional diagnosis of equipment status and dynamic optimization of protection strategies, and ensure the reliable operation and efficient power supply of emergency energy vehicles under complex working conditions.

[0003] Chinese patent publication number CN119261595A discloses an efficient power and energy management system for emergency energy vehicles, 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 a stable supply of hydrogen. The fuel cell converts hydrogen into electric energy to drive the vehicle. The excess electric energy is stored in the battery pack. The energy management module optimizes energy distribution and supply according to the operating status and energy demand of the vehicle. The present invention significantly improves the energy efficiency and reliability of the vehicle through the integration of multiple energy supply units, dynamic adjustment of intelligent management modules, energy prediction and optimization, and the use of regenerative braking systems; it can be seen that the 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 collaborative control logic, and there are problems of low efficiency and low safety in the energy management of emergency energy vehicles. Summary of the invention

[0004] The object 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 object, the present invention adopts the following technical solutions: An intelligent energy management method for an emergency energy vehicle, comprising: 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 battery compartment impedance 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 based on the battery status.

[0006] Optionally, the environmental risk level assessment method includes: performing coupling analysis on the environmental wind speed v0 and rainfall intensity j0 collected during the monitoring period to construct a meteorological impact factor; 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.

[0007] Optionally, the environmental risk level assessment method further includes: constructing a geological influence 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 influence 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.

[0008] Optionally, 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.

[0009] Optionally, 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 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.

[0010] Optionally, it also includes: comparing 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, comparing the cable swing frequency bp collected during the monitoring period with the frequency discrimination factor B, if the cable swing frequency is greater than the frequency discrimination factor B, optimizing the cable status judgment process, and adjusting the discrimination factor to c1.

[0011] Optionally, the method for generating 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.

[0012] Optionally, it also includes: conducting 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 issuing a safety warning to the user; when determining the status of the emergency energy vehicle, conducting 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 index R, comparing the safety index R with the safety judgment 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 judgment factor R0, then it is determined that the safety status of the emergency energy vehicle in the current management period is abnormal, otherwise, it is determined that the safety status of the emergency energy vehicle in the current management period is normal, and when the safety status of the emergency energy vehicle is abnormal, a safety warning is issued to the user.

[0013] According to another aspect of the present application, there is provided an intelligent energy management device for an emergency energy vehicle, comprising: 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.

[0014] According to another aspect of the present application, a computer-readable storage medium is provided, 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 during operation.

[0015] The beneficial effects of the present invention are as follows: Through the collaborative analysis of multi-source data and the intelligent decision-making mechanism, a closed-loop management system for the entire chain of emergency energy vehicles is constructed. At the environmental perception layer, the coupling of 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 collapse and landslides. At the equipment status layer, the mechanical-thermal-mechanical coupling model and the multi-dimensional battery diagnostic technology work together to achieve an upgrade from a single fault alarm to potential risk tracing; at the control strategy layer, dynamic weight adjustment and non-linear threshold optimization algorithms enable protective measures to more accurately adapt to actual working conditions and avoid energy waste caused by excessive intervention; global safety index calculation combined with historical data analysis supports the extension from real-time emergency response to preventive maintenance. The overall solution has been upgraded through intelligentization to enhance equipment reliability in extreme environments, optimize energy utilization efficiency, and provide multi-dimensional data support for operation and maintenance decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 It is a flow chart of the intelligent energy management method for emergency energy vehicles of this embodiment.

[0018] Figure 2 Schematic diagram of the flow of the environmental risk level assessment method of this embodiment.

[0019] Figure 3Schematic diagram of the flow of the method for generating the discharge protection strategy of this embodiment.

[0020] Figure 4 It is a structural schematic diagram of the intelligent energy management device for emergency energy vehicles in this embodiment.

[0021] Figure 5 Schematic diagram of the structure of the electronic device of this embodiment. DETAILED DESCRIPTION

[0022] In order to more clearly illustrate the present invention, the present invention is further described below in conjunction with preferred embodiments and accompanying drawings. Similar components in the accompanying drawings are represented by the same reference numerals. It should be understood by those skilled in the art that the content specifically described below is illustrative rather than restrictive, and should not be used to limit the scope of protection of the present invention.

[0023] It should be noted that, although the terms first, second, third, etc. may be used to describe in the embodiments of the present application, 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.

[0024] See also Figure 1 As shown, it is a flow chart of the intelligent energy management method for emergency energy vehicles of this embodiment, including: Step S101, coupling analysis is performed on meteorological data and geological data collected during the monitoring period to determine the environmental risk level, and a power protection strategy is generated according to the environmental risk level, wherein the meteorological data includes environmental wind speed and rainfall intensity, and the geological data includes surface micro-vibration frequency and terrain slope.

[0025] Specifically, by coupling wind speed, rainfall, geological micro-vibration and slope data, a multi-dimensional risk assessment model is constructed. Compared with the traditional single meteorological assessment, it can simultaneously perceive geological hazards and dynamically generate graded protection strategies. For example, when the potential impact of slope on vehicle stability and heavy rainfall form a synergistic risk, it can trigger a higher level of counterweight adjustment and power limitation to improve adaptability to complex working conditions.

[0026] Specifically, the environmental risk level assessment method is as follows: Figure 2 As shown, including: Step S201 , performing coupling analysis on the ambient wind speed and rainfall intensity collected during the monitoring period to construct a meteorological influencing factor.

[0027] Specifically, 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.

[0028] Specifically, through the nonlinear coupling modeling of ambient wind speed and rainfall intensity, the comprehensive impact of extreme meteorological events can be effectively quantified. Compared with the traditional single-parameter threshold method, it can identify the synergistic effect of wind and rain and avoid the misjudgment of isolated parameters.

[0029] Step S202, constructing geological impact factors based on geological data collected during the monitoring period.

[0030] Specifically, step S202 performs coupling analysis on the surface microvibration frequency z0 and the terrain slope p0 collected during the monitoring period to construct a geological influence 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, x2 is the slope weight, x1+x2=1.

[0031] Specifically, by combining the weighted calculation of surface microvibration frequency and slope index, geological hazards (such as enhanced microvibration as a precursor to landslides) can be keenly perceived, and the differences in contributions of different geological risks can be highlighted through dynamic weight allocation.

[0032] Step S203 , coupling analysis is performed on meteorological influence factors and geological influence factors to determine the environmental risk level, and a power protection strategy is generated according to the environmental risk level.

[0033] Specifically, the step S203 constructs a risk index FX according to the meteorological influence factor Q and the geological influence factor Zy, and the expression of FX is FX=w1×Q+w2×Zy, wherein w1 is the meteorological weight, w2 is the geological weight, and w1+w2=1; 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.

[0034] Specifically, a multi-factor fusion risk index supports a graded response strategy. At the first level of risk, only data is recorded to reduce the energy consumption of unnecessary protection actions. At the second level of risk, dynamic weight adjustment can balance vehicle stability and maneuverability and avoid the inefficiency of traditional fixed weight. At the third level of risk, the elasticity of engine power is limited to take into account emergency power supply needs and equipment protection to ensure sustainable operation under extreme conditions.

[0035] Exemplarily, the ambient wind speed is collected in real time by an ultrasonic anemometer, and the optimal range covers 0~50m / s. The wind speed discrimination factor v1 is recommended to be set to 60%~70% of the safe wind speed range (such as 12m / s). The rainfall intensity is dynamically monitored by a tipping bucket rain sensor or a laser raindrop spectrometer. The rainfall intensity threshold J is set according to historical meteorological data (such as the heavy rain warning standard of 50mm / h). The surface micro-vibration frequency can be deployed with a three-axis geological vibration sensor, and the vibration frequency threshold Z can be set to 10~20Hz. The terrain slope can be scanned by a high-precision inclinometer or a lidar terrain scan, the slope threshold P can be set to 18° according to the vehicle's anti-rollover design parameters, the vibration weight and the slope weight can be allocated in a ratio of 6:4, the meteorological weight and the geological weight can be allocated in a ratio of 5.5:4.5, the risk discrimination factors y1 and y2 can be set to 0.2 and 0.4, and the limiting index α can be set to 0.6; in this embodiment, no specific limitation is made on the collection method and threshold value of the above data, and those skilled in the art can freely set them according to actual needs.

[0036] For example, when the emergency energy vehicle performs power supply tasks in mountainous areas, the environmental wind speed v0=15m / s is collected, which exceeds the wind speed discrimination factor v1=12m / s, triggering the calculation of the wind speed factor, and the wind speed factor Fy=ln{[(15-12) / 15]²+1}=0.039; at the same time, the rainfall intensity j0=60mm / h is collected, and the rainfall intensity threshold is 50mm / h, then the meteorological impact factor Q=Fy×(j0 / J)=0.0468, and the wind and rain synergy effect is quantified through a nonlinear model to avoid misjudgment of isolated parameters; The geological data was collected, and the surface micro-vibration frequency z0=18Hz was collected, and the vibration frequency threshold Z was 15Hz, indicating a potential landslide risk. The terrain slope p0=20° and the slope threshold P=18° significantly increased the probability of vehicle rollover, and the geological influence factor Zy=0.6×(18 / 15)^1.2+0.4×ln[3×20 / 18+1] / ln4=1.332 was constructed; 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 the third-level risk, limiting the engine power to the rated value of α=0.6, so as to take into account the power supply demand and equipment protection under extreme conditions and avoid system collapse caused by overload.

[0037] Please continue reading Figure 1 As shown, the intelligent energy management method for emergency energy vehicles also includes: Step S102, collecting cable data within the monitoring period, and constructing a mechanical-thermal coupling model based on the collected cable data to determine the cable status, and updating the power protection strategy based on the cable status, wherein the cable data includes cable tension, core temperature and swing angle.

[0038] Specifically, 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 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 is compared with the adjustment discrimination factor c 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, and α1=α×exp[cS] is set.

[0039] Specifically, a tension-temperature-swing coupling model is introduced to analyze the correlation between mechanical stress and thermal effect. Compared with independent monitoring of single parameters, it can identify abnormal cable conditions in advance (such as material creep caused by local overheating), and intelligently optimize the power limitation strategy based on the risk level to avoid overload or fatigue fracture.

[0040] Exemplarily, the cable tension can be monitored in real time by a fiber grating sensor to monitor the tension change, the rated breaking tension is based on the rated value of the cable tensile strength (such as 80% of the breaking tension of the steel core cable is taken as the threshold), the core temperature can be collected by a distributed fiber optic temperature measurement system or a thermistor patch, the temperature threshold te can be 90°C (normal working conditions) + 5°C redundancy, the swing angle can be dynamically detected by an inertial measurement unit (IMU), and the angle threshold θy is set according to the cable expansion margin, such as being set to 15°, and the adjustment judgment factor can be set to 0.68; in this embodiment, the collection method and threshold value of the above data are not specifically limited, and those skilled in the art can freely set them according to actual needs.

[0041] Exemplarily, the collected cable data is 80kN, the core temperature is 95℃, the swing angle is 12°, the rated breaking tension is 100kN, the temperature threshold te is 95℃, the angle threshold θy is 15°, and the force-heat 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 judgment factor c=0.68. No additional power restriction is required. Since the cable status is normal and the environmental risk level is level three, the original restriction index α=0.6 is maintained.

[0042] Please continue reading Figure 1 As shown, the intelligent energy management method for emergency energy vehicles also includes: Step S103, comparing the cable swing frequency collected during the monitoring period with the frequency discrimination factor to optimize the cable status judgment process.

[0043] Specifically, when optimizing the judgment process of the cable status, 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 judgment process of the cable status 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]}.

[0044] Specifically, by adding cable swing frequency monitoring and optimizing the discrimination logic, the monitoring sensitivity is improved through frequency domain feature identification (such as resonance risk).

[0045] Exemplarily, the swing frequency can be collected by a vibration acceleration sensor, and the threshold B can be set to 2.5 Hz. In this embodiment, no specific limitation is imposed on the collection method and threshold value of the above data, and those skilled in the art can freely set them according to actual needs.

[0046] For example, the cable swing frequency bp=3Hz is collected, which is greater than the frequency discrimination factor B=2.5Hz. The discrimination factor needs to be optimized and adjusted. After optimization, the discrimination factor c1 is calculated as: 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, the cable status is determined to be 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 judgment logic is dynamically adjusted through the exponential function to reduce the false alarm rate.

[0047] Please continue reading Figure 1 As shown, the intelligent energy management method for emergency energy vehicles also includes: Step 104, 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, combine the battery compartment abnormality index and the impedance abnormality index to determine the battery status, and generate a discharge protection strategy based on the battery status.

[0048] Specifically, by integrating dust concentration, temperature difference and impedance heterogeneity data, a mapping relationship between environment and electrical performance is established. Through multi-dimensional anomaly detection, the risk of battery aging or short circuit is accurately judged, and differentiated derating strategies are generated to ensure power supply continuity and avoid sudden failures.

[0049] See also Figure 3 As shown, the method for generating the discharge protection strategy includes: Step S301 , jointly analyzing 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.

[0050] Specifically, when determining the battery compartment abnormality index, the dust concentration n0 in the battery compartment, 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 are used to construct the battery compartment abnormality index k. The expression of k is k=ln{[(n0 / n1)×lg(△t / △ty)] 2 +1} / ln2.

[0051] Specifically, through the logarithmic correlation modeling of dust concentration and temperature difference, the potential correlation between environmental pollution and thermal failure (such as dust accumulation leading to reduced heat dissipation efficiency) is revealed. Compared with single threshold alarms, it can identify progressive anomalies and provide data support for preventive maintenance.

[0052] Please continue reading Figure 3 As shown, the method for generating the discharge protection strategy also includes: Step S302, monitoring the impedance of the battery compartment to determine an impedance anomaly index.

[0053] Specifically, 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.

[0054] Specifically, by locating local defects in the battery pack (such as a sudden increase in impedance caused by poor single-cell contact), the sensitivity to early faults, such as imbalance between battery clusters, is significantly improved compared to overall impedance detection.

[0055] 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 the particles, such as being set to 5 mg / m³. The temperature difference △t can be monitored by a dual redundant infrared temperature measurement module to monitor the internal and external temperature difference. The temperature difference discrimination factor △ty is dynamically adjusted according to the heat dissipation performance, such as being set to 20°C. In this embodiment, no specific limitation is imposed on the collection method and threshold value of the above data, and those skilled in the art can freely set them according to actual needs.

[0056] For example, in this embodiment, the method for collecting the impedance of each region is not specifically limited, and those skilled in the art can freely set it according to needs. 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 can be divided into n regions by an orthogonal grid division method. The impedance of each region is collected by a four-wire patch electrode and a temperature compensation sensor, and the fluctuation judgment threshold can be set to 0.3. In this embodiment, the collection method and threshold value of the above data are not specifically limited, and those skilled in the art can freely set it according to actual needs.

[0057] Please continue reading Figure 3 As shown, the method for generating the discharge protection strategy also includes: Step S303, data fusion of the battery compartment abnormality index and the impedance abnormality index is performed to determine the battery status, and a discharge protection strategy is generated according to the battery status.

[0058] Specifically, when generating the discharge protection strategy, the battery compartment abnormality index k and the impedance abnormality index ZY are data fused 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.

[0059] Specifically, the battery status index based on multi-source data fusion supports refined derating control, adjusts the derating ratio in abnormal conditions, avoids the risk of power interruption caused by traditional "full shutdown", and extends the power supply time under safety conditions.

[0060] Exemplarily, the battery compartment weight can be set to 0.7, the impedance weight can be set to 0.3, the discharge limit factor can be set to 0.7, and the battery state judgment factor can be set to 0.2; in this embodiment, no specific limitation is made on the values ​​of the above thresholds, and those skilled in the art can freely set them according to actual needs.

[0061] For example, the battery compartment is divided into 10 areas, and the impedance data (unit: Ω) is: 50,52,51,49,50,70,48,50,53,49, calculated ZY=0.374; The dust concentration n0=6mg / m³ and temperature difference Δt=25℃ were collected, and the dust discrimination factor n1=5mg / m³ and temperature difference discrimination factor △ty=20℃ were set. The battery compartment abnormality index k=ln{[(6 / 5)×lg(25 / 20)]²+1} / 0.693=0.019 was calculated. The battery status index ZT=0.7×0.019+0.3×0.374=0.1255 was constructed based on ZY and k, which is less than the battery status discrimination factor zt=0.2, and the discharge rate was not triggered to be reduced to β=0.7 times the rated value.

[0062] Please continue reading Figure 1 As shown, the intelligent energy management method for emergency energy vehicles also includes: Step S105, comprehensively analyzing 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 issue a safety warning to the user.

[0063] Specifically, when determining the state of the emergency energy vehicle, a comprehensive analysis is performed on the environmental risk level, cable state, and battery state of each monitoring period within the management period to determine the safety index R, and 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, and m3 is the battery weight. m1+m2+m3=1, d1 is the number of monitoring periods with a second or third level environmental risk level within the management period, d2 is the number of monitoring periods with abnormal cable states within the management period, d3 is the number of monitoring periods with abnormal battery states within the management period, and D is the number of monitoring periods within the management period; 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 cycle is judged to be abnormal. Otherwise, the safety status of the emergency energy vehicle in the current management cycle is judged to be normal. When the safety status of the emergency energy vehicle is abnormal, a safety warning is issued to the user.

[0064] Specifically, the global safety index R can identify long-term equipment degradation trends through cumulative risk ratio analysis within the management cycle. Multi-weighted composite analysis supports priority division, which can match the operation and maintenance focus of different scenarios and significantly improve the system's active protection capabilities.

[0065] 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 judgment factor can be set to 0.24; in this embodiment, no specific limitation is made on the values ​​of the above thresholds, and those skilled in the art can freely set them according to actual needs.

[0066] Exemplarily, in this embodiment, the monitoring period can be set to 1 minute, and the management period can be set to 1 hour. In this embodiment, the settings of the monitoring period and the management period are not specifically limited, and those skilled in the art can freely set them.

[0067] For example, data within the management cycle (1 hour, 1 monitoring per minute): Environmental risk level: 3 times level 3, 2 times level 2; Cable status abnormality: 4 times; Battery status abnormality: 5 times; Total monitoring cycle D = 60; Safety index R calculation: 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 early warning is required, and through historical data analysis, long-term equipment degradation trends are identified to support preventive maintenance.

[0068] Please continue reading Figure 4 As shown, it is a structural schematic diagram of an intelligent energy management device for emergency energy vehicles, including: The power protection unit 501 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 according to the environmental risk level; The cable monitoring unit 502 is used to collect cable data within the monitoring period, and to construct a mechanical-thermal coupling model based on the collected cable data to determine the cable status, and to update the power protection strategy based on the cable status; The frequency monitoring unit 503 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 504 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 505 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 issue a safety warning to the user.

[0069] The present application also provides an electronic device for executing the intelligent energy management method for emergency energy vehicles. Figure 5 As 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 a flash memory, a solid state drive (SSD) or a combination thereof, which is used to store computer programs, process intermediate data and historical data sets; the communication interface includes a wired communication module and a wireless communication module, and the wired communication module supports Ethernet or RS-485 protocol for connecting to a sensor network; the wireless communication module supports LoRa, 5G or satellite communication protocol for transmitting 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, memory and communication interface.

[0070] The embodiment of the present application further provides a computer readable storage medium on which a computer program code is stored. When the program code is loaded into a processor through an integrated circuit carrier, it is written into a memory via a system bus.

[0071] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not limitations on the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the embodiments here. All obvious changes or modifications derived from the technical solution of the present invention are still 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 battery compartment impedance 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 based on the battery status.

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 influence 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 influence 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 1 is characterized in that: 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.

8. The intelligent energy management method for emergency energy vehicles according to claim 7 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 performed 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.

9. 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.

10. 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 1-8 during operation.

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