A multi-type information equipment health state evaluation system and method

Abstract

The present application relates to the field of information processing, in particular to a multi-type information equipment health state evaluation system and method. Based on the electrical topology of the internal device of the energy router, the global and instruction driving structure is constructed and the local driving structure is divided. Through the access test energy acquisition device temperature change and loss, the initial and overall aging value is evaluated. Then the second test energy obtains the reference aging value, the device and local driving aging value is analyzed, and the health state is evaluated combined with the distribution position and the number of markers. The present application decouples the global topology of the energy router into local driving structure, applies progressive safety energy test, fuses multi-level aging data, accurately maps performance degradation, solves the problem of low accuracy of evaluation results when relying on abnormal data in operation to evaluate or predict the health state of the energy router, and can also locate faults, capture degradation trends, cross-verify data, and provide accurate decision support for operation and maintenance.

Description

Technical Field

[0001] This invention relates to the field of information processing, and in particular to a health status assessment system and method for multiple types of information equipment. Background Technology

[0002] An energy router is a multi-port intelligent power electronic device used in modern power grids and distributed energy systems. It serves as a core energy dispatching device for various types of information equipment, enabling flexible AC / DC power conversion and bidirectional flow. It supports the bidirectional flow of power between the power grid, distributed power sources, energy storage, and loads, providing stable power supply and coordinated dispatch for multiple devices, and adapting to the needs of multi-energy interconnection and dynamic dispatch. With the increasing penetration rate of distributed energy, its role in distribution networks and microgrids is becoming increasingly critical. Because energy routers operate under high power and high switching frequency conditions for extended periods, their internal components are prone to aging, potentially leading to insulation degradation, parameter drift, and reduced efficiency. If this deterioration in health is not detected in time, it can result in equipment failure, power outages, or even grid disturbances. Therefore, accurate health status assessment of energy routers is of great importance.

[0003] Traditional health status monitoring relies on regular manual inspections, assessing the health of internal components by measuring operating parameters such as temperature, voltage, current, and insulation resistance. This method is labor-intensive, inefficient, requires shutdown for inspection, affects power supply continuity, and fails to reflect dynamic changes in equipment during actual operation. To improve assessment efficiency, some technologies have begun to adopt data-driven methods, using anomaly data from energy routers to predict their remaining lifespan. For example, the method disclosed in Chinese patent application CN113780689A uses historical anomaly data from the tested energy router as training data to train a lifespan prediction model. It then constructs a lifespan prediction dataset using anomaly data generated by energy routers of the same model during real-time operation. The trained model is then used to predict the remaining lifespan of the tested equipment. This type of method reduces manual intervention to some extent and improves assessment efficiency.

[0004] However, the health degradation of a power router is a continuous and gradual process. Its performance decline often manifests as a long-term trend, such as a continuous decrease in the efficiency of internal component operating parameters and a gradual increase in local temperature rise. In contrast, abnormal data such as instantaneous overcurrent and overtemperature alarms are merely discrete spike signals that occasionally appear during long-term operation of the device, and their number is limited and sparsely distributed. If the model is trained and predicted solely based on these abnormal data, it will be difficult to reflect the subtle changes and continuous degradation trajectory of the core physical hardware of the power router under most normal operating conditions, resulting in inaccurate health status assessment results. Summary of the Invention

[0005] This invention provides a method for assessing the health status of various types of information devices to address the problem of low accuracy in assessing or predicting the health status of energy routers when relying on abnormal data during operation.

[0006] To solve the above-mentioned technical problems, this application provides the following technical solution: A method for assessing the health status of various types of information devices includes the following steps: S10: Based on the electrical topology of the energy flow through the internal devices of the energy router, construct a global driving structure; use the device topology that implements a specific energy distribution function as the instruction driving structure; mark the internal devices and energy flow direction of each instruction driving structure in the global driving structure; divide the global driving structure into several local driving structures according to the number of marks and energy flow direction held by adjacent internal devices; and obtain the energy flow direction between the local driving structures. S20: Obtain the initial parameter range of the internal devices, and take the maximum energy that the local drive structure is allowed to safely access as the first test energy; when the local drive structure is connected to the first test energy, obtain the change rate of temperature of each internal device over time, evaluate the initial aging value of the internal devices, and combine the loss data of energy flowing through the local drive structure to evaluate the overall aging value of the local drive structure. S30: Based on the loss data corresponding to the overall aging value and the energy flow direction between the local drive structures, the maximum safe energy that the instruction drive structure is allowed to access is taken as the second test energy; when the instruction drive structure is supplied with the second test energy, the loss data of the local drive structure and the aging value of the internal devices are obtained again, and the aging value of the internal devices is taken as the reference aging value. S40: Based on the initial aging value and several reference aging values, analyze the relationship between the temperature change rate of the internal device and time to obtain the device aging value of the internal device; combine the reference aging value with the corresponding loss data to obtain the overall reference aging value of the local driving structure; process the overall reference aging value into the local driving aging value according to the difference between the reference aging value and the device aging value. S50: Assess the health status of the energy router by combining the distribution location of each local driving structure in the global driving structure and the number of tags it holds.

[0007] The basic principle and beneficial effects of this invention are as follows: The complex global electrical topology of the energy router is decoupled into multiple independently testable and evaluable local driving structures based on the specific functions implemented, namely the command-driven structure and energy flow path. By applying progressive safety energy tests from local to global levels to these local driving structures, the performance characteristics of the local structures under operating conditions are actively guided. By integrating aging data from multiple levels—devices, local structures, and the global system—continuous and accurate mapping and localization from microscopic parameter drift to macroscopic performance degradation are achieved. In this way, the traditional evaluation model relying on passive, sparse, and abnormal data is transformed into a proactive, continuous, and functional path-based refined management model, which can accurately capture the slow performance degradation trajectory of equipment during long-term operation and provide early warning of potential fault points.

[0008] Through the marking and partitioning operations in step S10, the global topology is decomposed into local structures according to function. This allows any performance degradation, as assessed by the aging values ​​in steps S20 to S40, to be directly associated with a specific functional module, rather than a determination of the overall aging status of the energy router. When the aging value of a certain local drive structure is abnormal, maintenance personnel can immediately locate the corresponding functional circuit. Combined with the distribution location and number of markings in step S50, they can quantitatively assess the impact of the local fault on other upstream and downstream functions and the overall system reliability, achieving a leap from alarm to accurate diagnosis and impact analysis.

[0009] Steps S20 and S30 emphasize using the maximum energy allowed for safe access during testing, rather than destructive extreme testing. By performing tests periodically or triggeredly, performance snapshots—a series of aging values—of the same local structure at different points in time can be obtained. A continuous data stream of equipment health status can be obtained without equipment downtime. By comparing historical aging value sequences, slow degradation trend lines, such as capacitor ESR increases and heat dissipation performance declines, can be clearly plotted, providing crucial information for predictive maintenance.

[0010] In step S40, the initial aging value comes from local testing, while multiple reference aging values ​​come from global testing in different instruction structures. The two are combined, and the final device aging value and local drive aging value are confirmed through difference analysis and correction. This forms a built-in cross-validation and correction mechanism. Errors or randomness in a single test can be identified and corrected by observing the device's performance in different functional combinations, i.e., instruction-driven structures. For example, if a device exhibits aging behavior in functional path A but tests normally in functional path B, it may indicate a problem with other related devices in path A, such as the drive circuit, rather than the device itself, significantly improving the reliability and robustness of the evaluation results.

[0011] The final health assessment of the energy router is derived by comprehensively considering the aging values, distribution locations, and importance of each local structure in the overall function. The number of markers implicitly indicates the frequency with which devices participate in core functions. The assessment results can distinguish severely aged but non-core components that can be replaced in a planned manner, slightly aged but critical components that require close monitoring, and which local structural degradations are the main causes of overall system efficiency decline. This provides multi-dimensional support for operation and maintenance decisions, from urgency to economic considerations, upgrading from a simple judgment of equipment health to in-depth analysis of anomaly locations, causes, and response strategies.

[0012] In summary, this invention decouples the global topology of the energy router into a local driving structure, applies progressive security energy testing, integrates multi-level aging data, and accurately maps performance degradation. It solves the problem of low accuracy in evaluation results when relying on abnormal data during operation to assess or predict the health status of the energy router. Furthermore, it can locate faults, capture degradation trends, and cross-validate data, providing precise decision support for operation and maintenance.

[0013] Furthermore, the initial parameter range includes impedance, current, and voltage. Based on the impedance, current, and voltage, the real-time power loss of the internal devices is calculated. In step S20, after obtaining the initial parameter range of the internal devices, the power loss calculation accuracy is corrected by combining temperature data. The energy transmission path in each internal device is identified according to the electrical topology of each internal device in the global drive structure. Energy flow direction data in each internal device is extracted from the transmission path. The attenuation of energy flowing through the internal devices is calculated based on the difference between the real-time power loss and the input power of each internal device. These are then summarized to form the attenuation data of energy flowing through each internal device. The energy flow... The data and attenuation data are associated one-to-one with each other according to the electrical topology path of the global drive structure. With the impedance value and temperature corresponding to each internal device as constraints, the attenuation of all internal devices on the same transmission path is accumulated to obtain the total attenuation of each transmission path. Based on the total attenuation of each transmission path and the safety threshold, the upper limit of the energy carrying capacity of the global drive structure is analyzed. Based on the upper limit of the energy carrying capacity and combined with the functional priority of the local drive structure, the specific value of the first test energy is determined. The functional priority is preset according to the number of markers of the local drive structure in the global drive structure and its position in the energy transmission path.

[0014] This invention calculates power loss based on parameters such as impedance and current, combines temperature correction accuracy, and correlates energy flow and attenuation data with electrical topology. It solves for the global energy carrying capacity limit using a safety threshold as a constraint, and then allocates values ​​according to functional priority. This not only accurately calibrates the first test energy, perfectly matching the actual carrying capacity of the local drive structure, improving the effectiveness and reliability of aging assessment data, and solving the assessment deviation problem caused by traditional experience values; it also allocates energy according to functional priority, avoiding excessive test resources occupied by non-core modules, focusing on core modules for in-depth evaluation, and improving test resource utilization; furthermore, it forms a closed-loop constraint through multi-dimensional parameter verification, avoiding the risk of overload of local drive structures from the source, while reducing additional wear and tear on devices during testing and extending device lifespan.

[0015] Furthermore, a heat dissipation substrate is deployed for each local driving structure, and each internal device within the local driving structure is fixedly connected to the heat dissipation substrate. When energy is applied to the local driving structure, temperature data at each location on the heat dissipation substrate is collected and processed into temperature distribution change data. The timing of temperature changes of internal devices in the corresponding area is obtained by combining the temperature data collection time. Then, the temperature change gradient at each location on the heat dissipation substrate is calculated based on the temperature data at each location on the heat dissipation substrate, and the temperature diffusion trend at each location on the heat dissipation substrate is analyzed. The influence data of ambient temperature is obtained based on the temperature diffusion trend analysis.

[0016] Temperature data is indirectly collected by the heat dissipation substrate, and the temperature change gradient and diffusion trend are calculated. The timing of temperature change in the corresponding area of ​​the device is inferred, and the degree of influence of ambient temperature on device temperature can be quantified. This makes up for the shortcomings of traditional assessments that ignore environmental variables and improves the comprehensiveness of aging assessment. Furthermore, by analyzing the temperature diffusion trend, potential associated aging risks caused by temperature conduction can be identified in advance, expanding the dimensions of aging assessment and achieving an upgrade from single-point assessment to regional associated assessment.

[0017] Furthermore, in step S20, after evaluating the overall aging value of the local driving structure, the overall aging value is associated with the corresponding acquisition time and stored as a historical local evaluation record; when setting the first test energy for the local driving structure again, the decay law of the overall aging value over time is analyzed according to the time sequence of the overall aging value in the historical local evaluation record, and a predicted overall aging value is generated. The initial value of the first test energy is determined based on the predicted overall aging value; in step S20, when the local driving structure is connected to the first test energy, the initial value of the first test energy is used as the basis for energy access, and then the energy is gradually increased.

[0018] Using fixed-energy testing can easily damage aging devices and lacks the ability to predict aging trends. This invention stores records of the correlation between local aging values ​​and time, analyzes time-series decay patterns to generate predicted values, and sets initial test energy based on these predicted values, gradually increasing it. It can predict aging trends through historical data, identify performance degradation trajectories in advance, and achieve a shift from passive assessment to proactive early warning. Furthermore, the stepped energy input completely avoids instantaneous high-energy impacts on aging devices, reducing additional damage to the devices during testing and improving the safety of the testing process. Simultaneously, it dynamically adjusts the test energy to adapt to the real-time aging status of the device, improving the accuracy of aging assessment and overcoming the limitation of fixed-energy testing being unable to adapt to devices at different aging stages.

[0019] Furthermore, when energy is connected to the local driving structure, temperature data of adjacent local driving structures are collected. Combined with the temperature change gradient and temperature diffusion trend of the local driving structure, as well as the physical positional relationship between the local driving structures, the environmental temperature influence data is analyzed. The environmental temperature influence data of each local driving structure in the command driving structure are combined to analyze the proportion of the second test energy connected to the command driving structure and the rate of energy change.

[0020] This invention collects temperature data from adjacent local driving structures and analyzes the impact of ambient temperature on the data, combining temperature gradients, diffusion trends, and physical location relationships, thereby determining the energy input ratio and rate. By comprehensively utilizing multi-dimensional temperature information, this invention makes the input of the second test energy more closely match the actual operating environment, significantly improving the accuracy of test energy allocation and avoiding evaluation deviations caused by environmental differences. Simultaneously, by controlling the energy rise rate, it effectively slows down the overall heating rate, preventing thermal imbalance within the energy router and improving the stability and safety of the testing process. Furthermore, by quantifying the temperature coupling effect between different local driving structures, the evaluation process can fully reflect the thermal correlation characteristics between multiple modules, improving the accuracy of multi-module collaborative evaluation and solving the problem that traditional unified input strategies cannot adapt to complex structures and environmental differences.

[0021] Furthermore, in step S30, a second test energy is simultaneously supplied to the instruction driving structures that do not overlap with the local driving structures.

[0022] By filtering out non-overlapping instruction-driven structures and synchronously introducing a second test energy, simulating a multi-task parallel load state, this parallel testing method not only significantly shortens the testing cycle and reduces evaluation time, improving overall evaluation efficiency, but also restores the multi-instruction collaborative working condition during real-world operation, making up for the shortcomings of serial testing being disconnected from actual operating scenarios and enhancing the reference value of evaluation results.

[0023] Furthermore, a position influence value is generated based on the number and distance of physically adjacent local driving structures, and a marker influence value is generated based on the number of markers held by the local driving structure. The position influence value and marker influence value are multiplied by the difference between the reference aging value and the device aging value to obtain the local driving aging value. The router aging value is obtained by summing all local driving aging values. The router aging value is matched with a preset health status level to obtain the current health level, and the current health level is used as the router's health status. For local driving structures whose local driving aging values ​​exceed the preset local aging warning value, the corresponding topology location and physical location are extracted to generate warning information, and the warning information is added to the router's health status.

[0024] Based on the number of adjacent structures, distances, and marker counts of locally driven structures, the influence value of location and markers is calculated. This, combined with differences in aging values, quantifies the aging value of locally driven structures, generating warning information. This upgrades health assessment from a binary judgment to a multi-dimensional quantitative analysis, improving assessment accuracy through weighted quantification. Simultaneously, it accurately locates the topological and physical positions of abnormal structures, significantly reducing the difficulty and cost of manual inspection and improving operational efficiency. Furthermore, by weighting the importance of associated structures, the aging impact of core modules is highlighted, prioritizing warnings of critical component failure risks, providing accurate data for health status assessment and more convenient maintenance prompts.

[0025] Furthermore, in step S30, after the second test energy is introduced into the instruction-driven structure, the temperature distribution change data, temperature change gradient, and ambient temperature influence data of the local driving structure are combined to analyze and obtain the temperature change data in the energy router as the overall temperature change data. Then, combined with the specific energy allocation function implemented by the energy router, the temporal relationship between the overall temperature change data of the energy router and the specific energy allocation function is analyzed, and the health status of the energy router is evaluated based on the analysis results. When analyzing the overall temperature change data, the position influence value of the local driving structure is adjusted according to the proportion of the coverage area corresponding to the temperature diffusion trend of the local driving structure in the physical structure range of the energy router.

[0026] This invention integrates temperature distribution changes, gradients, and environmental impact data of local driving structures to generate overall temperature change data. It analyzes the temporal relationship between this data and energy distribution functions, providing a multi-dimensional assessment of equipment health. This approach considers both device aging and functional compatibility, enabling the early detection of hidden temperature effects on energy distribution efficiency, mitigating functional failures, and improving equipment reliability. The invention dynamically adjusts the positional influence value based on the proportion of temperature diffusion coverage within the physical structure, optimizing positional weights, eliminating interference from temperature diffusion in the assessment results, and improving the accuracy of calculating local driving aging values. Simultaneously, it identifies key areas with widespread temperature diffusion, strengthens the assessment weight of core areas, and prioritizes monitoring the aging status of high-impact areas. Furthermore, it quantifies the correlation effects of temperature diffusion, establishing a dynamic correlation mechanism to compensate for the shortcomings of fixed weights that ignore structural correlations.

[0027] Furthermore, based on the topology between each energy router, a preset verification command is sent to the energy router. After receiving the verification command, the energy router, based on its local health status, obtains the time consumed to execute the verification command locally before executing the verification command, and uses this as the estimated verification time. After executing the verification command, the energy router obtains the time consumed to execute the verification command locally, and uses this as the actual verification time. The energy router integrates the verification command, the preset local unique identifier, the local health status, the estimated verification time, and the actual verification time into a verification record, and sends the verification record to the next energy router.

[0028] After receiving the verification record, the energy router uses it as a reference record; it retrieves the verification command from the reference record, and obtains the estimated verification time, local health status, and actual verification time for the verification command; it uses the difference between the estimated and actual verification times in the reference record as a reference difference, and the health status in the reference record as a reference health status; it uses the difference between the estimated and actual verification times for the verification command as a control difference, and the local health status as a control health status; based on the correspondence between the reference health status and the reference difference, and the control health status, it obtains the difference corresponding to the control health status, compares the obtained difference with the control difference, and evaluates the confidence value of the local health status based on the comparison results.

[0029] This invention relies on the topology of multi-energy routers to issue verification commands and relay verification records. First, it completes self-check by comparing the difference between the local estimated and actual verification time. Then, it uses the health status and time difference of upstream devices as a reference and cross-verifies the local health assessment results with the help of the operation of other routers. Based on this, it quantifies and generates a health status confidence value, effectively avoiding the bias and misjudgment of a single device's autonomous assessment, and greatly improving the accuracy and reliability of health status assessment of multi-device clusters. Attached Figure Description

[0030] Figure 1 This is a flowchart of the health status assessment method for multiple types of information devices in Example 1. Detailed Implementation

[0031] The following will describe the concept and technical effects of the present invention clearly and completely with reference to embodiments, so as to fully understand the purpose, features and effects of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are all within the scope of protection of the present invention. Example 1 like Figure 1 As shown, a method for assessing the health status of multiple types of information devices includes the following steps: S10: Based on the electrical topology of the energy flow through the internal components of the energy router, a global drive structure is constructed. The electrical topology refers to the electrical connection diagram of all components (such as IGBTs, capacitors, inductors, resistors, etc.) inside the router. It is generally obtained from the circuit schematic provided with the device at the factory. In this embodiment, the electrical topology diagram provided with the device at the factory is directly called to clarify the series, parallel and signal interaction relationships of each component. Based on this, a global drive structure covering all components is built for overall analysis of the energy transmission path.

[0032] The device topology that performs a specific energy distribution function is used as the command-driven structure. Generally, it is divided according to the core function of the device. For example, DC / DC conversion command-driven structure (composed of IGBT, freewheeling diode, and filter capacitor), grid connection command-driven structure (composed of grid connection inductor, thyristor, and sampling resistor), and auxiliary power supply command-driven structure (composed of linear regulator and filter inductor) correspond to the three core functions of photovoltaic energy conversion, grid connection, and internal device power supply, respectively.

[0033] In the global driving structure, the internal devices and energy flow of each instruction driving structure are marked. The number of marks is the number of instruction driving structures each internal device belongs to. Generally, core devices (such as IGBTs) participate in multiple instruction driving structures. Based on the number of marks and energy flow of adjacent internal devices, the global driving structure is divided into several local driving structures, and the energy flow between local driving structures is obtained. For example, first, the instruction driving structures are defined according to function, and the number of instructions (marks) belonging to each device is marked; then, based on the rule of "continuous energy flow and difference of mark quantity between adjacent devices ≤ 1", the global topology is split into several local driving structures, and the energy flow between local structures is simultaneously sorted out.

[0034] A heat dissipation substrate is deployed for each local driving structure. Each internal component within the local driving structure is fixedly bonded to one side of the heat dissipation substrate using heat dissipation resin. An infrared temperature sensor is set on the other side of the heat dissipation substrate to collect temperature data at various locations on the heat dissipation substrate. The relative positions of the heat dissipation substrate and the internal components are combined to obtain the temperature data at each location.

[0035] S20: Obtain the initial parameter range of internal components, including impedance, current, and voltage. Calculate the real-time power loss of internal components based on these parameters. After obtaining the initial parameter range, adjust the power loss calculation accuracy using temperature data. Specifically, different electrical laws are applied according to the working principles of different components. For resistive components, heat loss is obtained by multiplying the square of the current by the resistance value, based on Joule's law. For power semiconductor devices, conduction loss is calculated using the AC circuit active power formula P=U·I·cosφ, using voltage U, current I, and power factor cosφ (technicians maintaining the energy router directly read this value using a power analyzer and then set a specific power factor cosφ for this type of semiconductor device). For inductors and capacitors, copper loss or dielectric loss is calculated by multiplying the square of the current by the equivalent series resistance, based on their equivalent series resistance.

[0036] Based on the electrical topology of each internal device in the global drive structure, the energy transmission path in each internal device is sorted out. The energy flow direction data between each internal device (i.e., the energy input or output direction and flow sequence of each device) is extracted from the transmission path. The energy attenuation through the internal device is calculated based on the difference between the real-time power loss and the input power of each internal device. The energy attenuation data of each internal device is then summarized.

[0037] Energy flow data and attenuation data are correlated one-to-one according to the electrical topology of the global drive structure. Using the impedance values ​​and temperature-related safety thresholds of each internal component as constraints, the attenuation of all internal components along the same transmission path is accumulated to obtain the total attenuation for each transmission path. Based on the total attenuation of each transmission path and the safety threshold, the upper limit of the energy carrying capacity of the global drive structure is analyzed. Specifically, the transmission paths are analyzed and the attenuation of components is accumulated to obtain the total attenuation. Bottleneck paths are identified through safety threshold verification, and the total attenuation of the bottleneck path is subtracted from the maximum power that can be provided at the input. The difference is the maximum energy input limit that the global drive structure can safely carry.

[0038] The first test energy is determined based on the energy carrying capacity limit of the global drive structure and the preset functional priority of the local drive structures. Priorities are set according to the number of markers on the structure and the location of the energy transmission path (technicians set the first priority based on the energy transmission path location in the circuit schematic provided with the equipment; technicians set different priorities for different numbers of markers, retrieve the corresponding priority based on the current number of markers on the local drive structure as the second priority, and use the higher of the two as the preset functional priority). Structures with more markers and located on the main path have higher priorities. The preset functional priorities are divided proportionally, for example, into high, medium, and low levels, each corresponding to a specific percentage. High-priority structures take a higher percentage of the upper limit, and low-priority structures take a lower percentage, thus determining the specific value of the first test energy for each local structure.

[0039] The specific value of the first test energy is determined based on the upper limit of energy carrying capacity and the functional priority of the local drive structure. The functional priority is preset according to the number of markers of the local drive structure in the global drive structure and its position in the energy transmission path.

[0040] The maximum energy allowed for safe access to the local drive structure is used as the first test energy (an initial value is set by the administrator based on the parameters of the internal components before aging, and subsequently updated based on the aging of the internal components). When the local drive structure is connected to the first test energy, the rate of temperature change of each internal component is acquired over time to evaluate the initial aging value of the internal components. Specifically, the rate of temperature change of the internal components is collected by a temperature sensor, the duration of the test is recorded, and an attenuation coefficient matching the material properties of the components is selected (set by technicians according to the type of internal components). The product of the rate of temperature change, the duration, and the attenuation coefficient is used as the initial aging value.

[0041] By combining energy loss data flowing through the local drive structure, the overall aging value of the local drive structure is evaluated. Specifically, firstly, loss weights are assigned to each device within the local drive structure. The loss weights are set according to the proportion of the power loss of the internal device to the total loss of the local drive structure (technicians set different proportion ranges and set different loss weights for each range; during calculation, the obtained proportions are matched with the proportion ranges to obtain the corresponding loss weights); then, the initial aging value of each device is multiplied by its corresponding loss weight, and the sum is accumulated to obtain the weighted aging value; finally, the weighted aging value is divided by the total number of devices in the structure, and the result is the overall aging value of the local drive structure.

[0042] After evaluating the overall aging value of the local driving structure, the overall aging value is associated with the corresponding acquisition time and stored as a historical local evaluation record.

[0043] When setting the first test energy for the local drive structure again, the decay pattern of the overall aging value over time is analyzed based on the time sequence of the overall aging value in the historical local evaluation records to generate a predicted overall aging value, which is then used as the initial value of the first test energy. When the local drive structure is connected to the first test energy, the initial value of the first test energy is used as the basis for energy access, and the energy is gradually increased. In this embodiment, the increase rate is set by the technician by default.

[0044] S30: Based on the loss data corresponding to the overall aging value and the energy flow direction between local drive structures, the maximum safe energy allowed to be connected to the command drive structure is used as the second test energy. By default, the second test energy is the average value of the first test energy of the local drive structure to which the command drive structure belongs, or the minimum first test energy in the local drive structure, derived from the position of the local drive structure in the power flow direction and the attenuation data of each local drive structure. When the second test energy is applied to the command drive structure, the loss data of the local drive structure and the aging value of the internal components are acquired again, and the aging value of the internal components is used as the reference aging value.

[0045] Specifically, a second test energy is simultaneously supplied to instruction-driven structures that do not overlap with the local driving structures.

[0046] S40: Based on the initial aging value and several reference aging values, analyze the relationship between the internal device temperature change rate and time (as mentioned above, "the product of temperature change rate, duration, and attenuation coefficient is used as the initial aging value") to obtain the device aging value of the internal device. Combine the reference aging value with the corresponding loss data to obtain the overall reference aging value of the local drive structure; based on the difference between the reference aging value and the device aging value, process the overall reference aging value into the local drive aging value; S50: Assess the health status of the energy router by combining the distribution location of each local driving structure in the global driving structure and the number of tags it holds.

[0047] The location influence value of a local driving structure is generated based on the number and distance of its physically adjacent local driving structures. A higher number of adjacent structures indicates that the local driving structure undertakes more connectivity and energy scheduling tasks in the global topology, thus increasing its location importance. Closer proximity indicates more frequent energy interactions and stronger mutual influence, also increasing location importance. Specifically, firstly, a quantity weight is determined based on the number of adjacent local driving structures; the more adjacent structures, the larger the quantity weight (the quantity weight is determined by technicians setting a range of quantities and corresponding weights, then matching them). Then, a distance weight is determined based on the distance between each adjacent structure (the distance weight is determined by technicians setting a range of distances and corresponding weights, then matching them); the closer the distance, the larger the distance weight. The distance weights of all adjacent structures are averaged and then superimposed with the quantity weights to obtain the location influence value of the local driving structure. During the superposition process, the quantity weight reflects the connectivity importance of the structure in the topology, while the distance weight reflects the tightness of energy interactions between structures; both together determine the criticality of the structure in the global layout.

[0048] A tag influence value is generated based on the number of tags held by the local driving structure. Technicians set the corresponding tag influence value when assigning a first priority to the number of tags, and then match them together. Specifically, the first priority is obtained based on the number of tags held by the local driving structure and its position in the circuit schematic, and the tag influence value corresponding to the first priority is also obtained.

[0049] The local drive aging value is obtained by multiplying the location influence value, the marker influence value, and the difference between the reference aging value and the device aging value. The router aging value is obtained by summing all local drive aging values. The router aging value is then matched with the preset health status level (set by technicians according to maintenance needs) to obtain the current health level, which is taken as the router's health status.

[0050] For local driver structures whose local driver aging values ​​exceed the preset local aging warning value, extract the corresponding topology location and physical location to generate warning information (i.e., generate warning information containing topology location and physical location), and add the warning information to the router's health status.

[0051] like Figure 1 As shown, the circular markers on S10 and below S50 represent the starting point and ending point of a step in a method for assessing the health status of multiple types of information devices, respectively.

[0052] In practice, a global drive structure is first built based on the electrical topology of the internal components of the energy router; instruction drive structures that implement specific functions are divided, and the number of instruction drive structures (i.e., the number of marks) and energy flow direction of each component are marked. Local drive structures are divided according to the number of marks and flow direction of adjacent components; heat dissipation substrates are deployed for each local drive structure, and the device temperature data is collected by an infrared temperature sensor on the other side of the substrate.

[0053] Next, initial parameters such as device impedance, current, and voltage are acquired, and real-time power loss is calculated according to electrical laws. Temperature data is used to correct the loss accuracy. The energy transmission path is analyzed, the energy attenuation of each device is calculated, and the total path attenuation is accumulated. Bottleneck paths are identified through safety threshold verification, and their total attenuation is deducted to obtain the global energy carrying capacity limit. Functional priorities are set according to the number of markers on the local drive structure and the path location, and the first test energy is determined according to the priority ratio. After energy is input, the device temperature change rate is collected, and the initial aging value is calculated based on the test duration and attenuation coefficient. Then, the overall aging value of the local drive structure is calculated according to the device loss weight, and timestamped historical evaluation records are stored. When setting subsequent test energies, the overall aging value is predicted based on historical data and used as the initial value to gradually increase the energy input.

[0054] Then, based on the overall aging value and energy flow, the average or minimum value of the first test energy of the local driving structure is taken as the second test energy, and the reference aging value of the device is obtained after the instruction driving structure is passed in.

[0055] Next, the initial aging value is compared with the reference aging value to analyze the temporal relationship of the device temperature change rate and correct the device aging value; the overall reference aging value is calculated by combining the loss data, and the local drive aging value is obtained based on the difference between the reference aging value and the device aging value.

[0056] Finally, based on the number and distance of adjacent local driving structures, a location impact value is generated by superimposing quantity weights and average distance weights; a label impact value is generated according to the number of labels; the difference between the location, label impact values, and aging values ​​is multiplied and summed to obtain the router aging value, which is then matched with a preset health level. For local driving structures that exceed the threshold, their topology and physical location are extracted to generate warning information, which is incorporated into the final health status assessment result.

[0057] This embodiment also includes a health status assessment system for multiple types of information devices that uses a method for assessing the health status of multiple types of information devices.

[0058] Example 2 The only difference between this embodiment and Embodiment 1 is that, when energy is applied to the local driving structure, temperature data at various locations on the heat dissipation substrate is collected and processed into temperature distribution change data. This temperature distribution change data is then combined with the data acquisition time to obtain the temporal sequence of temperature changes in the corresponding region's internal devices. The temperature distribution change data is a collection of temperature values ​​at various physical locations on the heat dissipation substrate changing over time, reflecting the dynamic temperature differences in different regions of the substrate. Furthermore, the temperature gradient at each location on the heat dissipation substrate is calculated based on the temperature data, and the temperature diffusion trend at each location is analyzed. Based on this temperature diffusion trend analysis, the influence data of ambient temperature is obtained.

[0059] Specifically, the heat sink is divided into M×N monitoring areas based on its physical location, and each area is assigned a unique coordinate. Assuming =1,2,...,5; =1,2,...,5.

[0060] Each time point collected (Assuming) The temperature values ​​of each area (corresponding to a test duration of 5 minutes) were compiled into a temperature distribution matrix. (Unit: °C)

[0061] Calculate the difference in temperature distribution matrix between adjacent time points The temperature changes in 25 regions were obtained at all time points. The set represents the temperature distribution variation data. Assume a core region. The temperature change was 0.2℃. Edge area The change was 0.1℃. .

[0062] For each monitoring area, a corresponding acquisition timestamp is matched to the temperature value, generating a single-area "temperature-time curve," i.e., the temperature change time series. Assume the temperature time series for area (3,3) is ( ,…, This visually reflects the upward trend of temperature in the area over time.

[0063] For the temperature distribution matrix at each time point, calculate the temperature change gradient between each region and its adjacent regions, using the following formula: , The coordinates on the heat sink substrate are: The temperature change gradient of the monitoring area reflects the rate and direction of temperature change from that area to adjacent areas; The coordinates on the heat sink substrate are: The temperature value of the monitoring area at a certain moment; Indicates and Temperature values ​​of adjacent regions at the same time; and It represents the coordinate offset of adjacent regions, including adjacent regions in the four directions of up, down, left, and right; This indicates the physical distance between two adjacent monitoring areas. In this embodiment, the default unit is centimeters (cm).

[0064] Assuming the distance between adjacent regions ,by Time zone It is related to the right area The temperature difference is Then the gradient .

[0065] The sign of the gradient indicates the direction of temperature diffusion: a positive value indicates that heat diffuses from... Towards Diffusion, and the opposite for negative values.

[0066] By statistically analyzing the gradient direction and magnitude at all time points, the heat diffusion path of the heat dissipation substrate is determined. For example, assuming that the gradient of the core region (3,3) of the heat dissipation substrate of the IGBT driver unit is always positive, the heat continues to diffuse to the surrounding edge regions; and the gradient value is the largest in the direction towards the left grid-connected inductor unit (0.3℃ / cm), indicating that the heat mainly diffuses to the adjacent grid-connected inductor local driving structure.

[0067] When energy is applied to the local drive structure, temperature data of the heat dissipation substrate is collected simultaneously to generate temperature distribution change data and temperature gradient. The environmental temperature impact data is calculated by combining the physical distance between the two local drive structures, the temperature diffusion gradient of the target structure, and the temperature gradient of adjacent structures. Specifically, the temperature gradient directions of the target and adjacent local drive structures are first calibrated to determine the relative trend of heat diffusion. The effective amplitude of the gradient superposition is then calculated based on the physical distance between them. Normalization calibration is then performed using the maximum gradient amplitude under rated operating conditions as a benchmark, constraining the data to a fixed range, ultimately obtaining the environmental temperature impact data that quantifies the degree of thermal interference between adjacent structures. If the target and adjacent structures are in opposite directions (heat diffuses between them), the environmental temperature impact data is proportional to the sum of the absolute values ​​of the gradients and inversely proportional to the physical distance. If the gradient directions are the same (heat diffuses in the same direction), the environmental temperature impact data value decreases. The larger the environmental temperature impact data value, the stronger the temperature interference of the adjacent structures on the target structure, requiring careful correction during subsequent aging value evaluation.

[0068] By combining the environmental temperature impact data of each local driving structure within the instruction-driven structure, the proportion of the second test energy connected to the instruction-driven structure and the rate of energy change are analyzed. Specifically, based on the environmental temperature impact data and functional priority of each local driving structure within the instruction-driven structure, the proportion of the second test energy connected is determined, calculated according to the rule that the larger the environmental temperature impact data, the lower the proportion, and the higher the priority, the higher the base value; then, the rate of energy change is set according to the temperature diffusion trend and the environmental temperature impact data value.

[0069] In practice, when energy is connected to the local drive structure, the heat dissipation substrate is divided into multiple monitoring areas according to its physical location, and each area is assigned a unique coordinate. Temperature data of each area at different time points are collected, organized into a temperature distribution matrix, and the difference between the temperature distribution matrices of adjacent time points is calculated to obtain the temperature change of each area. The set of temperature changes at all time points is the temperature distribution change data. At the same time, the temperature value of each area is matched with the collection timestamp to generate the temperature change time series of that area.

[0070] Based on the temperature distribution matrix, the temperature gradient between each region and its adjacent regions is calculated. The gradient reflects the rate and direction of temperature change in space. By analyzing the gradient direction and magnitude at each time point, the heat diffusion trend on the heat dissipation substrate is determined.

[0071] While the local driving structure is receiving energy, temperature data from adjacent local driving structures are simultaneously collected to generate temperature distribution change data and temperature gradients. Combining the physical distance between the two local driving structures, the temperature diffusion gradient of the target structure, and the temperature gradients of adjacent structures, the environmental temperature influence data is calculated. When the gradient directions of the two structures are opposite, the environmental temperature influence data is directly proportional to the sum of the absolute values ​​of the gradients and inversely proportional to the distance; when the gradient directions are the same, the value of the environmental temperature influence data decreases.

[0072] By combining the environmental temperature impact data of each local drive structure in the instruction-driven architecture with functional priorities, the access ratio of the second test energy is determined. The greater the environmental temperature impact data, the lower the access ratio; the higher the functional priority, the higher the base value of the access ratio. Then, based on the temperature diffusion trend and the environmental temperature impact data, the energy change rate is set.

[0073] Example 3 The only difference between this embodiment and embodiments 1-2 is that, in step S30, after the second test energy is introduced into the instruction-driven structure, the temperature distribution change data, temperature change gradient, and ambient temperature influence data of the local driving structure are combined to analyze and obtain the temperature change data in the energy router as the overall temperature change data. The overall temperature change data reflects the dynamic change of the internal temperature of the physical structure of the energy router over time during the execution of a specific energy distribution function. First, the monitoring area division and temperature acquisition time granularity of each local driving structure are unified, and the data is mapped to the global coordinate system of the energy router; outliers in the temperature change gradient are removed, and the interference intensity (weak interference, medium interference, strong interference) of the ambient temperature influence data is labeled according to the intervals of 0-0.3, 0.3-0.6, and 0.6-1.

[0074] Then, based on the interference intensity, a correction coefficient is set, assuming a weak interference of 0.95, a medium interference of 0.85, and a strong interference of 0.75, to adjust the temperature distribution change data; the temperature change gradient is corrected in combination with the gradient diffusion direction, subtracting the interference ratio when diffusing in the opposite direction and adding the interference ratio when diffusing in the same direction, to eliminate thermal interference between adjacent structures.

[0075] Subsequently, weights were assigned according to functional priority, assuming that temperature distribution change data accounted for 50%, temperature change gradient for 30%, and environmental correction for 20%, and the weighted fusion value for each region was calculated. Finally, all regional data were aligned along the time axis, and the fusion data of each functional zone was summarized to generate overall temperature change data for the energy router that combines spatial distribution characteristics with temporal variation trends.

[0076] Furthermore, considering the specific energy distribution function implemented by the energy router, the temporal relationship between the overall temperature change data of the energy router and the specific energy distribution function is analyzed, and the health status of the energy router is assessed based on the analysis results. When analyzing the overall temperature change data, the positional influence value of the local driving structure is adjusted according to the proportion of the coverage area corresponding to the temperature diffusion trend of the local driving structure within the physical structure range of the energy router.

[0077] Specifically, the process of the energy router performing a specific energy distribution function is first divided into multiple stages, such as the function startup stage, power rise stage, stable operation stage, and function switching or shutdown stage, and the time interval of each stage is recorded. Then, temperature change characteristics corresponding to the time intervals are extracted from the overall temperature change data, including the temperature rise rate, temperature gradient change, hotspot appearance time, and temperature stabilization time. Next, the functional stages are mapped to temperature change characteristics, and the temperature response of different stages is compared to see if it matches the design expectations. For example, a significant temperature rise should occur during the function startup stage, the temperature change should tend to level off during the stable operation stage, and the temperature gradient may fluctuate briefly during the function switching stage. Finally, by comparing the actual temperature change with the theoretical or historical normal temperature response (a stable correlation between the specific energy distribution function execution stage and the corresponding temperature change characteristics established by continuously collecting and statistically analyzing a large amount of operational data under long-term stable operation and no potential faults), the temporal relationship between the two is determined to be consistent. If the matching is good (during the process of the energy router performing a specific energy distribution function, the difference between the current actual overall temperature change data and the historical normal temperature response relationship in terms of time characteristics and numerical characteristics is less than the difference value preset by the technician, and there is no obvious deviation), it indicates that the thermal behavior of the energy router is normal when performing this function; if there is an abnormal temperature rise, a delayed temperature response, or a sudden gradient change, it indicates that the timing relationship is abnormal, suggesting that there may be a risk of device aging or failure, and generating corresponding health status assessment results and warning information.

[0078] In practice, after the second test energy is supplied to the command-driven structure, the temperature distribution change data, temperature change gradient, and ambient temperature influence data of each local driving structure are fused to obtain the overall temperature change data of the energy router. First, the monitoring area division and temperature acquisition time granularity of each local driving structure are unified, and the data is mapped to the global coordinate system of the energy router. Outliers in the temperature change gradient are removed, and the ambient temperature influence data is classified according to the interference intensity in the intervals of 0-0.3, 0.3-0.6, and 0.6-1.

[0079] Next, correction coefficients are set based on the interference intensity: 0.95 for weak interference, 0.85 for medium interference, and 0.75 for strong interference, to adjust the temperature distribution change data. The temperature change gradient is then corrected based on the gradient diffusion direction; the interference percentage is subtracted for back-diffusion and added for unidirectional diffusion to eliminate thermal interference from adjacent structures. Subsequently, weights are allocated according to functional priority: 50% for temperature distribution change data, 30% for temperature change gradient, and 20% for environmental correction, and the weighted fusion value for each region is calculated. Finally, all regional data are aligned along the time axis, and the fused data from each functional zone is summarized to generate overall temperature change data that combines spatial distribution characteristics with temporal trends.

[0080] Furthermore, considering the specific energy allocation function currently being performed by the energy router, the functional process is divided into stages such as startup, power increase, stable operation, switching, or shutdown. Features such as the temperature rise rate, temperature gradient change, hotspot appearance time, and temperature stabilization time for each stage are extracted from the overall temperature change data. The functional stages are mapped to these temperature change features, and their consistency with theoretical or historical normal temperature response is compared. If the difference is less than a preset threshold, indicating a good match, the energy router's thermal behavior is considered normal. If abnormal temperature increases, response lags, or abrupt gradient changes occur, the timing relationship is considered abnormal, indicating potential device aging or failure risks, and corresponding health status assessment results and warning messages are generated.

[0081] When analyzing overall temperature change data, the positional influence value of the local driving structure is dynamically adjusted based on the proportion of the temperature diffusion trend coverage of the local driving structure to the physical structure range of the energy router, making the health status assessment more accurate.

[0082] Example 4 The only difference between this embodiment and embodiments 1-3 is that, based on the topology between the energy routers (such as cascaded or parallel physical topology, communication topology), the first device is selected, and a preset verification command (such as a specified power energy allocation or specified path energy transmission verification command) is sent to the first energy router.

[0083] After receiving the verification command, the energy router calculates the estimated execution time of the command using a linear time estimation algorithm based on its local health status (including health level, router aging value, and local driver aging average). This estimated time is used as the predicted verification time. After executing the verification command, the energy router collects the actual execution time in real time. This actual execution time is used as the actual verification time.

[0084] The energy router integrates the verification command, the preset local unique identifier, the local health status, the estimated verification time and the actual verification time into a verification record, and sends the verification record to the next energy router.

[0085] After receiving a verification record (from other energy routers), the energy router uses it as a reference record, retrieves the verification command from the reference record, and obtains the estimated verification time, local health status, and actual verification time for the verification command. The difference between the estimated and actual verification times in the reference record is used as the reference difference, and the health status in the reference record is used as the reference health status. The difference between the estimated and actual verification times for the verification command is used as the control difference, and the local health status is used as the control health status. Based on the correspondence between the reference health status and the reference difference, and the control health status, the difference corresponding to the control health status is obtained, and the obtained difference is compared with the control difference. The confidence value of the local health status is then assessed based on the comparison result.

[0086] Specifically, a pre-built and stored correspondence between health status and standard time difference benchmarks is established. In this embodiment, health status is divided into three levels, each bound to a fixed standard time difference threshold. For example, health level 1 corresponds to ≤0.05 seconds, level 2 to ≤0.1 seconds, and level 3 to ≤0.15 seconds. The energy router first extracts the reference health status and reference difference from the reference record, checks whether they match the preset benchmark relationship, and after confirming the benchmark is valid, reads the local control health status. Using the control health status as the sole matching basis, a one-to-one search and match is performed in the preset benchmark correspondence relationship to directly obtain the standard time difference value corresponding to the control health status. This method, relying on a preset fixed mapping relationship, completes rapid matching without complex calculations. It provides unified and accurate benchmark data for subsequent comparison of local control differences and standard differences, and assessment of health status confidence values, ensuring the consistency and fairness of cross-device cross-validation.

[0087] The energy router compares the differences in the acquired control health status with the local control differences, and combines this with verification records from multiple energy routers to comprehensively assess the confidence value. Specifically, it first calculates the difference between the standard difference corresponding to a single reference device and the local control difference to obtain a single deviation value; then, it collects verification records from upstream and adjacent energy routers according to the topology link, extracting the reference health status, reference difference, and standard difference data for each device. A multi-source weighted comprehensive assessment algorithm is used to take the arithmetic mean of the standard differences of multiple reference devices as the comprehensive benchmark difference, while simultaneously counting the number of times each single deviation value is qualified. The local control difference is compared with the comprehensive benchmark difference, and the basic confidence level is determined based on the degree of deviation. Then, a weighted correction is made based on the proportion of qualified times for each deviation, eliminating interference from single-point abnormal data, and finally obtaining an accurate and reliable comprehensive confidence value for the local health status.

[0088] By combining the proportion of qualified deviations in each deviation with weighted correction, when eliminating interference from single-point abnormal data, first count the total number of qualified deviations of multiple reference devices, and calculate the proportion of qualified deviations to the total number of verifications as the weighting coefficient; assign high weight to valid data with a high proportion of qualified deviations, reduce the weight of single-point abnormal data that deviates from the overall result or remove them directly, and then complete the correction according to the weighting coefficient to obtain a stable and reliable comprehensive confidence value.

[0089] In practice, the first device is selected based on the topology of multiple energy routers, and a verification command is issued. The device estimates the execution time of the command based on its local health status, collects the actual execution time, generates a verification record, and forwards it to the next device along the topology. The receiving device uses the external record as a reference, calculates the time difference and health status matching items between the local device and the reference device, and matches the standard difference corresponding to the local health status based on the preset relationship between the health status and the standard time difference benchmark.

[0090] Subsequently, the deviation value of a single reference device is calculated, and validation data from multiple devices within the topology are collected. The average of the standard differences across multiple devices is obtained using a multi-source weighted algorithm as the comprehensive baseline difference, while the number of times the deviation passes is counted. The local difference is compared with the comprehensive baseline difference to determine the basic confidence level. Then, the percentage of passing times is used as a weighting coefficient to increase the weight of valid data and remove or weaken outlier data. After weighted correction, the comprehensive confidence value of the local health status is obtained, completing the multi-device cross-validation.

[0091] The above are merely embodiments of the present invention. The invention is not limited to the fields covered by these embodiments. Commonly known structures and characteristics in the solutions are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are able to access all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, under the guidance of this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A method for assessing the health status of multiple types of information devices, characterized in that, Includes the following steps: S10: Construct a global drive structure based on the electrical topology of energy flowing through the internal components of the energy router; The device topology that implements a specific energy distribution function is used as an instruction-driven structure; In the global driving structure, the internal devices and energy flow of each instruction driving structure are marked. Based on the number of tags and energy flow of adjacent internal devices, the global driving structure is divided into several local driving structures, and the energy flow between local driving structures is obtained. S20: Obtain the initial parameter range of the internal devices, and take the maximum energy that the local drive structure is allowed to safely access as the first test energy; when the local drive structure is connected to the first test energy, obtain the change rate of temperature of each internal device over time, evaluate the initial aging value of the internal devices, and combine the loss data of energy flowing through the local drive structure to evaluate the overall aging value of the local drive structure. S30: Based on the loss data corresponding to the overall aging value and the energy flow direction between the local drive structures, the maximum safe energy that the instruction drive structure is allowed to access is taken as the second test energy; when the instruction drive structure is supplied with the second test energy, the loss data of the local drive structure and the aging value of the internal devices are obtained again, and the aging value of the internal devices is taken as the reference aging value. S40: Based on the initial aging value and several reference aging values, analyze the relationship between the temperature change rate of the internal device and time to obtain the device aging value of the internal device; combine the reference aging value with the corresponding loss data to obtain the overall reference aging value of the local driving structure; process the overall reference aging value into the local driving aging value according to the difference between the reference aging value and the device aging value. S50: Assess the health status of the energy router by combining the distribution location of each local driving structure in the global driving structure and the number of tags it holds.

2. The method for assessing the health status of multiple types of information devices according to claim 1, characterized in that: The initial parameter range includes impedance value, current, and voltage. The real-time power loss of the internal devices is calculated based on the impedance value, current, and voltage. In step S20, after obtaining the initial parameter range of the internal devices, the power loss calculation accuracy is corrected by combining temperature data. The transmission path of energy in each internal device is sorted out according to the electrical topology relationship of each internal device in the global drive structure. The flow direction data of energy in each internal device is extracted from the transmission path. The attenuation of energy flowing through the internal device is calculated based on the difference between the real-time power loss of each internal device and the input power. The attenuation data of energy flowing through each internal device is summarized. Energy flow data and attenuation data are correlated one-to-one according to the electrical topology path of the global drive structure. With the impedance value and temperature corresponding to each internal device as constraints, the attenuation of all internal devices on the same transmission path is accumulated to obtain the total attenuation of each transmission path. Based on the total attenuation of each transmission path and the safety threshold, the upper limit of energy carrying capacity of the global drive structure is analyzed. Based on the upper limit of energy carrying capacity and combined with the functional priority of the local drive structure, the specific value of the first test energy is determined. The functional priority is preset according to the number of markers of the local drive structure in the global drive structure and its position in the energy transmission path.

3. The method for assessing the health status of multiple types of information devices according to claim 2, characterized in that: A heat dissipation substrate is deployed for each local driving structure, and the internal components within the local driving structure are fixedly connected to the heat dissipation substrate. When energy is applied to the local driving structure, temperature data at various locations on the heat dissipation substrate is collected and processed into temperature distribution change data. The timing of temperature changes of the internal devices in the corresponding region is obtained by combining the temperature data collection time. Then, based on the temperature data of each location on the heat dissipation substrate, the temperature change gradient of each location on the heat dissipation substrate is calculated, the temperature diffusion trend of each location on the heat dissipation substrate is analyzed, and the influence data of ambient temperature is obtained based on the temperature diffusion trend analysis.

4. The method for assessing the health status of multiple types of information devices according to claim 1, characterized in that: In step S20, after evaluating the overall aging value of the local driving structure, the overall aging value is associated with the corresponding acquisition time to form a historical local evaluation record for storage. When setting the first test energy for the local drive structure again, the decay law of the overall aging value over time is analyzed according to the time sequence of the overall aging value in the historical local evaluation record, and a predicted overall aging value is generated. The initial value of the first test energy is determined based on the predicted overall aging value. In step S20, when the local driving structure is connected to the first test energy, the initial value of the first test energy is used as the basis for energy connection, and then the energy is gradually increased.

5. A method for assessing the health status of multiple types of information equipment according to claim 3 or 4, characterized in that: When energy is connected to the local driving structure, temperature data of adjacent local driving structures are collected. Combined with the temperature change gradient and temperature diffusion trend of the local driving structure, as well as the physical positional relationship between the local driving structures, the environmental temperature influence data are analyzed. By combining the environmental temperature influence data of each local driving structure in the instruction-driven structure, the proportion of the second test energy connected to the instruction-driven structure and the rate of energy change are obtained.

6. The method for assessing the health status of multiple types of information devices according to claim 1, characterized in that: In step S30, a second test energy is simultaneously supplied to the instruction drive structures that do not overlap with the local drive structures.

7. The method for assessing the health status of multiple types of information devices according to claim 1, characterized in that: The position influence value is generated based on the number and distance of physically adjacent local driving structures, and the tag influence value is generated based on the number of tags held by the local driving structure. The position influence value and tag influence value are multiplied by the difference between the reference aging value and the device aging value to obtain the local driving aging value. The router aging value is obtained by summing all local driving aging values. The router aging value is matched with the preset health status level to obtain the current health level, and the current health level is used as the health status of the router. For local driver structures whose local driver aging values ​​exceed the preset local aging warning value, the corresponding topology location and physical location are extracted to generate warning information, which is then added to the router's health status.

8. The method for assessing the health status of multiple types of information devices according to claim 7, characterized in that: In step S30, after the second test energy is introduced into the instruction driving structure, the temperature distribution change data, temperature change gradient and ambient temperature influence data of the local driving structure are combined to analyze the temperature change data in the energy router as the overall temperature change data. Then, combined with the specific energy distribution function implemented by the energy router, the temporal relationship between the overall temperature change data of the energy router and the specific energy distribution function is analyzed, and the health status of the energy router is evaluated based on the analysis results. When analyzing overall temperature change data, the positional influence value of the local driving structure is adjusted according to the proportion of the coverage area corresponding to the temperature diffusion trend of the local driving structure within the physical structure range of the energy router.

9. The method for assessing the health status of multiple types of information equipment according to claim 8, characterized in that: Based on the topology between each energy router, a preset verification command is sent to the energy router. After receiving the verification command, the energy router obtains the time consumed by executing the verification command locally before executing the verification command, based on its local health status, and uses it as the estimated verification time. After executing the verification command, the energy router obtains the time consumed by executing the verification command locally as the actual verification time; The energy router integrates the verification command, the preset local unique identifier, the local health status, the estimated verification time and the actual verification time into a verification record, and sends the verification record to the next energy router. After receiving the verification record, the energy router uses it as a reference record; it retrieves the verification command from the reference record, and obtains the estimated verification time, local health status, and actual verification time for the verification command; it uses the difference between the estimated and actual verification times in the reference record as a reference difference, and the health status in the reference record as a reference health status; it uses the difference between the estimated and actual verification times for the verification command as a control difference, and the local health status as a control health status; based on the correspondence between the reference health status and the reference difference, and the control health status, it obtains the difference corresponding to the control health status, compares the obtained difference with the control difference, and evaluates the confidence value of the local health status based on the comparison results.

10. A health status assessment system for multiple types of information devices, characterized in that, The method for assessing the health status of multiple types of information devices as described in any one of claims 1-9 was used.

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