Intelligent humidity control tool cabinet data management method and system

Abstract

The application relates to the technical field of humidity control tool cabinet management, and provides an intelligent humidity control tool cabinet data management method and system, which comprises the following steps: acquiring tool cabinet information of a humidity control tool cabinet in a sliding time window before a current time; determining a humidity risk index of each storage partition in the humidity control tool cabinet in the sliding time window according to the tool cabinet information; determining a moisture index of each tool stored in the humidity control tool cabinet according to the humidity risk indexes of the multiple sliding time windows; and determining a management strategy of the humidity control tool cabinet according to the moisture indexes of all the tools in the humidity control tool cabinet. Through the scheme, the efficiency of humidity control tool cabinet data management can be improved.

Description

Technical Field

[0001] This application relates to the field of humidity control tool cabinet management technology, and in particular to an intelligent humidity control tool cabinet data management method and system. Background Technology

[0002] In industries such as power, intelligent humidity-controlled tool cabinets are widely used for centralized storage to ensure the safety of insulating tools and precision equipment. These tool cabinets are typically equipped with temperature and humidity sensors and dehumidification devices to maintain a constant, dry environment inside.

[0003] With the widespread adoption of the Industrial Internet, numerous intelligent tool cabinets are often deployed in substations and production workshops. Each cabinet operates independently, generating massive amounts of heterogeneous data, including cabinet door opening counts, ambient temperature and humidity, and equipment operating status. Furthermore, due to the lack of a unified collection and integration mechanism, this data is distributed in isolated silos. Managers struggle to integrate and analyze these environmental parameters and equipment operating data across different devices, making it impossible to monitor the moisture risk of tools within the area in real time. This limits the ability to perform refined preventative maintenance throughout the tool's entire lifecycle. Existing management methods are insufficient for data-driven dynamic calculation of tool moisture indices and automatic optimization of management strategies, resulting in low management efficiency. Summary of the Invention

[0004] This application provides a data management method and system for intelligent humidity control tool cabinets, aiming to solve the problem that existing technologies lack a unified data integration and collaboration mechanism, which makes it impossible to conduct cross-device comprehensive analysis and refined full life cycle management of a large number of intelligent humidity control tool cabinets in a region.

[0005] The technical solution of this application is as follows: In a first aspect, this application discloses a data management method for an intelligent humidity-controlled tool cabinet, comprising: acquiring tool cabinet information of the humidity-controlled tool cabinet within a sliding time window prior to the current moment; the tool cabinet information includes the number of times the cabinet door is opened, the average ambient humidity of the workshop, the average humidity inside the cabinet, the operating information of the dehumidification module, the center location information of multiple storage partitions, and the location information of the dehumidification module; the humidity-controlled tool cabinet is used to store tools; determining the moisture risk index of each storage partition in the humidity-controlled tool cabinet within the sliding time window based on the tool cabinet information; determining the moisture index of each tool stored in the humidity-controlled tool cabinet based on the moisture risk index of multiple sliding time windows; and determining the management strategy of the humidity-controlled tool cabinet based on the moisture index of all tools in the humidity-controlled tool cabinet.

[0006] Optionally, the humidity risk index of each storage area within the humidity-controlled tool cabinet within the sliding time window is determined based on the tool cabinet information, including: when the average ambient humidity is less than or equal to the average cabinet humidity, the external humidity risk index is set to 0; when the average ambient humidity is greater than the average cabinet humidity, the difference between the average ambient humidity and the average cabinet humidity is taken as the humidity deviation; the product of the first value and the second value is taken as the external humidity risk index of the humidity-controlled tool cabinet; the first value is the ratio of the number of times the cabinet door is opened to the preset maximum number of times the cabinet door is opened, and the second value is the product of the humidity deviation and the preset humidity deviation coefficient; based on the external humidity risk index and the tool cabinet information, the humidity risk index of each storage area within the humidity-controlled tool cabinet within the sliding time window is determined.

[0007] Optionally, the dehumidification module's operating information includes the actual average dehumidification power. Based on the external humidity risk index and tool cabinet information, the humidity risk index of each storage zone within the humidity-controlled tool cabinet within the sliding time window is determined, including: obtaining a first correspondence; the first correspondence includes a one-to-one correspondence between multiple average cabinet humidity ranges and multiple expected dehumidification powers; taking the expected dehumidification power corresponding to the average cabinet humidity range of the humidity-controlled tool cabinet in the first correspondence as the target expected dehumidification power; determining the internal humidity risk index of the humidity-controlled tool cabinet based on the target expected dehumidification power and the actual average dehumidification power; and determining the humidity risk index of each storage zone within the humidity-controlled tool cabinet within the sliding time window based on the external humidity risk index, the internal humidity risk index, the center location information of each storage zone in the multiple storage zones of the humidity-controlled tool cabinet, and the location information of the dehumidification module.

[0008] Optionally, the dehumidification module's operating information includes working time. Based on the target expected dehumidification power and the actual average dehumidification power, the internal humidity risk index of the humidity control tool cabinet is determined, including: determining whether the actual average dehumidification power is greater than the target expected dehumidification power; when the actual average dehumidification power is less than or equal to the target expected dehumidification power, the internal humidity risk index is determined to be 0; when the actual average dehumidification power is greater than the target expected dehumidification power, the product of the third and fourth values ​​is used as the internal humidity risk index; the third value is the ratio of working time to the duration of the sliding time window, the fourth value is the product of the fifth value and the preset power deviation coefficient, and the fifth value is the difference between the actual average dehumidification power and the target expected dehumidification power.

[0009] Optionally, based on the external humidity risk index, the internal humidity risk index, the center location information of each storage partition in the multiple storage partitions of the humidity control tool cabinet, and the location information of the dehumidification module, the humidity risk index of each storage partition in the humidity control tool cabinet within the sliding time window is determined, including: taking the average of the external humidity risk index and the internal humidity risk index as the overall humidity index; taking the distance between the center location information of each storage partition and the location information of the dehumidification module as the dehumidification distance of each storage partition; taking the product of the dehumidification distance of each storage partition and a preset distance coefficient as the distance humidity risk value of each storage partition; and taking the average of the distance humidity risk value of each storage partition and the overall humidity index as the humidity risk index of each storage partition in the humidity control tool cabinet within the sliding time window.

[0010] Optionally, the moisture index of the tool is determined based on the moisture risk index of multiple sliding time windows, including: obtaining the storage information of the tool in the humidity-controlled tool cabinet, the storage information including multiple actual storage partitions of the tool in multiple storage zones and the storage time period in each actual storage partition; for each actual storage partition, integrating the moisture risk index within the storage time period corresponding to the actual storage partition, and obtaining and using the integrated value as the sub-moisture index of the tool in the actual storage partition; using the sum of the multiple sub-moisture indices corresponding to the tool as the initial moisture index of the tool; and determining the moisture index of the tool based on the initial moisture index of the tool.

[0011] Optionally, the moisture index of the tool is determined based on the initial moisture index of the tool, including: obtaining the volume of the tool and the surface complexity index of the tool; using the product of the volume and a preset volume coefficient as the volume adjustment coefficient; using the product of the surface complexity index and a preset complexity coefficient as the shape adjustment coefficient; determining the moisture index of the tool based on the initial moisture index and the target adjustment coefficient; the target adjustment coefficient is the average of the volume adjustment coefficient and the shape adjustment coefficient.

[0012] Optionally, the moisture index of the tool is determined based on the initial moisture index and the target adjustment coefficient, including: multiplying the initial moisture index and the target adjustment coefficient as the intermediate moisture index of the tool; obtaining the preset moisture sensitivity index of the tool; and multiplying the preset moisture sensitivity index and the intermediate moisture index as the moisture index of the tool.

[0013] Optionally, the management strategy for the humidity-controlled tool cabinet is determined based on the moisture index of all tools in the cabinet, including: determining whether the average moisture index of all tools in the cabinet is greater than a first preset moisture index; if the average moisture index of all tools in the cabinet is greater than the first preset moisture index, determining the management strategy for the humidity-controlled tool cabinet to notify maintenance personnel to repair the cabinet; if the average moisture index of all tools in the cabinet is less than or equal to the first preset moisture index, determining whether there is a target tool among all tools in the cabinet with a moisture index greater than the first preset moisture index; if there is no target tool among all tools in the cabinet with a moisture index greater than the first preset moisture index, determining the management strategy for the humidity-controlled tool cabinet to continue using it. When a target tool with a moisture index greater than a first preset moisture index is found among all tools in the humidity control tool cabinet, the system obtains and determines whether the usage time of the target tool exceeds a preset time threshold. If the usage time of the target tool exceeds the preset time threshold, the management strategy for the humidity control tool cabinet is to notify maintenance personnel to dry the target tool in the humidity control tool cabinet. If the usage time of the target tool is less than or equal to the preset time threshold, the management strategy for the humidity control tool cabinet is to notify the user to store the target tool in the target storage partition. The target storage partition is the storage partition among multiple storage partitions whose dehumidification distance is less than the dehumidification distance of the storage partition where the target tool is currently located. The dehumidification distance of the storage partition is the distance between the center location information of the storage partition and the location information of the dehumidification module.

[0014] Secondly, this application discloses an intelligent humidity-controlled tool cabinet data management system, comprising: an acquisition device and a processing device; the acquisition device is used to acquire tool cabinet information of the humidity-controlled tool cabinet within a sliding time window prior to the current moment; the tool cabinet information includes the number of times the cabinet door of the humidity-controlled tool cabinet is opened, the average ambient humidity of the workshop where the humidity-controlled tool cabinet is located, the average humidity inside the humidity-controlled tool cabinet, the operating information of the dehumidification module inside the humidity-controlled tool cabinet, the center position information of each storage partition in the multiple storage partitions of the humidity-controlled tool cabinet, and the position information of the dehumidification module; the humidity-controlled tool cabinet is used to store tools; the processing device is used to determine the moisture risk index of each storage partition in the humidity-controlled tool cabinet within the sliding time window based on the tool cabinet information; the processing device is also used to determine the moisture index of each tool stored in the humidity-controlled tool cabinet based on the moisture risk index of multiple sliding time windows; the processing device is also used to determine the management strategy of the humidity-controlled tool cabinet based on the moisture index of all tools in the humidity-controlled tool cabinet.

[0015] Beneficial effects This application provides a data management method for intelligent humidity-controlled tool cabinets. By collecting environmental parameters, dehumidification operation information, and zoning distribution information of the tool cabinet within a sliding time window, a multi-dimensional, spatial, and time-cumulative risk assessment system is established. This solution dynamically quantifies the humidity risk of each zone by calculating external humidity deviation, internal dehumidification power differences, and zoning dehumidification distances; it also calculates the moisture index by integrating tool storage history and calibrates the system based on tool characteristics. This application effectively integrates fragmented tool cabinet operation data, automatically generating intelligent management strategies from maintenance suggestions to tool location adjustments, thereby breaking the data silo effect in existing technologies and achieving scientific and refined preventative maintenance throughout the tool lifecycle. Attached Figure Description

[0016] Figure 1 A flowchart illustrating a data management method for an intelligent humidity control tool cabinet provided in this application; Figure 2 A schematic diagram of the architecture of an intelligent humidity control tool cabinet data management system provided in this application; Figure 3 This is a schematic diagram of the architecture of another intelligent humidity control tool cabinet data management system provided in this application. Detailed Implementation

[0017] The technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application. It should be noted that similar reference numerals and letters in the following drawings indicate similar items; therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0018] like Figure 1 As shown, the technical solution disclosed in this embodiment is as follows: S101. Obtain the tool cabinet information of the humidity control tool cabinet within the sliding time window before the current moment.

[0019] The tool cabinet information includes the number of times the cabinet door of the humidity control tool cabinet is opened, the average ambient humidity of the workshop where the humidity control tool cabinet is located, the average humidity inside the humidity control tool cabinet, the operating information of the dehumidification module inside the humidity control tool cabinet, the center location information of each storage compartment in the multiple storage compartments of the humidity control tool cabinet, and the location information of the dehumidification module; the humidity control tool cabinet is used to store tools.

[0020] S102. Determine the moisture risk index of each storage area in the humidity-controlled tool cabinet within the sliding time window based on the tool cabinet information.

[0021] S103. For each tool stored in the humidity control tool cabinet, determine the moisture index of the tool based on the moisture risk index of multiple sliding time windows.

[0022] S104. Determine the management strategy for the humidity control tool cabinet based on the moisture index of all tools in the humidity control tool cabinet.

[0023] The following will combine Figure 2 This application provides further explanation of the proposed solution.

[0024] Regarding the core technical approach of "determining the moisture risk index of each storage area within a sliding time window of a humidity-controlled tool cabinet based on tool cabinet information," the system needs to establish a mapping relationship between tool cabinet information and moisture risk. In some implementations, this can be achieved by pre-constructing a basic weighted scoring model. To make this mapping relationship feasible, the pre-construction process can be completed by combining historical operational data processing with manual calibration. Specifically, historical data on a batch of tool cabinets under different environmental conditions, different door opening frequencies, and different dehumidification states can be collected first. Combined with actual inspection records, records of excessive humidity inside the cabinet, or records of tools getting damp, these historical periods can be marked as low-risk, medium-risk, or high-risk. Subsequently, based on the strength of the influence of each factor on risk changes, the relative contribution of each factor in the basic score can be determined. If certain specific weight values ​​differ in different scenarios, they can be recorded as weight coefficients A, B, C, and D, respectively. The average humidity inside the cabinet usually corresponds to a higher influence weight, while the number of cabinet door openings, average ambient humidity, and dehumidification module operating status correspond to auxiliary influence weights. During system operation, the system first normalizes or ranks all input data under the same dimension, then combines them according to preset weights to obtain a basic risk value. The "normalization or rank processing" can be implemented in a readily implementable manner, such as classifying cabinet door opening frequency as low-frequency, medium-frequency, and high-frequency; average cabinet humidity as normal, slightly high, and relatively high; and dehumidification module operating status as normal, slightly high load, and abnormal, thus avoiding bias caused by directly adding data from different dimensions. Subsequently, the straight-line distance between the center location information of each storage partition and the location information of the dehumidification module is calculated. For storage partitions with greater distances, a spatial correction value corresponding to the distance level is added to the basic risk value, thus obtaining the humidity risk index for each storage partition. Here, a "distance-level correction" approach is preferred over simply setting a single fixed penalty value. That is, the distance between the partition and the dehumidification module can be first divided into near-distance, medium-distance, and far-distance zones, and then assigned low, medium, and high spatial correction values ​​respectively. The reason for this approach is that the airflow distribution inside the cabinet is usually not a strictly linear change. Using a graded correction makes it easier to match the actual dehumidification coverage effect and also facilitates subsequent adjustments based on the on-site operating conditions.

[0025] Furthermore, regarding the core technical approach of "determining the moisture index of each tool stored in the humidity-controlled tool cabinet based on the moisture risk index across multiple sliding time windows," the system needs to track the tool's storage trajectory. In one possible example, RFID technology can be used to record the access time of tools in different storage compartments. In specific implementation, a unique RFID tag can be attached to each tool, and RFID identification devices can be placed near each storage compartment, or an identification device can be placed at the cabinet door and combined with a compartment sensing unit to record the actual storage location of the tool after it enters or leaves. Each time a tool tag is identified, the system generates a record containing the tool identifier, timestamp, storage compartment identifier, and event type. Event types can include placement, removal, and relocation. The system extracts the storage compartment where the tool has been located over the past week, directly sums the moisture risk indices of that storage compartment across various sliding time windows, and uses the summed result as the tool's moisture index. To make this process clearer, in practice, the system can first organize all access records of a tool in chronological order to recover the storage partition where the tool was located during each time period. Then, these dwell time periods are mapped one by one to sliding time windows to determine which partition the tool mainly occupied in each window, or whether it occupied multiple partitions successively within a single window. If a tool experienced multiple partitions within the same sliding time window, the risk contribution within that window can be allocated according to the length of dwell time; that is, partitions with longer dwell times contribute more to the moisture accumulation in that window. If the system implementation aims to simplify the process, it can also adopt the method of "taking the partition where the window ended as the partition to which the window belongs," but the method of allocating based on dwell time is preferred because it better reflects the actual process of the tool being exposed to different microenvironments. The past week timeframe is suitable for common periodic inspections and tool usage management scenarios; if the actual management cycle is different, the statistical period can also be set to time period P to adapt to the operation and maintenance systems of different units.

[0026] Regarding the core technical approach of "determining the management strategy for the moisture-controlled tool cabinet based on the moisture index of all tools in the cabinet," the system can make decisions by setting thresholds. To ensure the feasibility of these thresholds, the preset thresholds can be determined jointly through historical operational data statistics, tool maintenance records, and on-site management requirements. Specifically, the system calculates the arithmetic mean of the moisture index of all tools in the cabinet. When this average exceeds the preset safety threshold, the system automatically generates a text alarm message containing the tool cabinet number and location information, and sends it to workshop managers via email or SMS, prompting them to manually inspect the tool cabinet. When the average is below the safety threshold, the system records the status as normal in the background, without triggering any action. Furthermore, to avoid masking individual high-risk tools based solely on the average, the management strategy can also consider the maximum moisture index or the number of high-risk tools. For example, even if the average does not exceed the safety threshold, but the moisture index of a particular tool remains consistently high, the system can still generate a local warning message for that tool. The alarm message can include the tool cabinet number, installation area, abnormal time, abnormality type, suggested handling actions, and a list of associated tools. Suggested actions could include checking the cabinet door seal, verifying the dehumidification module's operation, retesting the humidity inside the cabinet, and re-testing the insulation performance of high-risk tools. With this setup, the management strategy is no longer simply "alarm or not alarm," but can provide more actionable recommendations.

[0027] Through the aforementioned overall technical solution, this application effectively solves the technical problem of scattered data and difficulty in cross-device comprehensive analysis of intelligent humidity control tool cabinets in the industrial internet environment. Its working principle lies in breaking the limitations of monitoring single environmental parameters and unifying and integrating multi-source heterogeneous data such as external environment, internal environment, human operation, equipment status, and spatial layout. Through a sliding time window mechanism, dynamic interception and analysis of continuous data streams are achieved, enabling the assessment process to closely follow real-time changes in environmental and equipment status. Based on this, macroscopic tool cabinet environmental data is refined to the humidity risk index of each storage zone and further mapped to the moisture index of specific tools, establishing a complete data analysis link from environmental monitoring to spatial assessment and individual tracking. This logical closed loop allows the management system to accurately quantify the moisture status of each tool based on multi-dimensional objective data, thereby automatically generating global management strategies. Therefore, this technical solution can achieve in-depth mining and collaborative analysis of massive tool cabinet operating data, providing reliable data-driven support for the full lifecycle management and preventative maintenance of tools. Further analysis of its technical effects reveals that the sliding time window eliminates the system's reliance on single-point instantaneous data, thus reducing the interference of occasional sampling fluctuations on the judgment results. The introduction of spatial location information enables the system to distinguish the risk differences between different zones within the same cabinet, thereby avoiding the simplistic view of the entire cabinet as a uniform environment. The correlation between tool trajectory and zone risk allows the system to truly implement environmental risks onto specific tool objects, thus the output results can directly serve tool maintenance and allocation decisions. It is precisely because of the seamless integration of these multiple links that the system possesses comprehensive analytical capabilities across devices, time periods, and objects.

[0028] Optionally, in some preferred embodiments, determining the humidity risk index of each storage compartment within the humidity-controlled tool cabinet within the sliding time window based on the tool cabinet information includes: determining the external humidity risk index as 0 when the average ambient humidity is less than or equal to the average cabinet humidity; taking the difference between the average ambient humidity and the average cabinet humidity as the humidity deviation when the average ambient humidity is greater than the average cabinet humidity; taking the product of a first value and a second value as the external humidity risk index of the humidity-controlled tool cabinet; the first value is the ratio of the number of times the cabinet door is opened to the preset maximum number of times the cabinet door is opened, and the second value is the product of the humidity deviation and the preset humidity deviation coefficient; and determining the humidity risk index of each storage compartment within the humidity-controlled tool cabinet within the sliding time window based on the external humidity risk index and the tool cabinet information.

[0029] In practical applications, the risk of moisture buildup inside tool cabinets largely stems from the intrusion of external humidity. When using a basic weighted scoring model to assess this risk, parameters are often treated as independent factors and linearly superimposed. However, in a real physical environment, opening the cabinet door itself does not directly generate moisture; the risk arises only when the external humidity is higher than the internal humidity. If the external environment is very dry, frequent opening of the cabinet door will not cause dampness inside, and may even help dissipate moisture. To more accurately reflect the real threat posed by external moisture intrusion to the cabinet's internal environment, an assessment mechanism is needed that reflects the coupling relationship between environmental humidity differences and human operation.

[0030] This specific implementation plan details the calculation logic of the external humidity risk index. First, the system compares the average ambient humidity and the average cabinet humidity. When the average ambient humidity is less than or equal to the average cabinet humidity, it means the external air is relatively dry. Opening the cabinet door in this case will not introduce additional moisture, so the external humidity risk index is directly set to 0. Here, the "average ambient humidity" and "average cabinet humidity" are preferably the average values ​​within the same sliding time window to ensure consistent comparison benchmarks. If the data acquisition system supports finer-grained analysis, a sequence can be formed first using shorter sampling periods, and then summarized within the window. When the average ambient humidity is greater than the average cabinet humidity, the system calculates the difference as the humidity deviation, which directly reflects the degree to which external moisture penetrates into the cabinet. Next, the system calculates the first value, the ratio of the number of times the cabinet door is opened to the preset maximum number of times the cabinet door is opened. This ratio reflects the frequency with which the moisture intrusion channel is opened. The preset maximum number of cabinet door openings can be obtained through historical operational statistics. Specifically, this can be achieved by continuously collecting daily door opening data for multiple tool cabinets over a specific operational period, excluding obviously abnormal days such as maintenance or concentrated handling operations, and selecting the upper limit value that represents normal high-frequency usage as the preset maximum number of cabinet door openings. If usage habits differ significantly between different sites or work groups, corresponding values ​​can be set separately for each site or tool cabinet type. The purpose of this setting is to ensure that the first value reflects the "level of door opening activity relative to the normal upper limit," rather than considering the absolute number of openings in isolation. Simultaneously, the system calculates a second value, which is the product of humidity deviation and a preset humidity deviation coefficient. The preset humidity deviation coefficient is used to convert humidity differences into risk sensitivity and can be determined through historical data playback: first, select several known periods where cabinet humidity has significantly increased or the risk of tools becoming damp has increased, observe the difference between ambient humidity and cabinet humidity during these periods, and then determine a coefficient range that can effectively distinguish between low-risk and high-risk states. If a specific value is difficult to standardize, this coefficient can be denoted as coefficient Q. Finally, multiply the first value by the second value to obtain the external humidity risk index. To facilitate system implementation, the product result can be further mapped to several risk levels, such as low, medium, and high, or retained as a continuous value for subsequent partition risk calculation.

[0031] Through this specific implementation plan, the system can accurately quantify the moisture threat posed by the combined effects of the external environment and human operation. Its core principle lies in introducing a conditional judgment and product coupling mechanism, abandoning simple linear weighting. External moisture risk only arises when the external humidity is higher than the internal humidity and the cabinet door is opened, and the magnitude of the risk is positively correlated with the humidity difference and the frequency of opening. This non-linear assessment logic highly aligns with the physical laws of moisture exchange, effectively avoiding misjudgments of risk caused by frequent door openings in dry environments. Furthermore, since the first value reflects the degree to which the moisture entry channel is opened, and the second value reflects the driving force of external moisture migration inward, both must coexist to constitute a true external moisture intrusion condition. Therefore, this scheme can distinguish between situations of "channels without moisture driving" and "moisture driving but no channel open" from truly high-risk situations, thus significantly improving the accuracy and scientific rigor of the moisture risk index assessment.

[0032] Optionally, in some preferred embodiments, the operating information of the dehumidification module includes the actual average dehumidification power. Based on the external humidity risk index and tool cabinet information, the humidity risk index of each storage compartment within the humidity-controlled tool cabinet within the sliding time window is determined, including: obtaining a first correspondence; the first correspondence includes a one-to-one correspondence between multiple average cabinet humidity ranges and multiple expected dehumidification powers; taking the expected dehumidification power corresponding to the average cabinet humidity range of the humidity-controlled tool cabinet in the first correspondence as the target expected dehumidification power; determining the internal humidity risk index of the humidity-controlled tool cabinet based on the target expected dehumidification power and the actual average dehumidification power; and determining the humidity risk index of each storage compartment within the humidity-controlled tool cabinet within the sliding time window based on the external humidity risk index, the internal humidity risk index, the center location information of each storage compartment in the multiple storage compartments of the humidity-controlled tool cabinet, and the location information of the dehumidification module.

[0033] When assessing the moisture risk of a tool cabinet, in addition to considering the intrusion of external moisture, it is also necessary to evaluate the health of the cabinet's own dehumidification capacity. Measuring its operational status solely by the cumulative runtime or number of times the dehumidification module is turned on often fails to accurately reflect its dehumidification efficiency. For example, when the molecular sieve inside the dehumidification module ages or the fan experiences a minor malfunction, its runtime may still be long, but the actual dehumidification power has significantly decreased, resulting in ineffective moisture removal from the cabinet. To promptly identify the hidden moisture risks caused by dehumidification module performance degradation, an assessment mechanism that compares theoretical dehumidification requirements with actual dehumidification output is needed.

[0034] This specific implementation plan further refines the process for determining the internal humidity risk index. In one possible example, the system pre-builds and maintains a first correspondence table in memory. This table records the theoretical power requirements under different humidity environments. To avoid introducing unverified fixed values, it is preferable to establish this correspondence table separately according to the tool cabinet model, cabinet volume, dehumidification module specifications, and target humidity control requirements. The establishment process can be carried out as follows: first, conduct phased tests on the prototype or the tool cabinet already in operation, and record the typical power level corresponding to maintaining stable or gradually decreasing humidity in the cabinet when the dehumidification module is in a normal and healthy state under different average cabinet humidity ranges; then, combined with the equipment factory parameters, operation logs, and on-site verification results, organize the "average cabinet humidity range - expected dehumidification power" into a structured table and store it in the system. The table entry format can be: "Humidity range H1, corresponding expected dehumidification power P1; Humidity range H2, corresponding expected dehumidification power P2; Humidity range H3, corresponding expected dehumidification power P3", where the expected dehumidification power corresponding to subsequent humidity ranges is usually not lower than the expected dehumidification power corresponding to the previous lower humidity range. This setting conforms to the basic law that dehumidification demand increases with the humidity level. Within the sliding time window, the system first reads the current average cabinet humidity, matches it to the corresponding average cabinet humidity range by looking up the table, and thus obtains the target expected dehumidification power. The table lookup can use an interval matching method, that is, determining which preset humidity range the current average cabinet humidity falls into and directly reading the expected dehumidification power corresponding to that range. If the current humidity happens to be at the boundary of two intervals, it can be processed according to the pre-set boundary assignment rules to ensure consistent system output.

[0035] Subsequently, the system obtains the actual average dehumidification power within the sliding time window using a smart meter deployed on the power supply circuit of the dehumidification module. This actual average dehumidification power can be obtained by averaging multiple power sampling points within the window, or it can be directly output by a power monitoring unit with statistical functions. If the dehumidification module has start-stop switching, it is preferable to only count the power sampling value when it is in the working state to avoid standby power consumption diluting the true dehumidification output. The system analyzes the difference between the target expected dehumidification power and the actual average dehumidification power to determine the internal humidity risk index. More specifically, when the actual average dehumidification power reaches or approaches the target expected dehumidification power, it can be determined that the dehumidification module output is basically normal and the internal humidity risk index is low; when the actual average dehumidification power is consistently lower than the target expected dehumidification power, it can be determined that the dehumidification module has performance degradation, power supply abnormalities, control abnormalities, or airflow obstruction, and the internal humidity risk index increases accordingly. To facilitate system implementation, this difference can be further divided into multiple levels, such as normal deviation, slight deviation, and significant deviation, each corresponding to a different internal humidity risk level. Finally, the system integrates the external and internal humidity risk indices, along with the center location information of each storage zone and the location information of the dehumidification module, to calculate the final humidity risk index for each storage zone. In practice, a cabinet-level comprehensive risk can be first established, and then secondary allocation can be performed based on the spatial relationship between the zones and the dehumidification modules; alternatively, external and internal risk components can be established separately, and then the two types of risk components can be superimposed and corrected according to spatial location. If some zones are located in areas with weak airflow coverage, their final humidity risk index may be higher than the average level of the cabinet.

[0036] Through this specific implementation plan, the system achieves dynamic monitoring and risk quantification of the dehumidification efficiency inside the tool cabinet. Its core principle lies in establishing a dynamic benchmark based on the current humidity environment and comparing it in real time with the actual operating performance of the equipment. This comparison mechanism can keenly detect performance degradation of the dehumidification module due to aging, filter clogging, or component failure. When the actual power deviates from the expected power, the system can convert it into an internal humidity risk index, thus providing early warning of potential moisture risks before the humidity inside the cabinet deteriorates significantly. Further analysis shows that if only the humidity inside the cabinet is observed, problems are often only discovered after the humidity has increased significantly; however, by comparing the "expected dehumidification power - actual average dehumidification power," anomalies can be identified when dehumidification capacity begins to decline but the environment is not completely out of control, thus providing earlier warnings. Combined with comprehensive calculations based on spatial location information, the system further improves the precision of risk assessment in the three-dimensional spatial distribution within the cabinet, enabling the system not only to determine "whether there is a risk in the cabinet" but also "which storage areas are more likely to be at risk," thus providing a more direct basis for subsequent tool allocation, key re-inspections, and dehumidification module maintenance.

[0037] In some preferred embodiments, the dehumidification module's operating information includes operating time. Based on the target expected dehumidification power and the actual average dehumidification power, the internal humidity risk index of the humidity control tool cabinet is determined, including: determining whether the actual average dehumidification power is greater than the target expected dehumidification power; when the actual average dehumidification power is less than or equal to the target expected dehumidification power, the internal humidity risk index is determined to be 0; when the actual average dehumidification power is greater than the target expected dehumidification power, the product of the third value and the fourth value is used as the internal humidity risk index; the third value is the ratio of the operating time to the duration of the sliding time window, the fourth value is the product of the fifth value and the preset power deviation coefficient, and the fifth value is the difference between the actual average dehumidification power and the target expected dehumidification power.

[0038] This specific implementation plan details the quantitative calculation method for the internal humidity risk index. The system first compares the actual average dehumidification power with the target expected dehumidification power. When the actual average dehumidification power is less than or equal to the target expected dehumidification power, it indicates that the current operating state of the dehumidification module is sufficient to handle the humidity load inside the cabinet, and there is no overload operation; therefore, the system directly determines the internal humidity risk index to be 0. When the actual average dehumidification power is greater than the target expected dehumidification power, the system quantifies the risk by calculating the product of a third value and a fourth value. The third value is the ratio of the working time to the duration of the sliding time window. This ratio reflects the proportion of time the dehumidification module is in operation within the current time window, i.e., the cumulative weight of the time dimension. The working time here can be calculated by reading at least one of the following from the controller: the dehumidification module relay closing status, compressor start / stop status, heating / regeneration unit start / stop status, or fan operation status. When the dehumidification module type is a condensing dehumidification module, the effective working time can be primarily determined by the time period when the compressor and fan are running simultaneously. When the dehumidification module type is an adsorption dehumidification module, the adsorption working stage and the regeneration stage can be recorded separately, and the stage actually involved in dehumidification can be included in the working time statistics. The fifth value is the difference between the actual average dehumidification power and the target expected dehumidification power, which directly reflects the absolute degree of power overload. The fourth value is the product of the fifth value and the preset power deviation coefficient, used to convert the power difference into risk sensitivity. The preset power deviation coefficient can be calibrated based on the rated power of the dehumidification module and historical fault data to ensure that the converted risk value is within a reasonable dimension. Specifically, several typical states can be selected from historical operation records, including normal state, slightly inefficient state, and obviously abnormal state. The typical range in which the actual average dehumidification power exceeds the target expected dehumidification power under each state can be recorded. Then, the risk level is determined by combining inspection results or disassembly testing results. Finally, the power difference is compared and calibrated with the corresponding risk level to obtain a preset power deviation coefficient. If sufficient historical samples are lacking at the beginning of the project, an initial calibration value can be given based on the dehumidification module's rated power, design margin, and operation and maintenance experience, and then corrected as operational data accumulates. The advantage of this setup is that the internal humidity risk index no longer reflects only the power deviation at a single moment, but rather the comprehensive risk formed by the equipment continuously under pressure throughout the entire sliding time window.

[0039] The working principle of this specific implementation scheme lies in combining the transient performance of power deviation with the steady-state accumulation of operating time to construct a two-dimensional risk assessment model. When the actual power exceeds expectations, the system not only focuses on how much it exceeds (i.e., the fifth value), but also on how long this overload state lasts (i.e., the third value). By converting the two into a product relationship, the system can effectively filter out power spike interference caused by short-term environmental fluctuations or the moment the equipment starts up. Combined with the application scenarios mentioned above, this calculation logic can accurately identify dehumidification modules that are in a state of "underpowered operation" for a long time. When the dehumidification module suffers from low dehumidification efficiency due to severe filter blockage or large-scale failure of molecular sieves, the controller often forcibly extends its operating time and increases its operating power in order to maintain the humidity inside the cabinet. This scheme can keenly capture this abnormal characteristic, converting prolonged high-power operation into a higher internal humidity risk index, thereby providing accurate early warning information to managers before the equipment completely shuts down or the humidity inside the cabinet becomes completely out of control, significantly improving the pertinence of preventive maintenance. Furthermore, in practical deployments, the controller can perform simple preprocessing on the raw power data. For example, it can remove surge sampling values ​​within the first few seconds of device power-on, or mark abnormal sampling values ​​that clearly exceed the hardware's rated range as invalid, to avoid unnecessary amplification of the internal humidity risk index due to sampling noise. Through this processing, the internal humidity risk index can better reflect the true and continuous dehumidification load status of the dehumidification module.

[0040] Optionally, based on the external humidity risk index, the internal humidity risk index, the center location information of each storage partition in the multiple storage partitions of the humidity control tool cabinet, and the location information of the dehumidification module, the humidity risk index of each storage partition in the humidity control tool cabinet within the sliding time window is determined, including: taking the average of the external humidity risk index and the internal humidity risk index as the overall humidity index; taking the distance between the center location information of each storage partition and the location information of the dehumidification module as the dehumidification distance of each storage partition; taking the product of the dehumidification distance of each storage partition and a preset distance coefficient as the distance humidity risk value of each storage partition; and taking the average of the distance humidity risk value of each storage partition and the overall humidity index as the humidity risk index of each storage partition in the humidity control tool cabinet within the sliding time window.

[0041] The "center location information" here can be understood as the representative position of each storage compartment in the cabinet coordinate system, generally selected as the spatial coordinates of the geometric center point of that storage compartment; the "dehumidification module location information" can be understood as the position of the center of the dehumidification module's air outlet, air inlet, or the center of the module body in the same coordinate system. If the airflow inside the cabinet is mainly dominated by the air outlet, the center position of the air outlet is preferred as the calculation benchmark; if the structure is more suitable to represent it using the overall installation position of the module, the center position of the module body can also be used. By unifying the coordinate benchmark, the system can further refine cabinet-level risks to each spatial location.

[0042] This specific implementation plan clarifies the concrete steps for dimensionality reduction mapping from cabinet-level risk to zone-level risk. First, the system arithmetically averages the external and internal humidity risk indices to obtain an overall humidity index representing the current overall humidity threat level of the tool cabinet. Then, using pre-entered 3D coordinate data, the system calculates the spatial straight-line distance between the center location information of each storage zone and the location information of the dehumidification module, defining this as the dehumidification distance. 3D coordinate data can be obtained in several ways. During the cabinet production stage, the installation coordinates of each storage zone and dehumidification module can be directly determined based on the structural design drawings and written into the system as initial data. During the on-site installation stage, installers can measure the positions of key points inside the cabinet using electronic tape measures, laser rangefinders, or 3D scanning equipment, and then the data is entered into the database by the configuration software. To ensure coordinate consistency, the lower left rear corner of the cabinet or a fixed mounting hole on the bottom plate can be set as the origin, with horizontal, vertical, and longitudinal coordinate axes defined respectively, and all positions recorded based on the same origin. A longer dehumidification distance means a longer path for the drying airflow to reach that zone, resulting in greater losses.

[0043] Next, the system multiplies the dehumidification distance by a preset distance coefficient to obtain the distance-based humidity risk value. The preset distance coefficient is an adjustment parameter used to convert physical distance into a risk increment. Its value can be experimentally determined based on the cabinet's air duct design, fan airflow, and the permeability of the partitions. Specifically, it can be calibrated as follows: select multiple different locations of the distance-based dehumidification modules within the same cabinet, maintain consistent external environmental conditions, continuously monitor the humidity recovery rate or steady-state humidity differences at each point, and then compare these differences with the corresponding distances. Select the coefficient value that best reflects the spatial decay trend as the preset distance coefficient. If there are obvious obstructions such as partitions, drawers, or deflectors inside the cabinet, different preset distance coefficients can be established according to the cabinet type during calibration. Finally, the system averages the distance-based humidity risk value specific to each storage zone with the overall humidity index shared by the entire cabinet to obtain the final humidity risk index for each storage zone. Through this process, even if the external and internal humidity risk indices are the same across the entire cabinet, zones in different spatial locations can still exhibit differentiated risk levels.

[0044] The working principle of this specific implementation plan lies in constructing an assessment framework of "basic risk + spatial correction" through two averaging processes and one distance-weighted averaging. The first averaging integrates external moisture intrusion and internal dehumidification efficiency into a macro benchmark; distance weighting quantifies the obstruction effect of physical space into specific risk increments; the second averaging merges the macro benchmark with local increments, so that the final risk index includes both the overall environmental deterioration trend of the cabinet and highlights the microenvironmental disadvantages of local spaces. Combined with the application scenarios mentioned earlier, this approach perfectly matches the airflow dynamics characteristics within the cabinet. When the cabinet door is frequently opened or the dehumidification module's performance declines, the overall humidity index increases. At this time, storage areas farther from the dehumidification module often experience much faster microenvironmental deterioration than nearby areas due to insufficient dry airflow replenishment. This solution accurately transforms this spatial vulnerability into a numerical high-risk index, enabling the management system to clearly identify "humidity dead zones" within the cabinet. This provides solid data support for guiding users to prioritize storing high-value or highly sensitive tools in low-risk areas. Furthermore, in scenarios with more complex cabinet structures, if the partition opening ratio is low, the drawer sealing is strong, or the airflow channel is significantly bent, although the system still uses "dehumidification distance" for uniform characterization, the comprehensive influence of these structural factors has been absorbed into the coefficient during the preset distance coefficient calibration stage. Therefore, it can adapt to different cabinet types without changing the existing calculation framework, which is convenient for engineering implementation and batch deployment.

[0045] In practical applications, the moisture index of a tool is determined based on the moisture risk index of multiple sliding time windows. This includes: obtaining the storage information of the tool in the humidity-controlled tool cabinet, which includes the actual storage partitions of the tool in multiple storage zones and the storage time period in each actual storage partition; for each actual storage partition, integrating the moisture risk index within the corresponding storage time period to obtain and using the integrated value as the sub-moisture index of the tool in the actual storage partition; using the sum of the multiple sub-moisture indices corresponding to the tool as the initial moisture index of the tool; and determining the moisture index of the tool based on the initial moisture index.

[0046] The "storage information" here includes not only which storage partition the tool was placed in, but also the entry time, exit time, and status changes such as whether it was borrowed, returned, or transferred across cabinets. Only by obtaining a complete timeline can the subsequent accumulation of moisture be authentic and traceable.

[0047] This specific implementation plan details a method for calculating cumulative moisture based on time series and spatial trajectory. The system first obtains complete storage information of the target tool over a past period using RFID readers, intelligent visual recognition, or an electronic ledger system. If RFID is used, a unique electronic tag can be attached to each tool, and read / write nodes can be set at each storage compartment or cabinet door. When a tool is placed in or removed, the system records the time the tool enters or leaves a specific storage compartment based on the tag identification result and the read / write node location. If intelligent visual recognition is used, a camera can be installed inside the cabinet to identify the tool by combining its shape and serial number, and an image analysis program can determine which storage compartment the tool is located in. To improve accuracy, visual calibration lines or positioning marks can be set at the boundaries of each compartment. If an electronic ledger is used, users can manually enter the tool's borrowing and returning time and storage compartment through a terminal. The system simultaneously records the user's account and timestamp for subsequent traceability. This information not only records every actual storage compartment the tool has ever stayed in, but also accurately records the start and end times of its stay in each compartment, i.e., the storage period.

[0048] Next, for each actual storage partition the tool traversed, the system extracts the moisture risk index sequence for that partition within the corresponding storage time period. Since the moisture risk index is discrete or continuous data that updates continuously with a sliding time window, the system uses integration (represented as summation over time steps for discrete data) to calculate the cumulative risk over that time period, defining this integral value as the tool's sub-moisture index for that actual storage partition. For ease of implementation, in discrete update scenarios, accumulation can be performed at fixed time steps. For example, the system records the partition moisture risk index at fixed intervals and accumulates the recorded values ​​covered during the tool's stay in chronological order. If the tool's entry or exit time falls between two recorded points, the risk contribution for that time period can be calculated based on the actual overlap duration. The sub-moisture index represents the local moisture load borne by the tool at a specific historical stage and spatial location. Subsequently, the system sums the multiple sub-moisture indices generated by the tool in all the actual storage partitions it traversed to obtain an initial moisture index spanning its entire storage history. Finally, based on this initial moisture index, the system further determines the tool's final moisture index. The advantage of this approach is that even if the tool is frequently moved, borrowed, returned, or briefly paused, the system can still retain a continuous and complete risk accumulation trajectory without causing a break in the assessment due to changes in the tool's location.

[0049] The working principle of this specific implementation scheme lies in mapping the physical movement trajectory of the tool onto a multi-dimensional spatiotemporal risk matrix. Through mathematical processing of "segmented integration" followed by "global summation," the system can seamlessly stitch together the fragmented environmental risks encountered by the tool at different times and in different spaces into a continuous and complete moisture accumulation. Combined with the application scenarios mentioned above, this trajectory-tracking-based integration mechanism completely breaks through the limitations of static assessment in traditional management systems that "only look at the present and not the past." Even if two tools are currently stored side by side in the same high-risk zone, the system can accurately distinguish which one was just placed there (with less moisture accumulation) and which one has been stored for a long time (with more moisture accumulation) by tracing their historical trajectories. This precise, individual-level dynamic accumulation assessment allows the moisture index to truly reflect the fatigue and deterioration degree of the tool's internal insulation materials or metal components, thus providing irreplaceable data for subsequent accurate scrapping and targeted drying treatment. Furthermore, if the impact of the external environment needs to be taken into account during the period when the tool is borrowed, the "outside cabinet status" recording channel can be extended in the system architecture. However, in the current solution, the accumulation of moisture inside the cabinet is explicitly excluded during the borrowing period, thus achieving clear boundaries and facilitating project implementation and division of responsibilities.

[0050] In a preferred embodiment of this application, determining the moisture index of a tool based on its initial moisture index includes: obtaining the volume of the tool and its surface complexity index; using the product of the volume and a preset volume coefficient as a volume adjustment coefficient; using the product of the surface complexity index and a preset complexity coefficient as a body shape adjustment coefficient; and determining the moisture index of the tool based on the initial moisture index and a target adjustment coefficient; the target adjustment coefficient being the average of the volume adjustment coefficient and the body shape adjustment coefficient.

[0051] Here, "volume" can refer to the overall external volume of the tool or its equivalent volume after standardization. The "surface complexity index" describes the complexity of the tool's surface, including its bumps, gaps, textures, hinge structures, and partially obscured areas. For tools with relatively smooth surfaces, such as insulating rods and insulating glove supports, a lower surface complexity index can be set. For tools with multiple joints and grooves, such as ratchet wrenches, wire cutters, and combination hinge tools, a higher surface complexity index can be set. By introducing these two attributes, the system can combine "how long it has been exposed and in what environment" with "how easily the tool absorbs moisture," resulting in a more realistic final judgment.

[0052] This specific implementation plan details how to personalize the initial moisture index using the physical properties of the tool. The system first retrieves the target tool's volume and surface complexity index from the tool's basic information database. Volume data can be obtained directly through 3D modeling or factory parameters; the surface complexity index is a dimensionless parameter used to quantify the tool's surface roughness, number of gaps, and surface unevenness. It can be obtained through 3D scanning analysis of the tool surface or pre-calibrated manually according to the tool type (e.g., a lower value for a smooth insulating rod, a higher value for a multi-joint ratchet wrench). If using 3D modeling, point cloud data of the tool surface can be collected using a structured light scanner, laser scanner, or industrial camera when the tool is received into the warehouse. The modeling software then generates a 3D model of the tool, and the volume is read from the model. If using factory parameters, the dimensions or product specifications provided by the manufacturer can be directly read, converted by the system, or manually entered to form the volume field. For the surface complexity index, a hierarchical calibration method can be used to establish a standard. For example, the system can pre-create a mapping table between tool type and surface complexity index, with the record format being "Tool Category - Structural Feature Description - Surface Complexity Index". For example: Insulated operating rod - smooth and continuous body, no hinges - low grade; Insulated wrench - with openings and sharp edges - medium grade; Insulated wire cutters - with hinges, anti-slip textures, and multiple gaps - high grade. This mapping table can be determined by management personnel during the trial operation phase by combining physical observation, 3D scanning results, and moisture absorption test results, and then entered into the background database as preset rules.

[0053] Next, the system multiplies the acquired volume by a preset volume coefficient to calculate the volume adjustment coefficient; simultaneously, it multiplies the surface complexity index by a preset complexity coefficient to calculate the morphology adjustment coefficient. The preset volume coefficient and preset complexity coefficient are system parameters used to unify dimensions and adjust the weights of various attributes. These two preset coefficients can also be obtained experimentally. Specifically, multiple typical tools can be selected and compared under the same humidity environment and exposure duration, recording changes in mass, insulation performance, or surface residual moisture before and after drying. Then, based on the relative influence of volume and surface structure on the actual degree of moisture absorption, a more suitable preset volume coefficient and preset complexity coefficient are determined. Subsequently, the system arithmetically averages the volume adjustment coefficient and the morphology adjustment coefficient to obtain a target adjustment coefficient that comprehensively reflects the tool's own hygroscopic susceptibility. Finally, the system applies this target adjustment coefficient to the initial moisture index to calculate the final moisture index after personalized correction. In this way, even if two tools are under the exact same environmental exposure trajectory, the system can still give different moisture assessment results based on the differences in the tool's own morphology.

[0054] The working principle of this specific implementation scheme lies in the deep coupling of the objective exposure amount (initial moisture index) at the environmental dimension with the microscopic hygroscopic characteristics (target adjustment coefficient) at the object dimension. The volume adjustment coefficient reflects the potential capacity of the tool's interior to hold moisture, while the shape adjustment coefficient reflects the physical ability of the tool's surface to capture and retain moisture. By averaging these two into the target adjustment coefficient, the system constructs a comprehensive evaluation index for the individual susceptibility of tools. Combined with the application scenarios mentioned above, this personalized correction mechanism greatly improves the scientificity and accuracy of moisture assessment. In substations and other locations with extremely high insulation performance requirements, once capillary condensation occurs in the gaps of complex tools, it is extremely difficult to dry quickly through conventional cabinet dehumidification airflow, often becoming a fatal hidden danger leading to insulation breakdown. This scheme can accurately identify such complex tools with high hygroscopic characteristics and assign them a higher moisture index under the same environmental exposure conditions. This allows the management system to implement differentiated early warning and maintenance strategies for tools with different shapes, effectively avoiding hidden safety loopholes caused by "one-size-fits-all" assessments, and further improving the closed loop of refined management throughout the entire tool lifecycle. Furthermore, the management system can also set tiered response strategies based on the final moisture index. For example, when the moisture index is in a low range, it can simply prompt continued observation; when the moisture index rises to a high range, it can prompt priority drying, priority retesting of insulation performance, or restriction of reuse. Because this tiered response is based on a dual correction of environmental exposure and individual tool characteristics, it better reflects the differences in actual safety risks.

[0055] In a preferred embodiment of this application, determining the moisture index of a tool based on an initial moisture index and a target adjustment coefficient includes: multiplying the initial moisture index and the target adjustment coefficient as an intermediate moisture index of the tool; obtaining a preset moisture sensitivity index of the tool; and multiplying the preset moisture sensitivity index and the intermediate moisture index as the moisture index of the tool.

[0056] This specific implementation plan details the calculation logic for the final moisture index, taking into account material characteristics. The system first multiplies the initial moisture index, representing the cumulative amount of environmental exposure, with a target adjustment coefficient, representing the susceptibility to physical morphology, to obtain the tool's intermediate moisture index. This intermediate moisture index already incorporates spatiotemporal environmental factors and the tool's shape. Subsequently, the system retrieves the tool's preset moisture sensitivity index from the tool's basic information database. The preset moisture sensitivity index is a dimensionless parameter characterizing the sensitivity of the tool's main material to moisture. In practice, this can be achieved by pre-establishing a mapping table of "material type - moisture sensitivity index." For example, for high-voltage insulating gloves, which are highly susceptible to insulation breakdown due to trace amounts of moisture, the preset moisture sensitivity index can be set to a higher value; while for ordinary metal wrenches with rust-proof surfaces, the preset moisture sensitivity index can be set to a lower value. This mapping table can be entered by material experts or according to relevant operation and maintenance specifications during system initialization. Finally, the system multiplies the preset moisture sensitivity index by the intermediate moisture index to obtain the final moisture index. Through this step, the system uses the inherent properties of the material as the final amplification or reduction factor, ensuring that the moisture index accurately reflects the risk of substantial deterioration of the tool. This multi-dimensional correction mechanism ensures that the assessment results neither deviate from objective environmental facts nor fail to fully respect the physical and material differences of individual tools, thus providing an extremely accurate data benchmark for subsequent differentiated management.

[0057] After accurately calculating the moisture index of all tools in the cabinet, the system needs to automatically generate an actionable management strategy based on this quantitative data to guide on-site maintenance personnel in performing targeted maintenance. To this end, this application further proposes a specific implementation method for determining the management strategy of the moisture-controlled tool cabinet based on the moisture index of all tools in the cabinet, including: determining whether the average moisture index of all tools in the moisture-controlled tool cabinet is greater than a first preset moisture index; when the average moisture index of all tools in the moisture-controlled tool cabinet is greater than the first preset moisture index, determining the management strategy of the moisture-controlled tool cabinet as notifying maintenance personnel to repair the moisture-controlled tool cabinet; when the average moisture index of all tools in the moisture-controlled tool cabinet is less than or equal to the first preset moisture index, determining whether there is a target tool among all tools in the moisture-controlled tool cabinet with a moisture index greater than the first preset moisture index; when there is no target tool among all tools in the moisture-controlled tool cabinet with a moisture index greater than the first preset moisture index, determining the management strategy of the moisture-controlled tool cabinet as notifying maintenance personnel to repair the moisture-controlled tool cabinet; when the average moisture index of all tools in the moisture-controlled tool cabinet is less than or equal to the first preset moisture index, determining whether there is a target tool among all tools in the moisture-controlled tool cabinet with a moisture index greater than the first preset moisture index. The management strategy is to continue using the tool. If, among all tools in the humidity control tool cabinet, there is a target tool with a moisture index greater than a first preset moisture index, the usage time of the target tool is obtained and determined to be greater than a preset time threshold. If the usage time of the target tool is greater than the preset time threshold, the management strategy for the humidity control tool cabinet is to notify maintenance personnel to dry the target tool in the cabinet. If the usage time of the target tool is less than or equal to the preset time threshold, the management strategy for the humidity control tool cabinet is to notify the user to store the target tool in a target storage area. The target storage area is the storage area among multiple storage areas whose dehumidification distance is less than the dehumidification distance of the storage area where the target tool is currently located. The dehumidification distance of the storage area is the distance between the center location information of the storage area and the location information of the dehumidification module.

[0058] This specific implementation plan constructs a multi-level decision tree logic from macro to micro and from equipment to individual components. First, the system calculates the arithmetic mean of the moisture index of all tools inside the cabinet and compares it with a first preset moisture index. The first preset moisture index is a threshold representing the overall environmental safety baseline of the tool cabinet, which can be obtained through statistical analysis of historical maintenance data. When the average value exceeds the first preset moisture index, it means that the vast majority of tools inside the cabinet are at high risk of moisture absorption, which usually indicates that the tool cabinet's dehumidification system has severely failed or that the cabinet's seal has suffered extensive damage. In this case, the system directly determines the management strategy to notify maintenance personnel to repair the humidity-controlled tool cabinet, thereby addressing the environmental degradation problem at its source.

[0059] When the average moisture index is less than or equal to the first preset moisture index, it indicates that the tool cabinet is generally operating normally. At this point, the system enters the individual inspection phase to determine if there are any individual target tools with a moisture index greater than the first preset moisture index. If all tools are within the limit, the system determines the current state is safe and the management strategy is to continue using them. If there are individual target tools exceeding the limit, the system further obtains the usage time of the target tool and compares it with a preset duration threshold. The preset duration threshold is used to distinguish whether the tool's moisture accumulation is due to natural accumulation from long-term service or abnormal moisture absorption due to improper short-term storage. When the usage time of a target tool exceeds the preset duration threshold, it indicates that the tool has undergone long-term environmental exposure inside the cabinet, and its interior may have absorbed a significant amount of moisture. Simple relocation adjustments cannot reverse its moisture state. Therefore, the system determines the management strategy to notify maintenance personnel to remove the target tool and send it to a professional drying room or use specialized equipment for deep drying.

[0060] Conversely, if the usage time of the target tool is less than or equal to the preset time threshold, it indicates that the tool was recently placed in the cabinet, and its high moisture index is highly likely because it was placed in a "damp dead zone" within the cabinet (i.e., a storage area with a long dehumidification distance and a high risk index). In this case, the system determines the management strategy to notify the user to move the target tool to the target storage area. The system will traverse other available storage areas within the cabinet, selecting locations whose dehumidification distance is less than that of the target tool's current location, and recommending these as target storage areas to the user. Through this dynamic allocation mechanism, the system can guide high-risk tools to a closer area with more abundant dry airflow, quickly reducing their moisture risk by utilizing the existing high-quality dehumidification resources within the cabinet.

[0061] The technical advantage of this implementation plan lies in its complete transformation of the traditional "one-size-fits-all" alarm mode in tool cabinet management. Through a progressive logical judgment, the system can accurately identify whether the root cause of the risk lies in the "cabinet equipment," "tool aging," or simply "improper storage location," and output drastically different handling suggestions accordingly. This not only avoids the waste of resources caused by blindly dispatching maintenance work orders, but also maximizes the differentiated advantages of the tool cabinet's internal microenvironment through intelligent location allocation, achieving refined and adaptive management of the entire tool lifecycle.

[0062] like Figure 3As shown, based on the data management method described in detail above, this application also provides a corresponding hardware system architecture to support the automated operation of the above-mentioned data acquisition, risk assessment, and strategy generation steps. This application also proposes an intelligent humidity-controlled tool cabinet data management system, including: an acquisition device and a processing device; the acquisition device is used to acquire tool cabinet information of the humidity-controlled tool cabinet within a sliding time window before the current moment; the tool cabinet information includes the number of times the cabinet door of the humidity-controlled tool cabinet is opened, the average ambient humidity of the workshop where the humidity-controlled tool cabinet is located, the average humidity inside the humidity-controlled tool cabinet, the operating information of the dehumidification module inside the humidity-controlled tool cabinet, the center location information of each storage partition in the multiple storage partitions of the humidity-controlled tool cabinet, and the location information of the dehumidification module; the humidity-controlled tool cabinet is used to store tools; the processing device is used to determine the moisture risk index of each storage partition in the humidity-controlled tool cabinet within the sliding time window based on the tool cabinet information; the processing device is also used to determine the moisture index of each tool stored in the humidity-controlled tool cabinet based on the moisture risk index of multiple sliding time windows; the processing device is also used to determine the management strategy of the humidity-controlled tool cabinet based on the moisture index of all tools in the humidity-controlled tool cabinet.

[0063] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A data management method for an intelligent humidity control tool cabinet, characterized in that, include: Retrieve tool cabinet information for the humidity-controlled tool cabinet within the sliding time window prior to the current moment; tool cabinet information includes the number of times the humidity-controlled tool cabinet door was opened, the average ambient humidity of the workshop where the humidity-controlled tool cabinet is located, the average humidity inside the humidity-controlled tool cabinet, the operating information of the dehumidification module inside the humidity-controlled tool cabinet, the center location information of each storage compartment in the multiple storage compartments of the humidity-controlled tool cabinet, and the location information of the dehumidification module; the humidity-controlled tool cabinet is used to store tools; Determine the moisture risk index of each storage area within the humidity-controlled tool cabinet within the sliding time window based on the tool cabinet information; For each tool stored in the humidity-controlled tool cabinet, the moisture index of the tool is determined based on the moisture risk index of multiple sliding time windows; The management strategy for the humidity control tool cabinet is determined based on the moisture index of all tools in the cabinet.

2. The intelligent humidity control tool cabinet data management method according to claim 1, characterized in that, Based on the tool cabinet information, determine the moisture risk index of each storage compartment within the humidity-controlled tool cabinet during the sliding time window, including: When the average ambient humidity is less than or equal to the average cabinet humidity, the external humidity risk index is set to 0; when the average ambient humidity is greater than the average cabinet humidity, the difference between the average ambient humidity and the average cabinet humidity is taken as the humidity deviation. The product of the first value and the second value is used as the external humidity risk index of the humidity control tool cabinet; the first value is the ratio of the number of times the cabinet door is opened to the preset maximum number of times the cabinet door is opened, and the second value is the product of the humidity deviation and the preset humidity deviation coefficient. Based on the external humidity risk index and tool cabinet information, determine the humidity risk index of each storage area in the humidity-controlled tool cabinet within the sliding time window.

3. The intelligent humidity control tool cabinet data management method according to claim 2, characterized in that, The dehumidification module's operating information includes the actual average dehumidification power. Based on the external humidity risk index and tool cabinet information, the humidity risk index of each storage area within the humidity-controlled tool cabinet is determined within the sliding time window, including: Obtain the first correspondence; the first correspondence includes a one-to-one correspondence between multiple average cabinet humidity ranges and multiple expected dehumidification powers; The expected dehumidification power corresponding to the average humidity range of the humidity control tool cabinet in the first correspondence is taken as the target expected dehumidification power. The internal humidity risk index of the humidity control tool cabinet is determined based on the target expected dehumidification power and the actual average dehumidification power. Based on the external humidity risk index, the internal humidity risk index, the center location information of each storage compartment in the multiple storage compartments of the humidity control tool cabinet, and the location information of the dehumidification module, the humidity risk index of each storage compartment in the humidity control tool cabinet is determined within the sliding time window.

4. The intelligent humidity control tool cabinet data management method according to claim 3, characterized in that, The dehumidification module's operating information includes operating time. Based on the target expected dehumidification power and the actual average dehumidification power, the internal humidity risk index of the humidity control tool cabinet is determined, including: Determine whether the actual average dehumidification power is greater than the target expected dehumidification power; When the actual average dehumidification power is less than or equal to the target expected dehumidification power, the internal humidity risk index is determined to be 0. When the actual average dehumidification power is greater than the target expected dehumidification power, the product of the third and fourth values ​​is used as the internal humidity risk index; the third value is the ratio of the working time to the duration of the sliding time window, the fourth value is the product of the fifth value and the preset power deviation coefficient, and the fifth value is the difference between the actual average dehumidification power and the target expected dehumidification power.

5. The intelligent humidity control tool cabinet data management method according to claim 3, characterized in that, Based on the external humidity risk index, internal humidity risk index, the center location information of each storage compartment in the humidity control tool cabinet, and the location information of the dehumidification module, the humidity risk index of each storage compartment in the humidity control tool cabinet within the sliding time window is determined, including: The average of the external humidity risk index and the internal humidity risk index is used as the overall humidity index; The distance between the center location information of each storage partition and the location information of the dehumidification module is used as the dehumidification distance of each storage partition; The product of the dehumidification distance of each storage zone and the preset distance coefficient is used as the distance dampness risk value of each storage zone. The average of the distance moisture risk value of each storage compartment and the overall moisture index is used as the moisture risk index of each storage compartment in the humidity control tool cabinet within the sliding time window.

6. The intelligent humidity control tool cabinet data management method according to claim 1, characterized in that, The moisture index of the tool is determined based on the moisture risk index across multiple sliding time windows, including: Obtain information on the storage of tools in the humidity-controlled tool cabinet. This information includes the actual storage areas of the tools in multiple storage zones and the storage time period in each actual storage zone. For each actual storage partition, the moisture risk index of the actual storage partition during the corresponding storage time period is integrated to obtain the integral value, which is then used as a tool for the sub-moisture index of the actual storage partition. The sum of the multiple sub-moisture indices corresponding to the tool is taken as the initial moisture index of the tool; The moisture index of the tool is determined based on its initial moisture index.

7. The intelligent humidity control tool cabinet data management method according to claim 6, characterized in that, The moisture index of the tool is determined based on its initial moisture index, including: Obtain the tool's volume and surface complexity index; The product of the volume and the preset volume coefficient is used as the volume adjustment coefficient. The product of the surface complexity index and the preset complexity coefficient is used as the topography adjustment coefficient. The moisture index of the tool is determined based on the initial moisture index and the target adjustment factor; the target adjustment factor is the average of the volume adjustment factor and the shape adjustment factor.

8. The intelligent humidity control tool cabinet data management method according to claim 7, characterized in that, The moisture index of the tool is determined based on the initial moisture index and the target adjustment factor, including: The product of the initial moisture index and the target adjustment factor is used as the intermediate moisture index of the tool. Obtain the tool's preset humidity sensitivity index; The product of the preset moisture sensitivity index and the intermediate moisture index is used as the moisture index of the tool.

9. The intelligent humidity control tool cabinet data management method according to claim 1, characterized in that, The management strategy for the moisture-controlled tool cabinet is determined based on the moisture index of all tools in the cabinet, including: Determine whether the average moisture index of all tools in the moisture-controlled tool cabinet is greater than the first preset moisture index; When the average moisture index of all tools in the moisture-controlled tool cabinet is greater than the first preset moisture index, the management strategy for the moisture-controlled tool cabinet is to notify maintenance personnel to repair the moisture-controlled tool cabinet. When the average moisture index of all tools in the moisture-controlled tool cabinet is less than or equal to the first preset moisture index, determine whether there is a target tool among all tools in the moisture-controlled tool cabinet with a moisture index greater than the first preset moisture index; If none of the tools in the humidity control tool cabinet have a moisture index greater than the first preset moisture index, the management strategy for the humidity control tool cabinet is to continue using it. When there is a target tool with a moisture index greater than the first preset moisture index among all the tools in the moisture control tool cabinet, obtain and determine whether the usage time of the target tool is greater than the preset time threshold. When the usage time of the target tool exceeds the preset time threshold, the management strategy for the humidity control tool cabinet is to notify maintenance personnel to dry the target tool in the humidity control tool cabinet. When the usage time of the target tool is less than or equal to the preset time threshold, the management strategy for the humidity control tool cabinet is to notify the user to store the target tool in the target storage partition; the target storage partition is the storage partition among multiple storage partitions whose dehumidification distance is less than the dehumidification distance of the storage partition where the target tool is currently located; the dehumidification distance of the storage partition is the distance between the center location information of the storage partition and the location information of the dehumidification module.

10. A data management system for an intelligent humidity control tool cabinet, characterized in that, include: Acquisition device and processing device; The acquisition device is used to acquire tool cabinet information of the humidity-controlled tool cabinet within a sliding time window prior to the current moment; the tool cabinet information includes the number of times the humidity-controlled tool cabinet door was opened, the average ambient humidity of the workshop where the humidity-controlled tool cabinet is located, the average humidity inside the humidity-controlled tool cabinet, the operating information of the dehumidification module inside the humidity-controlled tool cabinet, the center position information of each storage compartment in the multiple storage compartments of the humidity-controlled tool cabinet, and the position information of the dehumidification module; the humidity-controlled tool cabinet is used to store tools; The processing device is used to determine the humidity risk index of each storage area in the humidity-controlled tool cabinet within the sliding time window based on the tool cabinet information; The processing device is also used to determine the moisture index of each tool stored in the moisture-controlled tool cabinet based on the moisture risk index of multiple sliding time windows; The processing device is also used to determine the management strategy for the humidity control tool cabinet based on the moisture index of all tools in the cabinet.

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