Electric power reliability analysis system

Abstract

The invention relates to the technical field of electric power reliability analysis, and discloses an electric power reliability analysis system, which is used for acquiring operation characteristic parameters of a target truck ice house, and calculating the electric power reliability of the target truck ice house based on a cargo temperature upper limit, an initial cargo temperature, an environment temperature and a product of thermal resistance and thermal capacitance. Determining the longest duration when the goods temperature of the target goods van ice house after power failure is less than or equal to the goods temperature upper limit; according to the power-off recovery rate and the longest duration, the interruption failure probability that the cargo temperature of the target truck ice house is larger than the cargo temperature upper limit due to any power-off is calculated; according to the failure strength of the main power supply and the interruption failure probability, determining the effective failure strength when the power-off time is greater than the longest duration in the travel duration; and based on the travel duration and the effective failure strength, determining the system reliability when the power-off time is less than or equal to the longest duration in the travel duration.

Description

Technical Field

[0001] The present invention relates to the technical field of power reliability analysis, and more specifically, to a power reliability analysis system. Background Art

[0002] With the increasingly strict temperature control requirements for the "origin - consumer" full - link cold chain transportation in industries such as fresh food, e - commerce, and medicine, the continuous heat preservation ability of vehicles in a non - fixed power supply environment has become the key to ensuring the quality of goods. However, the power supply systems such as vehicle generators and batteries are affected by driving conditions, fuel or power limitations, and temperature differences, and there is often a risk of random interruption. Therefore, it is necessary to couple and evaluate the power interruption with the heat buffer characteristics of the carriage cold storage to achieve more accurate risk prediction and design optimization.

[0003] The existing technologies mainly follow the power reliability evaluation methods, such as evaluating generator sets or battery systems with indicators such as mean time to failure, availability, redundancy, etc. However, they generally ignore the temperature buffer effect brought by the large heat capacity jointly formed by the goods and the insulation layer in the carriage after power failure, cannot distinguish short - time interruptions from long - time interruptions that really lead to temperature control failure, and cannot quantify the probability of temperature over - limit after the "power - off - temperature rise" coupling. Therefore, they cannot provide a quantitative basis for power redundancy configuration, fast - switching strategies, or heat preservation design, and there are problems of evaluation distortion and waste of design costs. Summary of the Invention

[0004] The present invention provides a power reliability analysis system, which solves the technical problems raised in the background art.

[0005] The present invention provides a power reliability analysis system, including: A data acquisition module for obtaining the operation characteristic parameters of the ice storage of the target truck, The operation characteristic parameters include: main power failure intensity, power - off recovery rate, upper limit of cargo temperature, initial cargo temperature, ambient temperature, product of thermal resistance and heat capacity, and travel duration; A first processing module for determining the longest duration during which the cargo temperature of the ice storage of the target truck after power failure ≤ the upper limit of the cargo temperature based on the upper limit of the cargo temperature, the initial cargo temperature, the ambient temperature, and the product of thermal resistance and heat capacity; A second processing module for calculating the interruption failure probability that the cargo temperature of the ice storage of the target truck > the upper limit of the cargo temperature caused by any power - off event according to the power - off recovery rate and the longest duration; A third processing module for determining the effective failure intensity during the travel duration when the power - off time > the longest duration according to the main power failure intensity and the interruption failure probability; A reliability analysis module, configured to determine the system reliability with a power-off time ≤ the longest duration within the travel duration based on the travel duration and the effective failure intensity.

[0006] Further, determining the longest duration for which the cargo temperature of the target truck ice storage ≤ the upper limit of the cargo temperature after power-off based on the upper limit of the cargo temperature, the initial cargo temperature, the ambient temperature, and the product of the thermal resistance and heat capacity includes: Collect the initial cargo temperature and the ambient temperature, and call the upper limit of the cargo temperature and the product of the thermal resistance and heat capacity; Based on the first-order thermal-RC model, determine the temperature rise curve of the target truck ice storage in the power-off state; Compare the temperature rise curve with the temperature level corresponding to the upper limit of the cargo temperature, obtain the moment of their first intersection, and define the intersection moment as the longest duration for which the cargo temperature does not exceed the upper limit of the cargo temperature after power-off.

[0007] Further, calculating the interruption failure probability that the cargo temperature of the target truck ice storage > the upper limit of the cargo temperature caused by any power-off based on the power-off recovery rate and the longest duration includes: Call the power-off recovery rate and the longest duration; Regard the duration of any power-off as an exponential distribution random variable with the parameter of the power-off recovery rate; By calculating the probability that this exponential distribution random variable is greater than the longest duration, obtain the interruption failure probability that the cargo temperature of the target truck ice storage > the upper limit of the cargo temperature caused by any power-off.

[0008] Further, determining the effective failure intensity with a power-off time > the longest duration within the travel duration based on the main power supply failure intensity and the interruption failure probability includes: Call the main power supply failure intensity and the interruption failure probability; Based on the product of the main power supply failure intensity and the interruption failure probability, determine the effective failure intensity with a power-off duration greater than the longest duration within the travel duration.

[0009] Further, determining the system reliability with a power-off time ≤ the longest duration within the travel duration based on the travel duration and the effective failure intensity includes: Call the travel duration and the effective failure intensity; Based on the Poisson distribution model, calculate the system reliability with a power-off duration less than or equal to the longest duration within the travel duration.

[0010] The beneficial effects of the present invention are as follows: By coupling parameters such as the main power failure intensity, power failure recovery rate, and the product of the heat resistance and heat capacity of the cargo compartment, based on the electro-thermal first-order model and Poisson process, the system reliability before the cargo temperature exceeds the limit during the entire transportation process is calculated and quantified in real time, so as to accurately evaluate the temperature control risk caused by power failure, and further significantly improve the safety and economy of cold chain transportation. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a module diagram of a power reliability analysis system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0012] Now, the subject matter described herein will be discussed with reference to exemplary embodiments. It should be understood that discussing these embodiments is only to enable those skilled in the art to better understand and thus implement the subject matter described herein. Without departing from the scope of protection of the content of this specification, changes can be made to the functions and arrangements of the elements discussed. Each example can omit, substitute, or add various processes or components as needed. Additionally, the features described relative to some examples can also be combined in other examples.

[0013] As Figure 1 shown, a power reliability analysis system includes: A data acquisition module for obtaining the operation characteristic parameters of the ice storage of the target truck, The operation characteristic parameters include: main power failure intensity, power failure recovery rate, upper limit of cargo temperature, initial cargo temperature, ambient temperature, product of heat resistance and heat capacity, and travel duration; A first processing module for determining the longest duration during which the cargo temperature of the ice storage of the target truck is ≤ the upper limit of the cargo temperature after power failure based on the upper limit of the cargo temperature, the initial cargo temperature, the ambient temperature, and the product of the heat resistance and heat capacity; In an embodiment of the present invention, determining the longest duration during which the cargo temperature of the ice storage of the target truck is ≤ the upper limit of the cargo temperature after power failure based on the upper limit of the cargo temperature, the initial cargo temperature, the ambient temperature, and the product of the heat resistance and heat capacity includes: Collecting the initial cargo temperature and ambient temperature, and calling the upper limit of the cargo temperature and the product of the heat resistance and heat capacity; Determining the temperature rise curve of the ice storage of the target truck in the power failure state based on the first-order heat-RC model; Comparing the temperature rise curve with the temperature level corresponding to the upper limit of the cargo temperature, obtaining the moment of their first intersection, and defining the intersection moment as the longest duration during which the cargo temperature does not exceed the upper limit of the cargo temperature after power failure.

[0014] In this embodiment, the temperature data is collected as follows: Obtaining the thermodynamic boundary conditions at the moment of power failure to provide initial values for subsequent heat model calculations.

[0015] Read the initial cargo temperature through the in-vehicle temperature sensor and the ambient temperature ; Record the initial cargo temperature as the "last cargo compartment internal temperature before power-off", and record the ambient temperature as the "vehicle exterior ambient temperature at the moment of power-off".

[0016] Retrieve the safety threshold and the thermal inertia constant to clarify the temperature control upper limit and the system's thermal response ability.

[0017] Read the upper limit of the cargo temperature from the system configuration or database , which is preset according to the cargo quality requirements; Read the product of the thermal resistance and heat capacity from the design data or experimental determination , which is equivalent to the time constant of the thermal system.

[0018] Based on the first-order thermal–RC model, simulate the dynamic change of the cargo compartment temperature over time after power-off. In an adiabatic insulation box, the temperature conduction satisfies the first-order RC network equation and can be approximated by a simplified exponential decay model.

[0019] Calculation formula of the first-order thermal–RC model: ; where represents the temperature (°C) inside the cargo compartment after the power-off time , represents the ambient temperature (°C), represents the initial cargo temperature (°C) at the moment of power-off, represents the thermal resistance, indicating the ability of the insulation layer to impede heat flow , represents the heat capacity, indicating the heat storage capacity of the cargo and the box , represents the product of the thermal resistance and heat capacity, corresponding to the time constant (h) of the thermal system; Calculation of the longest duration: Determine the maximum duration required for the cargo temperature to rise from the initial cargo temperature to the upper limit of the cargo temperature without any compensatory refrigeration, that is, the "thermal buffer capacity" of the system; Solution method:

[0020] where represents the longest duration after power-off when the cargo temperature ≤ the upper limit of the cargo temperature ; It should be noted that the refrigerated box of the truck and its contents can be regarded as an equivalent volumetric heat body at the moment of power-off. The heat exchange with the environment mainly occurs through the insulation layer, which satisfies the first-order heat–RC model; The time constant determined by thermal resistance and heat capacity governs the rate of temperature change; The longest duration is directly obtained by logarithmic transformation without numerical iteration.

[0021] It should be noted that by combining the first-order heat–RC model after power-off of the cargo compartment with the thermal inertia of the insulation layer and the goods, an electro-thermal coupling index of "thermal buffering capacity" is proposed and quantified, that is, the longest duration during which the cargo temperature can be maintained ≤ the upper limit of the cargo temperature without refrigeration after power-off . Different from the existing reliability evaluation that only focuses on the power interruption itself, this method has the following beneficial effects: Traditional MTBF / availability indicators cannot distinguish the differences in cold chain safety between "short-term" and "long-term" power outages. By modeling the entire process from power-off to temperature rise and taking into account physical parameters such as thermal resistance and heat capacity, "short-term power outages" and "excessive power outages" are automatically distinguished within the same framework.

[0022] This method does not rely on numerical simulation or empirical tables, but directly gives the exponential decay solution and calculates the longest duration logarithmically with a closed-form formula, which is simple and efficient, suitable for embedding in in-vehicle or dispatching real-time computing units, and significantly improves engineering practicability.

[0023] A second processing module, configured to calculate the interruption failure probability that the cargo temperature of the target truck ice storage > the upper limit of the cargo temperature caused by any power outage according to the power-off recovery rate and the longest duration; In an embodiment of the present invention, calculating the interruption failure probability that the cargo temperature of the target truck ice storage > the upper limit of the cargo temperature caused by any power outage according to the power-off recovery rate and the longest duration includes: Invoking the power-off recovery rate and the longest duration; Regarding the duration of any power outage as an exponentially distributed random variable with the power-off recovery rate as the parameter; By calculating the probability that this exponentially distributed random variable is greater than the longest duration, the interruption failure probability that the cargo temperature of the target truck ice storage > the upper limit of the cargo temperature caused by any power outage is obtained.

[0024] In this embodiment, according to the power-off recovery rate μ and the longest duration determined by the first processing module , calculate the interruption failure probability that the cargo temperature in the target truck ice storage exceeds the upper limit of the cargo temperature . Specifically as follows: . First, the system obtains the power failure recovery rate μ and the longest duration output by the first processing module from the data acquisition module or the internal storage unit. . The power failure recovery rate μ is defined as the rate at which the system returns to the normal power supply state after a power failure event. Its physical meaning is the number of power failure recovery operations completed per unit time, which is usually determined by the switching speed of the power failure recovery device or the self-recovery characteristics of the battery, and the unit is ; the longest duration is the maximum allowable power failure duration for keeping the temperature in the cargo compartment not exceeding the upper limit of the cargo temperature after a power failure. .

[0025] Secondly, the second processing module regards the duration of each power failure as an exponentially distributed random variable D with parameter μ. The reason for adopting the exponential distribution model is based on the following rationality: in various physical and engineering systems, the occurrence of recovery events (such as the access of backup power supply, the recharging after the battery is discharged, etc.) often conforms to the "memoryless" property, that is, the next recovery has nothing to do with past experiences, and the exponential distribution can exactly describe the probability characteristics of this random process with a single parameter μ.

[0026] The probability density function of the exponential distribution is: , ; where represents the probability density function, which is used to describe the probability density of the random variable D at the power failure time , represents the probability decay term of "not yet recovered at the power failure time ", represents the probability density of exactly restoring power supply at the power failure time ; Its "memoryless" property ensures that the durations of each power failure event are statistically independent and follow the same distribution. Then, the module calculates the probability that D exceeds according to the cumulative distribution function of the exponential distribution, that is ; where corresponds to the event of "the power failure duration exceeds the longest duration". Once this event occurs, the cargo temperature will surely rise to the upper limit of the cargo temperature or above, thus constituting a "disruption failure". That is, it represents the probability of the cargo temperature exceeding the limit due to the too long duration in any power failure event, and the numerical range is between 0 and 1.

[0027] Specifically, the "thermal buffering capacity" of the truck cold storage is organically coupled with the statistical characteristics of the power outage duration. The power outage duration is characterized by an exponential distribution model, and the longest duration is used as the threshold to derive a closed-form probability expression for the cargo temperature exceeding the limit caused by a single power outage event . Different from the prior art where all power outages are regarded as equivalent risks, the present invention has the following beneficial effects: By introducing the "temperature rise after power outage multiplied by the product of thermal resistance and heat capacity" into the calculation of the probability of interruption failure in electrical reliability, the present invention breaks through the neglect of the influence of temperature control in traditional power reliability assessment.

[0028] The exponential distribution with a parameter of power outage recovery rate μ is used to describe the power outage duration, and the longest duration is used as the threshold to automatically classify random power outages into "fatal (power outage time > the longest duration) / non-fatal power outage time ≤ the longest duration", realizing risk filtering.

[0029] By extracting the memoryless property of the Poisson process from the theory of random processes, a simple exponential formula containing only μ and the longest duration is obtained, avoiding numerical integration or simulation and facilitating deployment on in-vehicle or cloud platforms.

[0030] A reliability analysis module for determining the system reliability with the power outage time ≤ the longest duration within the travel duration based on the travel duration and the effective failure intensity.

[0031] A third processing module for determining the effective failure intensity with the power outage time > the longest duration within the travel duration according to the main power supply failure intensity and the interruption failure probability; In an embodiment of the present invention, determining the effective failure intensity with the power outage time > the longest duration within the travel duration according to the main power supply failure intensity and the interruption failure probability includes: Invoking the main power supply failure intensity and the interruption failure probability; Determining the effective failure intensity with the power outage duration greater than the longest duration within the travel duration according to the product of the main power supply failure intensity and the interruption failure probability.

[0032] In this embodiment, the statistical intensity of "all power outage events" is coupled with the occurrence probability of "the power outage time > the longest duration within the travel duration" to quantify the average failure rate that will actually cause the cargo temperature to exceed the limit during the entire travel, that is, the effective failure intensity , specifically as follows: First, obtain two key parameters from data collection or the first and second processing modules: The main power failure intensity λ, that is, the average number of power outages occurring per unit time (hour); the interruption failure probability , that is, the probability that any power outage event causes the cargo temperature to exceed the upper limit of the cargo temperature .

[0033] Then, calculate the effective failure intensity: According to the "Poisson process dilution theorem", all power outage events are extracted according to the probability to extract the part that will actually cause temperature control failure (that is, the power outage duration is greater than the longest duration ), and calculate the effective failure intensity , ; The main power failure intensity is used to measure the overall frequency of power outages of the main power supply per unit time and is derived from historical statistics.

[0034] The interruption failure probability is used to measure the probability that during a power outage event, the power outage duration exceeds the longest duration , thus causing the cargo temperature to exceed the limit.

[0035] The effective failure intensity is used to measure the average incidence rate of power outage events where "power outage time > the longest duration" (i.e., interruptions that will cause the cargo temperature to exceed the limit) per unit time.

[0036] In the theory of Poisson process, if the original events occur at the main power failure intensity , and each event is judged as an event where "power outage time > the longest duration" with the interruption failure probability , then the events where "power outage time > the longest duration" themselves still form a Poisson process with a rate of : In non - overlapping time intervals, the number of events occurring is independent of each other; The probability of at most one event occurring per unit time is proportional to the time length; In a very short time interval, the probability of an event occurring is linearly related to the interval length; The overall event rate is constant and does not change with time.

[0037] Since the effective failure intensity is itself defined as the average incidence rate of interruptions where "the power outage duration exceeds the longest duration " per unit time, which fully conforms to the basic assumptions of the Poisson process, the Poisson distribution can thus be used to describe the number distribution of fatal interruption events within a given travel duration T.

[0038] It should be noted that different from the rough treatment of "treating all power-off events equally" in traditional reliability assessment, the interruption failure probability after coupling "power-off - temperature control" is fused with the main power failure intensity λ through the Poisson process "dilution" theory to obtain the effective failure intensity for the event of "power-off time > the longest duration" within the travel duration. The beneficial effects of the present invention are as follows: In the past, the evaluation only calculated the main power failure rate λ and could not distinguish the consequences of power-off events. By multiplying λ with the interruption failure probability , only the power-off events that will cause temperature control failure are "retained", and the failure frequency that poses a substantial threat to the cargo temperature safety is accurately quantified.

[0039] Introduce the Poisson process "dilution" theorem into the cold chain power reliability analysis: The original power-off events occur at the rate of the main power failure intensity λ, and after sampling by the interruption failure probability , they still constitute a Poisson process with a rate of .

[0040] Different from the traditional methods that require numerical models or Monte Carlo simulations, the present invention only uses a product formula to complete the calculation of the effective failure rate, simplifying and improving the calculation efficiency.

[0041] In an embodiment of the present invention, based on the travel duration and the effective failure intensity, determining the system reliability of the power-off time ≤ the longest duration within the travel duration includes: Invoking the travel duration and the effective failure intensity; Based on the Poisson distribution model, calculating the system reliability of the power-off duration being less than or equal to the longest duration within the travel duration.

[0042] In this embodiment, the reliability analysis module calculates the system reliability based on the travel duration and the effective failure intensity in the following process to quantify the probability that the cargo temperature always remains below the allowable upper limit during the entire transportation process.

[0043] First, obtain two core parameters from the data acquisition module and the third processing module: Travel duration , representing the total duration of a transportation task, in hours, directly input by the scheduling system or the user; Effective failure intensity , representing the average number of occurrences of "power-off time > the longest duration " per unit time, in units of 。

[0044] System reliability is defined as the probability that the number of fatal interruption events is zero within the travel duration . Let be the number of occurrences of "power-off time > longest duration " within the travel duration . Then, according to the zero-event probability formula of the Poisson distribution, we have:

[0045]

[0046] where represents the system reliability, that is, the probability that "the power-off duration is ≤ the longest duration within the travel duration ".

[0047] When the event of "interruption where the power-off duration exceeds the longest duration " occurs independently at a constant rate , the zero-event probability is exactly given by the empirical formula of the Poisson distribution.

[0048] The calculated system reliability is used as the output of this module for:[[]] comparing with the preset reliability target to determine whether the system design or operation strategy meets the standard; displaying the remaining reliability in real time in the dispatching platform or vehicle-mounted system to support visual monitoring and early warning.

[0049] Specifically, the present invention screens out the fatal interruptions of "power-off time > longest duration " in the previous module, and directly uses the average occurrence rate of the electro-thermal coupling event as the input for reliability calculation, realizing the integrated assessment of the risks in the whole process of cold chain transportation.

[0050] By using the "zero-event probability" formula of the Poisson process to describe the probability that there is no occurrence of "power-off time > longest duration " within the travel duration , it avoids the cumbersome Monte Carlo simulation or numerical integration, ensuring that the evaluation results have analyticity, verifiability, and real-time performance.

[0051] The obtained system reliability It can be used for pre - design verification and can also be dynamically associated with the remaining journey during vehicle operation, providing real - time reliability monitoring and early warning for the dispatching system or drivers, and greatly improving the intelligent operation and maintenance ability of cold - chain transportation.

[0052] The above describes the embodiments of this example, but this example is not limited to the above - mentioned specific implementation manners. The above - mentioned specific implementation manners are only illustrative and not restrictive. Under the inspiration of this example, those of ordinary skill in the art can also make many forms, all of which fall within the protection scope of this example.

Claims

1. A power reliability analysis system, characterized in that, Including: A data acquisition module, configured to obtain the operation characteristic parameters of the ice storage of the target truck; The operation characteristic parameters include: main power failure intensity, power failure recovery rate, upper limit of cargo temperature, initial cargo temperature, ambient temperature, product of thermal resistance and heat capacity, and travel duration; A first processing module, configured to determine the longest duration during which the cargo temperature of the ice storage of the target truck after power failure is ≤ the upper limit of the cargo temperature based on the upper limit of the cargo temperature, the initial cargo temperature, the ambient temperature, and the product of thermal resistance and heat capacity; A second processing module, configured to calculate the interruption failure probability that the cargo temperature of the ice storage of the target truck caused by any power failure is > the upper limit of the cargo temperature according to the power failure recovery rate and the longest duration; A third processing module, configured to determine the effective failure intensity when the power failure duration > the longest duration during the travel duration according to the main power failure intensity and the interruption failure probability; A reliability analysis module, configured to determine the system reliability when the power failure duration ≤ the longest duration during the travel duration based on the travel duration and the effective failure intensity.

2. The power reliability analysis system according to claim 1, characterized in that, Determining the longest duration during which the cargo temperature of the ice storage of the target truck after power failure is ≤ the upper limit of the cargo temperature based on the upper limit of the cargo temperature, the initial cargo temperature, the ambient temperature, and the product of thermal resistance and heat capacity includes: Collecting the initial cargo temperature and the ambient temperature, and invoking the upper limit of the cargo temperature and the product of thermal resistance and heat capacity; Determining the temperature rise curve of the ice storage of the target truck in the power failure state based on the first-order thermal-RC model; Comparing the temperature rise curve with the temperature level corresponding to the upper limit of the cargo temperature, obtaining the first intersection time of the two, and defining the intersection time as the longest duration during which the cargo temperature after power failure does not exceed the upper limit of the cargo temperature.

3. An electric power reliability analysis system according to claim 2, wherein, Calculating the interruption failure probability that the cargo temperature of the ice storage of the target truck caused by any power failure is > the upper limit of the cargo temperature according to the power failure recovery rate and the longest duration includes: Invoking the power failure recovery rate and the longest duration; Regarding any power failure duration as an exponentially distributed random variable with the power failure recovery rate as the parameter; Obtaining the interruption failure probability that the cargo temperature of the ice storage of the target truck caused by any power failure is > the upper limit of the cargo temperature by calculating the probability that the exponentially distributed random variable is greater than the longest duration.

4. The power reliability analysis system according to claim 3, wherein, Determining the effective failure intensity when the power failure duration > the longest duration during the travel duration according to the main power failure intensity and the interruption failure probability includes: Invoking the main power failure intensity and the interruption failure probability; Determining the effective failure intensity when the power failure duration during the travel duration is greater than the longest duration according to the product of the main power failure intensity and the interruption failure probability.

5. An electric power reliability analysis system according to claim 4, characterized in that, Determining the system reliability when the power failure duration ≤ the longest duration during the travel duration based on the travel duration and the effective failure intensity includes: Invoking the travel duration and the effective failure intensity; Calculating the system reliability when the power failure duration during the travel duration is less than or equal to the longest duration based on the Poisson distribution model.

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