IT equipment lease management system based on credit

Abstract

The invention relates to the technical field, in particular to a credit-based IT equipment lease management system, which comprises a credit state recognition module, an equipment matching and screening module, a lease term behavior monitoring module, an equipment state rechecking module and a lease permission backtracking module. According to the method, the dynamic credit label is formed by extracting the credit score change trend of the service party and combining the task emergency degree, intelligent judgment on resource priority configuration is achieved, and in the equipment screening link, a scheduling combination is accurately constructed through purpose consistency comparison and historical adaptation data screening, the matching efficiency is improved, and the matching efficiency is improved. In the operation process, multi-dimensional behavior data is collected and equipment self-inspection information is combined to realize joint identification of abnormal use, interface response analysis is used to guide servicing task assignment, and a closed-loop treatment system of pre-rental evaluation, in-rental monitoring and post-rental management and control is constructed by combining service party score fluctuation and equipment credit bearing limit.

Description

Technical Field

[0001] The present invention relates to the technical field, and in particular to a credit-based IT equipment lease management system. Background Art

[0002] The lease technology field includes various business processes and information systems based on resource allocation, asset management, and transfer of the right of use. The core content of this technical field is centered around how to achieve legal and compliant circulation of equipment or resources among different entities without transferring ownership. It mainly covers aspects such as the establishment of lease processes, credit assessment, asset scheduling, contract management, risk control, and traceability, involving the construction of an information management platform, dynamic control of the lease cycle, information identification and life cycle recording of leased materials, and integrated applications with third-party service systems such as finance and insurance. The development of this field is manifested as an evolution from manual offline processing to a data-driven automated management system, showing significant application value especially in the fields of IT equipment, vehicles, construction machinery, and other assets with high-frequency circulation.

[0003] Among them, a credit-based IT equipment lease management system refers to a technical solution that judges the qualifications of lease users through a credit assessment mechanism and implements digital management of IT equipment throughout the pre-lease, in-lease, and post-lease processes with an information system as the carrier. The patent theme mainly covers aspects such as user credit data collection, credit rating modeling and scoring, lease contract information registration, equipment unique identifier information registration, equipment transfer application and approval process setting, credit limit and deposit policy formulation, and tracking of abnormal usage records. Specifically, by constructing a credit scoring model, setting lease policy rules corresponding to different credit levels, and combining the identity code of IT equipment and lease behavior logs for risk marking and hierarchical management, a complete credit-related data structure and control strategy framework for lease behavior is formed.

[0004] In the prior art, the credit assessment in the leasing process relies on the setting of static information. Usually, the scoring result is established only when the user first participates in the lease, lacking the real-time capture and interpretation of new changes during the subsequent use process, and it is difficult to distinguish the dynamic changes of the credit level with the completion of tasks. In terms of the device matching strategy, although it covers the judgment of task requirements and device suitability, it mostly stays at general rules or manual screening, and cannot quickly generate a matching priority combination according to the recent behavior of the service provider and the task time requirements, resulting in slow response speed and limited accuracy. The risk identification mechanism during the device usage period still focuses on log records and simple alarm threshold settings, lacking the cross-comparison of behavior patterns, operation data, and self-check data, and unable to identify potential failure risks from multi-dimensional index linkages, forming a management blind spot. At the same time, the lease permission management usually relies on fixed-period backtracking and manual review, ignoring the identification of the significant short-term credit fluctuations of the service provider, resulting in the difficulty of timely blocking of risk behaviors. Taking the IT device lease as an example, if the performance of the service provider varies significantly between two tasks, the existing mechanism cannot identify and adjust the resource allocation strategy in a timely manner, which may cause high-risk service providers to still occupy high-value devices, increasing management costs and affecting the device recovery efficiency. Summary of the Invention

[0005] The object of the present invention is to solve the shortcomings existing in the prior art, and to propose a credit-based IT device lease management system.

[0006] To achieve the above object, the present invention adopts the following technical solutions: A credit-based IT device lease management system includes: The credit status recognition module collects the multiple lease scores of the service provider, calculates the difference between two scores, combines the content of the fluctuation trend, calls the task urgency level to compare with the trend for judgment, marks it as to be inspected when the direction is concentrated and fluctuating, and marks it as recommended when the score rises and is stable, and generates the lease credit label of the service provider; The device matching and screening module matches the urgency of the task based on the recommended level in the lease credit label of the service provider, screens the adaptation records with the service provider, identifies the task requirements and the device usage, and generates a rental comparison table of the devices to be scheduled; The lease term behavior monitoring module calls the device number records in the rental comparison table of the devices to be scheduled to record the start frequency, operation duration, and temperature change during the lease term, reads the self-check abnormal values, and compares the standard deviation to generate a list of abnormally operating devices; The device status review module extracts the list of abnormally operating devices, extracts the abnormal device interface failure records, counts the number of failures and the response types, and generates a detailed list of maintenance tasks assigned; The lease permission backtracking module calculates the number of score drops and the cumulative amplitude according to the combination of the service provider and the device in the detailed list of maintenance tasks assigned, and compares with the credit boundary of the device category for judgment, and generates a device lease management list.

[0007] As a further solution of the present invention, the service provider rental credit label includes a score change range, a change direction annotation, a fluctuation trend type, an emergency level correspondence, and a credit level identifier; the to-be-scheduled equipment rental comparison table includes a recommended level screening option, an urgency matching value, an available equipment number, a use consistency screening result, and a preferred recommendation combination number; the abnormal operation equipment list includes an equipment number record, a start frequency data, an operation duration statistic, a temperature change index, an abnormal behavior label, and an internal abnormal monitoring value; the maintenance task assignment details include a list of abnormal equipment items, the number of interface response failures, a response failure type, a frequency limit comparison result, and a maintenance task assignment number; the equipment rental management list includes service provider equipment association information, the number of score decreases, the cumulative score decrease, the equipment credit tolerance boundary, and a rental eligibility status.

[0008] As a further solution of the present invention, the credit status identification module includes: The score change calculation sub-module extracts the score value of each rental task based on the credit score record formed by the service provider in the equipment rental task, calculates the numerical difference between adjacent scores after arranging them in chronological order, determines the positive and negative of the difference and annotates it as rising, falling, and flat, and generates a score difference direction sequence. The fluctuation trend judgment sub-module calls the score difference direction sequence, calculates the direction concentration by counting the proportion of consecutive same directions, extracts the absolute value interval of the score difference to judge the fluctuation range, and compares it with the emergency level of the corresponding task to obtain the direction fluctuation trend level. The credit label generation sub-module identifies the service provider tasks with continuously rising scores and stable fluctuations according to the direction fluctuation trend level and marks them as the recommended level. If the score changes and fluctuates, it is marked as the to-be-inspected level, and a service provider rental credit label is generated.

[0009] As a further solution of the present invention, the equipment matching and screening module includes: The level matching and screening sub-module extracts the service provider numbers corresponding to the recommended level based on the recommended level in the service provider rental credit label, matches the urgency field registered in the rental task, and screens the task entries that meet the requirements to obtain a list of available equipment numbers. The use consistency identification sub-module calls the list of available equipment numbers, extracts the service provider adaptation records associated with each equipment number, compares the consistency between the equipment use field and the rental task requirement field, and eliminates the inconsistent entries to obtain a list of equipment numbers with consistent use. The preferred combination generation sub-module counts the number of times each equipment is adapted to the service provider task according to the list of equipment numbers with consistent use, calculates the priority order of each combination, arranges the number combinations in order according to the priority order, and screens out the items with insufficient priority order to generate a to-be-scheduled equipment rental comparison table.

[0010] As a further solution of the present invention, the lease behavior monitoring module includes: The equipment scheduling comparison submodule calls the equipment number in the rental comparison table of the equipment to be scheduled, screens the set of equipment numbers in the scheduling state within the current rental period, matches the equipment number with the corresponding rental period for associated storage, and obtains the equipment rental period matching table; The startup behavior analysis submodule collects the startup frequency of the device during the lease period, the continuous running time of each startup, and the temperature change range during the operation period based on the equipment rental period pairing table, performs segmented statistical calculations on the startup frequency and continuous running time, and compares the startup behavior standard threshold to determine whether the startup frequency and continuous running time are within the specified range, screens devices with abnormal startup behavior, and obtains the abnormal startup behavior mark value; The operation status comparison submodule calls the device number in the startup behavior abnormality mark value, reads the device self-test record, extracts the operation abnormality value data within the corresponding time period, and compares it item by item with the parameter allowable deviation range set in the equipment use standard, using the formula: ; Calculate and obtain the abnormality comparison value, list the equipment that exceeds the allowable threshold of operation behavior as the key record object, and obtain the list of abnormal operation equipment; in, Represents the abnormality comparison value, Represents the standard value of the device’s set temperature. Represents the average temperature measured by the device. Represents the actual frequency of device operation. Represents the device's specified startup frequency threshold. Represents the fluctuation value of the duration of a single operation of the device. Represents the i-th abnormal value in the device self-test record, It represents the allowable value of the ith item in the equipment use standard, and n is the total number of parameters in the equipment use standard.

[0011] As a further solution of the present invention, the device status review module includes: The abnormal extraction submodule extracts the interface response failure records generated by the device during the lease period based on the abnormal operation device list, detects the interface identifier, response status code and request time, filters the record items of the response status code failure code, and determines whether it is within the lease period in combination with the request time, and obtains the number of interface failure records; The response recognition sub-module calls the number value of the interface failure records, identifies the response status code type of the class, counts the number of response failures of each device within a fixed time period, compares it according to the response frequency limit standard in the device classification description, and determines whether the device has a situation where the response error distribution is dense and the frequency exceeds the limit value, so as to obtain the interface failure density value; The task generation sub-module screens the devices that exceed the response frequency limit according to the interface failure density value, extracts the lease number, failure time period and device model, and uses the formula: ; Performs operations to obtain the maintenance task intensity value, compares it with the task assignment rule, and generates the maintenance task assignment details; Among them, Q represents the maintenance task intensity value, A represents the failure density value of the device, X is the start time of the response failure, Y is the end time of the response failure, B is the number of device classifications, C is the response frequency limit value set for each type of device, and Z is the actually monitored response failure frequency.

[0012] As a further solution of the present invention, the lease permission backtracking module includes: The device service identification sub-module extracts the service provider identification and device coding information marked in each task according to the maintenance task assignment details, establishes the combination relationship between the service provider and the device, and generates the service device combination list; The scoring change calculation sub-module based on the service provider identification contained in the service device combination list, retrieves the scoring sequence, sequentially obtains the adjacent scoring values before and after and calculates the difference, screens the data points with a decreasing score, accumulates the score decrease amplitude and the number of decreases respectively, and performs the scoring change aggregation operation, using the formula: ; Performs operations and obtains the service provider score decrease degree value; Among them, represents the scoring value of the th item in the scoring sequence, is the scoring value of the next item, is the scoring decrease discriminant item, is the number of scoring times that can form a comparison of the previous and subsequent items in the scoring sequence, is the service provider score decrease degree value; The lease permission suspension judgment sub-module calls the service provider score decrease degree value, compares it with the credit tolerance boundary value set for the device category. If the score decrease degree value is greater than the boundary value, the corresponding device is marked as the suspended lease status, and the device lease management list is generated.

[0013] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by extracting the changing trend of the service provider's credit score and combining it with the urgency of the task, a dynamic credit label is formed to achieve intelligent judgment of the priority allocation of resources. In the device screening process, through the comparison of usage consistency and the screening of historical adaptation data, a scheduling combination is accurately constructed to improve the matching efficiency. During the operation process, multi-dimensional behavior data is collected and combined with the device self-check information to achieve the joint recognition of abnormal usage. The interface response analysis guides the allocation of maintenance tasks, and the rental permission is dynamically adjusted by combining the score fluctuation of the service provider and the credit bearing limit of the device, thereby constructing a closed-loop governance system for pre-rental evaluation, in-rental monitoring, and post-rental control. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 is the system flow chart of the present invention; Figure 2 is the flow chart of the credit status recognition module of the present invention; Figure 3 is the flow chart of the device matching and screening module of the present invention; Figure 4 is the flow chart of the lease-term behavior monitoring module of the present invention; Figure 5 is the flow chart of the device status review module of the present invention; Figure 6 is the flow chart of the rental permission backtracking module of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0015] In order to make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0016] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by terms such as length, width, up, down, front, back, left, right, vertical, horizontal, top, bottom, inner, and outer is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as limiting the present invention. In addition, in the description of the present invention, the meaning of "plurality" is two or more unless otherwise specifically defined.

[0017] Please refer to Figure 1 , a credit-based IT device lease management system includes: The credit status identification module collects the credit score records of the service provider in multiple equipment leasing tasks, calculates the change difference between any two scores and marks the direction, combines them to form the fluctuation trend content, calls the emergency level in the leasing task, and compares and judges it with the score fluctuation trend. When the direction changes are concentrated and the fluctuation is drastic, it is marked as the level to be checked. When the score continues to rise and the fluctuation is stable, it is marked as the recommended level, and the service provider's leasing credit label is generated; The equipment matching and screening module matches the recommendation level in the service provider's rental credit tag with the urgency registered in the rental task, extracts the available equipment number, and screens the adaptation records with the service provider to identify the consistency between the task requirements and the use of the equipment. After eliminating the mismatched items, a priority recommendation combination is established to generate a comparison table for the rental of equipment to be scheduled; The lease behavior monitoring module calls the equipment number in the lease comparison table of the equipment to be scheduled, records the startup frequency, continuous operation time and temperature change data during the lease period, reads the abnormal operation value in the equipment self-test record, and compares the deviation range between the use behavior and the equipment use standard. If there is an abnormal operation behavior and an abnormal internal monitoring value, the equipment will be listed as a key record object and a list of abnormal operation equipment will be generated; The equipment status review module extracts the interface response failure records generated by the equipment during the lease period based on the abnormal operation equipment list, counts the number of failures and identifies the response type, and calls the response frequency limit standard in the equipment classification description for comparison. If the interface error records are densely distributed and exceed the frequency limit, the maintenance task dispatch details are generated; The rental rights backtracking module calls the service provider's credit score changes based on the service provider and equipment combination in the maintenance task assignment details, calculates the number of score declines and the cumulative magnitude, and then makes a judgment against the credit tolerance boundary of the category to which the equipment belongs. When the result formed by the change magnitude and frequency exceeds the limit, the current equipment's rental qualification is suspended and an equipment rental management list is generated.

[0018] The service provider's rental credit label includes the score change range, change direction mark, fluctuation trend type, emergency level correspondence, and credit level identification. The comparison table for rental of equipment to be scheduled includes recommended level screening items, urgency matching values, available equipment numbers, usage consistency screening results, and priority recommended combination numbers. The list of abnormal operating equipment includes equipment number records, startup frequency data, operating time statistics, temperature change indicators, abnormal behavior labels, and internal abnormal monitoring values. The maintenance task assignment details include abnormal equipment list items, number of interface response failures, response failure type, frequency limit comparison results, and maintenance task assignment numbers. The equipment rental management list includes the service provider's equipment association information, number of score declines, cumulative score declines, equipment credit tolerance boundaries, and rental qualification status.

[0019] See also Figure 2 , the credit status identification module includes: The scoring change calculation sub-module extracts the scoring values of each rental task based on the credit scoring records formed by the service provider in the equipment rental task. After arranging them in chronological order, it calculates the numerical difference between two adjacent scores, determines the positive or negative of the difference, labels it as rising, falling, or flat, and generates a scoring difference direction sequence. First, extract the historical rental task data of the service provider from the platform database, including the task number, completion time, and corresponding scoring values. Assume that a certain service provider has participated in 5 equipment rental tasks, and its scoring data is 85 points in January, 88 points in February, 83 points in March, 86 points in April, and 86 points in May in chronological order. Initially, obtain the scoring sequence [85, 88, 83, 86, 86]. Calculate the difference between two adjacent scores in this scoring sequence, that is, 88 - 85 = 3, 83 - 88 = -5, 86 - 83 = 3, 86 - 86 = 0, and generate a scoring difference sequence of [3, -5, 3, 0]. Subsequently, judge the positive or negative nature of each difference and make annotations. If the difference is greater than 0, it is marked as rising; if it is less than 0, it is falling; if it is equal to 0, it is flat. Thus, obtain a direction sequence of [rising, falling, rising, flat]. This process is completed by traversing the differences between two adjacent scores in the scoring sequence and combining symbol classification. In actual execution, a loop operation is used to read the scoring values pair by pair, subtract the previous value from the latter value, record the result and direction, append the direction label to the sequence array after each operation to form a direction annotation result, and eliminate scoring outliers or missing items during the processing to ensure the accuracy of data processing. For example, if a certain score is missing, skip that task or linearly estimate and fill in the data based on the scores of adjacent tasks. Finally, obtain a scoring difference direction sequence for subsequent analysis of the fluctuation trend.

[0020] The fluctuation trend judgment sub-module calls the scoring difference direction sequence, calculates the direction concentration by statistically analyzing the proportion of consecutive same directions, extracts the absolute value interval of the scoring difference to judge the fluctuation amplitude, and compares it with the emergency level of the corresponding task to obtain the direction fluctuation trend level. First, perform continuous direction statistics on the sequence. By defining a continuity identification variable and an accumulated count variable, traverse the direction sequence. For example, if the first item is an increase, which is inconsistent with the decrease of the second item, reset the accumulated count. When the third item is an increase again, start accumulating anew. The direction concentration is defined as the ratio of the maximum number of consecutive same-direction occurrences to the total number of directions. In the above example, the maximum consecutive increase count is 1, the decrease is inconsistent with the previous and subsequent directions and is only a single occurrence, and the flat state is also a single occurrence. Therefore, the maximum consecutive count is 1, the total number of directions is 4, and the direction concentration = 1 / 4 = 0.25. Next, perform amplitude analysis on the absolute value sequence of score differences [3, 5, 3, 0], distinguish the fluctuation levels, and based on the fluctuation amplitude determination threshold set by the system: 0 ≤ amplitude < 2 is slight, 2 ≤ amplitude < 5 is medium, and amplitude ≥ 5 is severe. Statistically calculate the proportion of each level. In the current sequence, the amplitudes are medium, severe, medium, and slight respectively, and the corresponding proportions are 0.5, 0.25, and 0.25. Combining with the task urgency level, which is set at the time of task creation and is divided into three levels: high, medium, and low. For example, if all the task urgency levels of this service provider are high, then extract the matching items from the preset judgment rules. The rules are set as follows: If the direction concentration < 0.4 and the proportion of severe fluctuations ≥ 25%, and the urgency level is high, the trend level is D. This threshold is set based on the platform's statistics of historical task data and is set in combination with the relationship between the rental success rate and score changes. Specifically, through the statistics of nearly a thousand rental data, it is found that when the direction concentration is less than 0.4, the score stability is weak, and when the severe fluctuation is higher than 25%, the system stability is low. Therefore, this item is set as an important threshold basis for judging the D-level trend.

[0021] The credit label generation sub-module identifies the service provider tasks with continuously increasing scores and stable fluctuations according to the direction fluctuation trend level and marks them as the recommended level. If the score changes and fluctuates, it is marked as the to-be-inspected level, and the service provider rental credit label is generated; Tag the service provider according to the fluctuation trend level determined above. If the direction concentration is higher than 0.7, the overall performance of the score difference sequence shows continuous positive values, that is, the score steadily rises, and the proportion of items with a fluctuation range less than 2 exceeds 75%, the system will rate this service provider as the recommended level. For example, for the score sequence [82, 84, 86, 88, 90], its difference is [2, 2, 2, 2], the direction is [rising, rising, rising, rising], the concentration is 1, and the fluctuation range is all 2, corresponding to the level of slight. Combined with the task urgency level being medium, it is matched as the A-level trend according to the threshold table and marked as recommended. On the contrary, if the score sequence is [90, 82, 86, 80, 85], the difference is [-8, 4, -6, 5], and the direction is [falling, rising, falling, rising], the direction changes frequently, the concentration = 1 / 4 = 0.25, the fluctuation range is higher than 5 in many places, and the proportion of severe is 50%. The task urgency level is high, and the direction and score change lack stability. The system matches the trend level as D according to the threshold setting, so it is marked as the to-be-inspected level. This module finally outputs the corresponding results of the service provider ID and the credit label in the JSON structure, and at the same time marks the timestamp and the analysis batch number for archival query to form a complete label record.

[0022] Please refer to Figure 3 , the device matching and screening module includes: The level matching and screening sub-module extracts the service provider numbers corresponding to the recommended level based on the recommended level in the service provider's lease credit label, matches the urgency level field registered in the lease task, screens the task entries that meet the requirements, and obtains the list of available device numbers; Based on the recommended level in the service provider's lease credit label, first screen all service provider records in the credit label library, extract the records with the label field value of "recommended", and obtain the corresponding service provider numbers. For example, if service providers A101, A103, and A108 are marked as the recommended level in the current label records of the platform, then construct a recommended number list as [A101, A103, A108]. Subsequently, read the task information currently in the waiting-to-be-matched state from the lease task pool, and extract the urgency field of each task. The urgency field is an enumerated numerical value, generally divided into high (urgency = 3), medium (urgency = 2), and low (urgency = 1). For example, the urgency of task T201 is 3, T202 is 2, and T203 is 1. Then, compare according to the service provider response ability set by the platform, and count the time from when the service provider received an order to the start of the operation (response time). Assume that the maximum response time threshold allowed for urgent tasks set by the platform is 6 hours. This threshold is derived from the analysis of 1000 high-urgency task data in the past three months on the platform, and it is found that the failure rate of high-urgency tasks with a response time exceeding 6 hours is significantly increased (higher than 22%). Therefore, set 6 hours as the upper limit. When the average response time of service provider A101 is 4 hours, A103 is 7 hours, and A108 is 5.5 hours, then only A101 and A108 can match task T201. An association relationship is established between the successfully matched service provider and the task. Extract the equipment number fields in tasks T201 and T203, assume they are E320 and E325 respectively, then the final generated list of available equipment numbers is [E320, E325]. This process comprehensively uses the recommended level and the urgent matching threshold to screen the equipment numbers to ensure that the obtained equipment numbers come from service providers that meet the recommended level and have response capabilities.

[0023] The usage consistency recognition sub-module calls the list of available equipment numbers, extracts the service provider adaptation records associated with each equipment number, compares the consistency between the equipment usage field and the lease task requirement field, and eliminates inconsistent entries to obtain a list of equipment numbers with consistent usage; The Usage Consistency Recognition Sub-module processes the device number list obtained in the previous stage, sequentially reads the service provider adaptation records corresponding to the device numbers, extracts the content of the device usage field in the records, and at the same time reads the task requirement usage field in the rental task. It compares the text content of the two fields. For example, the usage field of device E320 is "aerial work platform", and the usage field of task T201 is "aerial facility maintenance". The text matching extracts the core words "aerial work maintenance" through the set keyword extraction logic. The matching keyword coincidence rate reaches 2 / 3, that is, 66.7%. However, the platform sets the consistency threshold to 80%, so it is determined to be inconsistent. This set threshold comes from the analysis results of the platform's past device and task matching data. Statistical analysis shows that when the keyword coincidence rate is not less than 80%, the task success rate is more than 95%. When it is lower than this ratio, the task completion rate drops significantly. Therefore, 80% is set as the minimum matching ratio for the consistency of the usage field. The usage field of device E325 is "concrete pumping", and the task usage is "concrete pouring". The keyword consistency is that "concrete" is the only matching word, accounting for only 33.3% of the total number of keywords, which does not meet the threshold requirements. If the recorded usage of device E321 is "aerial repair", which shares the keyword "aerial maintenance" with the task field "aerial facility maintenance", and the matching rate is 2 / 2 = 100%, which meets the requirements, then E321 is retained. Finally, by eliminating inconsistent entries, the remaining device number list with consistent usage after screening is [E321].

[0024] The Priority Combination Generation Sub-module counts the number of times each device is adapted to the service provider's tasks according to the device number list with consistent usage, calculates the priority order of each combination, arranges the number combinations according to the priority order, and screens out the items with insufficient priority order to generate a comparison table of leased devices to be scheduled; The priority combination generation sub-module generates task combination priorities based on the device number list with consistent purposes. First, it extracts the number of matches of the device number E321 in the historical rental records in the list, and counts the task adaptation frequency of this device with different service providers in the recent 30 days. Suppose the device E321 has been matched with service provider A101 4 times, A104 3 times, and A105 1 time within 30 days, then the adaptation frequency table is constructed as E321: [A101: 4, A104: 3, A105: 1]. Sort the above combinations according to the priority, and the sorting standard is in descending order of the number of adaptations, that is, [A101, A104, A105]. At the same time, set the priority combination screening threshold to 2 times. The source of this threshold is the analysis of the platform rental completion rate, which shows that the task completion rate of service providers with a combination adaptation frequency of more than 2 times is generally higher than 90%, and the completion rate fluctuates greatly when it is less than 2 times. Therefore, 2 is set as the screening limit. For A105 in the above combination, there is only 1 adaptation record, which does not meet the screening conditions and is excluded. The remaining combinations are the device E321 and service providers A101 and A104, and the corresponding task numbers are T201 and T205. Then the finally generated scheduling device rental comparison table contains the combination records of the device E321 and service providers A101 and A104.

[0025] Please refer to Figure 4 , and the lease term behavior monitoring module includes: The device scheduling comparison sub-module calls the device numbers in the device rental comparison table to be scheduled, screens the set of device numbers in the scheduling state during the current lease term, matches the device numbers with the corresponding lease periods for associated storage, and obtains the device lease period pairing table; First, retrieve the rental information records of all devices in the database, obtain the fields device number, rental start point, rental end point, and device status, screen out the devices with the status of being scheduled, and judge whether their rental start and end points contain the preset scheduling reference interval in the current query logic. If the device number is D001 and the rental period is from the 1st period to the 4th period, and the current scheduling logic specifies to match the 2nd period to the 3rd period, then this device meets the scheduling period matching condition and is included in the rental period pairing process. Bind the device number D001 to its rental period (the 1st period, the 4th period) through setting key-value pairs and store it to form a pairing entry. Another device number D002 has a status field of being under repair, and it is excluded even if the rental period matches. All eligible devices are screened and mapped to uniformly generate a pairing table of device numbers and corresponding rental periods. This table is used as the basic input data source for starting behavior analysis to form the final device rental period pairing table.

[0026] The startup behavior analysis sub-module collects the startup frequency, the duration of each startup operation, and the temperature change range during operation of the device within the lease period based on the device lease cycle pairing table, performs segmented statistical calculations on the startup frequency and the duration of operation, compares them with the startup behavior specification thresholds, determines whether the startup frequency and the duration of operation are within the specified ranges, filters out devices with abnormal startup behaviors, and obtains the abnormal startup behavior flag values; For each device number, its startup behavior records are called, and the startup times and the duration of each startup operation are extracted. For device number D001, the startup times are recorded as [2, 4, 6, 3, 5], and the duration of each single startup is recorded as [28, 45, 52, 33, 48] minutes. The frequency values are divided into three intervals: not exceeding 3 times, between 4 and 5 times, and greater than 5 times. The corresponding frequency distribution is that two times fall within the interval of not exceeding 3 times, two times fall within the interval of 4 to 5 times, and one time falls within the interval of greater than 5 times. At the same time, the operation duration values are divided into not exceeding 40 minutes, 41 to 50 minutes, and greater than 50 minutes. The corresponding distribution is that two times fall within the first segment, two times are in the second segment, and one time is in the third segment. The frequency threshold set for the device startup behavior specification is 5 times, and the upper limit of the operation duration is 50 minutes. By judging whether the startup times are greater than 5 and whether the operation duration is greater than 50, each record is compared one by one. Among the frequency values, 6 times exceed the threshold, and among the operation duration values, 52 minutes also exceed the threshold. A total of two behaviors deviate from the standard, and it is determined that this device has abnormal startup behavior, and its abnormal startup behavior flag value is assigned as 1. If for another device number D002, the startup frequency in all startup behavior records does not exceed 5 times and the operation duration is between 40 and 50 minutes, then it is not marked as abnormal and continues to be used for the analysis of the next module.

[0027] The operation status comparison sub-module calls the device numbers in the abnormal startup behavior flag values, reads the device self-check records, extracts the abnormal value data within the corresponding time period, and compares them item by item with the parameter tolerance interval set in the device usage standard. The formula is used: ; Calculate to obtain the abnormal degree comparison value, list the devices that exceed the operation behavior tolerance threshold as the key record objects, and obtain the list of devices with abnormal operation; Among them, represents the abnormal degree comparison value, represents the device-set temperature standard value, represents the average measured temperature of the device, represents the actual operation frequency value of the device, represents the device-specified startup frequency threshold, represents the fluctuation value of the duration of a single device operation, represents the i-th abnormal value in the device self-check record, Represents the i-th allowable value of the equipment usage standard, and n is the total number of parameters in the equipment usage standard; The anomaly degree comparison value is a numerical index used to quantify the deviation of a system or equipment from its normal state, and is calculated by comparing with a historical baseline or a standard threshold; The operation status comparison sub-module calls the device number D001 marked with abnormal startup behavior, reads its self-check record, extracts the average temperature, actual operation frequency, and operation duration fluctuation value from it, and compares each item with the device standard value. The device standard setting values are a temperature of 85°C, a frequency of 5 times, and the operation duration fluctuation is allowed within a range of ±20%. The self-check data shows that the average temperature is 90°C, the average frequency is 6 times, the minimum startup duration is 30 minutes, the maximum is 60 minutes, and the average is 45 minutes. The temperature deviation term is calculated as: ; Frequency deviation term: ; The operation duration fluctuation value is: ; Four parameter items are extracted from the self-check anomaly record. The device standard values are [10, 5, 8, 7], and the actual detected values are [13, 6, 10, 6]. Then the difference of each item is calculated as follows: ; The sum is: ; Substitute into the formula: ; The device operation status anomaly threshold is 1.0, and the current result is 0.8333, which is lower than the threshold, indicating that although the device has abnormal startup behavior, the overall operation status has not reached a serious abnormal level and is not included in the list of abnormally operating devices; The benefit of this formula is that by constructing a comprehensive offset value with three actual deviation parameters as the numerator, and at the same time introducing multiple anomaly terms as the denominator for adjustment, the anomaly degree value has a discrimination boundary in the comparison, ensuring that the judgment result has reasonable boundary and numerical stability.

[0028] Please refer to Figure 5 , the device status review module includes: The anomaly extraction sub-module, based on the list of abnormally operating devices, extracts the interface response failure records generated by the device during the lease period, detects the interface identifier, response status code, and request time, filters the record items with unsuccessful response status codes, and determines whether it is within the leased period in combination with the request time to obtain the interface failure record quantity value; First, retrieve all device information currently in the leased state from the device management system, and obtain the device identification list as {A001, A002, A003}. The system retrieves all interface request records generated by these three devices during the lease period from the interface request record library. The record fields include device number, interface identification, response status code, and request sequence number. Subsequently, a judgment operation is performed on the response status code field in each record. If the response status code is not equal to 200, it is marked as a failed record item. For example, device A001 has a total of 1200 recorded request items, and 84 of them have a status code other than 200; device A002 has a total of 900 recorded items, and 67 of them are failed records; device A003 has 1000 recorded items, and 51 of them are failed records. The judgment process is executed through conditional screening, that is, the response status code field is read. If its value is not 200, it is set to the failed state. Then, the request sequence number is called and matched with the device operation status record. After confirming that the failed record occurs within the valid interval of the leased state, it is retained to form the final set of valid failed records. The results are that the number of valid failed records for A001 is 84, for A002 is 67, and for A003 is 51. This result is used as one of the input items of the response recognition sub-module.

[0029] The response recognition sub-module calls the number value of the interface failure records, identifies the type of response status code of the recognition class, counts the number of response failures of each device within a fixed time period, and compares it according to the response frequency limit standard in the device classification description to determine whether the device has a situation where the response error distribution is dense and the frequency exceeds the limit, and obtains the interface failure density value; First, statistical operations are performed on the failure record set of each device. The analysis unit is set to the request number block. Each block contains 50 consecutive records. The number of failed responses in each block is calculated. The A001 device has a total of 84 failure records, which are distributed in 1200 requests, corresponding to 24 blocks. The number of failures in each block is counted in turn, and the failure distribution array is {2, 1, 5, 0, 0, 3, 4, 6, 7, 3, 1, 2, 3, 2, 5, 4, 6, 7, 8, 3, 1, 0, 1, 0}. The criterion for judging dense distribution is: if there are ≥5 failure records in 3 consecutive blocks, the segment is judged to be a failure response dense segment. It is counted that there are two segments in the array that meet the above criteria. , which are the 8th to 10th blocks (6, 7, 3) and the 16th to 18th blocks (6, 7, 8), respectively. It is confirmed that there are two dense sections. The density value is further calculated as the product of the number of sections and the length of the section, that is, 2×3=6. This value is set as A, which is used for the downstream task generation submodule to perform task intensity evaluation. In the statistical process, the response frequency limit standard is set as the maximum allowable failure record per block is 4. If the number of failures in a single block exceeds 4, it is a high-frequency response error, thereby forming a dense distribution basis. The judgment process is based on the comparison operation of the array elements and the constant 4, and a dense mark is formed through a logical judgement. Then, the continuous high-value segments are detected by a sliding window method, and the result is determined to be a failure response density value of 6.

[0030] The task generation submodule screens the devices that exceed the response frequency limit according to the interface failure density value, extracts the lease number, failure time period and device model, and uses the formula: ; Obtain the maintenance task intensity value through calculation, and compare it with the task assignment rules to generate the maintenance task assignment details; Among them, Q represents the maintenance task intensity value, A represents the failure density value of the equipment, X is the start time of the response failure, Y is the end time of the response failure, B is the number of equipment categories, C is the response frequency limit value set for each type of equipment, and Z is the actual monitored response failure frequency; An indicator reflecting the comprehensive difficulty of equipment maintenance and combat readiness preparation work, which may include: man-hour requirements (such as standard man-hours), spare parts scarcity coefficient, and special tool requirement level; After receiving the response failure density value, a screening operation is performed to determine whether the equipment exceeds the response frequency limit. The A001 equipment density value A is 6, and the equipment classification quantity B is set to 5 categories, corresponding to light equipment, medium equipment, large equipment, heavy-duty equipment, and ultra-high frequency equipment. Among them, A001 belongs to the medium-sized equipment category, numbered MT-02. The system sets the classification response frequency limit standard C to 4. The actual failure frequency Z of the A001 equipment in the detection is 84, which meets the screening conditions. The extracted parameters are used to calculate the maintenance task intensity value, and the formula is called: ; Among them, , , , represent the start number and end number of the failure segment. , , , , substitute them into the calculation in turn: The numerator part: ; The denominator part: ; ; The total value of the denominator is: ; The strength value is: ; Thus, the task strength value is obtained as 4.38. If the task rule is defined as: : Light task; : Medium task; : Heavy task; Then the task strength level of the current device is a heavy task.

[0031] In the formula, the parameter represents the degree of response anomaly concentration, is the anomaly interval span, reflecting the severity of the anomaly, reflects the complexity of device classification, measures the deviation degree of the current anomaly frequency of the device compared with the standard. The innovation of the formula lies in integrating the response concentration and anomaly deviation degree in a linear and non-linear way to form a comprehensive evaluation of the task strength. The result shows that the A001 device needs to be assigned to a high-priority maintenance task plan due to its high degree of response anomaly concentration and the anomaly frequency far exceeding the classification limit.

[0032] Please refer to Figure 6 , the lease permission backtracking module includes: The device service identification sub-module extracts the service provider identifier and device coding information marked in each task according to the maintenance task assignment details, establishes the combination relationship between the service provider and the device, and generates a service device combination list; Extract the service provider identifier and equipment code information from each task according to the maintenance task assignment details. Suppose the maintenance task details contain the following data: Task A is assigned to service provider number SF001, which is responsible for equipment EQ101 and EQ102; Task B is assigned to service provider SF002, which is responsible for equipment EQ102 and EQ103. First, read the service provider number and equipment number fields in the task details one by one, combine the field values to construct service-equipment pairs, and generate the initial mapping pairs as: SF001-EQ101, SF001-EQ102, SF002-EQ102, SF002-EQ103. Then, determine whether the equipment is repeatedly mapped to multiple service providers. EQ102 appears in two service providers, so keep the two mapping pairs. Subsequently, perform deduplication and validity verification operations on all combination relationships, filter out combination relationships with incorrect formats or missing field items. Finally, generate the service-equipment combination list as: SF001-EQ101, SF001-EQ102, SF002-EQ102, SF002-EQ103. This list is encapsulated as a structured array for input in the subsequent scoring analysis stage.

[0033] Based on the service provider identifiers contained in the service-equipment combination list, the scoring change calculation sub-module retrieves the scoring sequence, sequentially obtains the adjacent scoring values before and after and calculates the difference, filters out the data points with decreasing scores, accumulates the decreasing amplitude and the number of decreases of the scores respectively, and performs the aggregation operation of scoring changes. The formula is used: ; Calculate and obtain the service provider scoring decrease degree value; Among them, represents the scoring value of the th item in the scoring sequence, is the scoring value of the next item, is the scoring decrease discrimination item, is the number of scoring times that can form a comparison of the front and back items in the scoring sequence, is the scoring decrease degree value; Quantitative parameters for dynamically evaluating the degradation of system performance. Common calculation dimensions: decrease rate (such as decreasing 0.5 points per day), absolute decrease (decreasing 15% compared to the reference value), influence weight (proportion of key indicators); Based on each service provider in the aforementioned service-equipment combination list, extract the corresponding scoring sequence data for calculating the scoring decrease degree value. Taking service provider SF001 as an example below, its scoring sequence is: 、 、 、 、 . Suppose the number of scoring segments is , that is, 4 groups of scoring pairs are formed, which are: , decreasing, ; , rising, ; , falling, ; , falling, ; Substitute into the formula: ; The first term is the weighted absolute difference part, and the specific calculation is as follows: Item 1: ; Item 2: ; Item 3: ; Item 4: ; The sum is: ; The second term is the square root of the weighted square difference: Item 1: ; Item 2: ; Item 3: ; Item 4: ; The sum is: , and then take the square root: ; The final calculation result is: ; Among them, and are the adjacent item values of the scoring sequence, is the scoring change difference, is the falling discrimination flag item. If the score drops, it is set to 1, otherwise it is 0, represents the sum of the scoring differences of all falling segments, is the square difference aggregation item of the falling score segment, and the final score drop degree value is the combined result of these two parts.

[0034] The rental permission suspension judgment sub-module calls the service provider's score drop degree value, and compares it with the credit tolerance boundary value set for the equipment category. If the score drop degree value is greater than the boundary value, the corresponding equipment is marked as in a suspended rental state, and an equipment lease management list is generated; Based on the service provider's score drop degree value, perform status judgment. Continuing with the service provider SF001 as an example, its score drop degree value has been calculated as , the system calls the classification information of the devices associated with the service provider. Assuming that both EQ101 and EQ102 belong to the medium-sized device category, and the system presets the upper limit of the tolerance for the score reduction of the service provider corresponding to medium-sized devices to be 18.0. Subsequently, a judgment operation is performed to compare the score reduction degree value with the tolerance boundary value. The calculation method is as follows: If , it is considered that the score change is too large, and the device rental permission of this service provider needs to be suspended. Currently holds, and the judgment result is out of the boundary. Therefore, both devices EQ101 and EQ102 are marked as suspended. The suspended state result is encapsulated in the form of a device-status field combination: EQ101 - Suspended, EQ102 - Suspended. This judgment rule can be extended to all device types, and the boundary value fluctuates between 10 and 25 according to the device's credit tolerance. The setting method is based on constructing an interval value based on the device risk level and the average rental frequency. The currently defined threshold of 18.0 for medium-sized devices is used for this logical judgment. Finally, the system generates a list of suspended device rentals for subsequent processing by the operation platform.

[0035] The above is only a preferred embodiment of the present invention, and it does not limit the present invention in other forms. Any person skilled in the art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A credit-based IT equipment leasing management system, characterized in that, The system includes: The credit status recognition module collects the rental scores of the service provider multiple times, calculates the difference between two scores, combines the content of the fluctuation trend, calls the task urgency level to compare with the trend for judgment, marks it as to be inspected when the direction is concentrated and fluctuating, marks it as recommended when the score rises and is stable, and generates the rental credit label of the service provider; The equipment matching and screening module matches the urgency degree of the task based on the recommended level in the rental credit label of the service provider, screens the adaptation records with the service provider, identifies the task requirements and the equipment usage, and generates the rental comparison table of the equipment to be scheduled; The lease term behavior monitoring module calls the equipment number records in the rental comparison table of the equipment to be scheduled to start frequency, running duration and temperature change during the lease term, reads the self-check abnormal values, compares the standard deviation, and generates the list of equipment with abnormal operation; The equipment status review module extracts the list of equipment with abnormal operation, extracts the interface failure records of abnormal equipment, counts the number of failures and the response types, and generates the details of the maintenance task assignment; The rental permission backtracking module calculates the number of score drops and the cumulative amplitude according to the combination of the service provider and the equipment in the details of the maintenance task assignment, compares with the credit boundary of the equipment category for judgment, and generates the equipment rental management list.

2. The credit-based IT equipment leasing management system according to claim 1, wherein The rental credit label of the service provider includes the score change range, the change direction annotation, the fluctuation trend type, the corresponding relationship of the urgency level, and the credit level identifier. The rental comparison table of the equipment to be scheduled includes the recommended level screening option, the urgency degree matching value, the available equipment number, the screening result of the usage consistency, and the priority recommended combination number. The list of equipment with abnormal operation includes the equipment number record, the start frequency data, the running duration statistics, the temperature change index, the abnormal behavior label, and the internal abnormal monitoring value. The details of the maintenance task assignment include the list of abnormal equipment items, the number of interface response failures, the response failure type, the frequency limit comparison result, and the maintenance task assignment number. The equipment rental management list includes the service provider equipment association information, the number of score drops, the cumulative score drop, the equipment credit tolerance boundary, and the rental qualification status.

3. The credit-based IT equipment lease management system according to claim 2, wherein The credit status recognition module includes: The score change calculation sub-module extracts the score value of each rental task based on the credit score record formed by the service provider in the equipment rental task, calculates the numerical difference between adjacent two scores after arranging them in chronological order, judges the positive and negative of the difference and marks it as rising, falling and flat, and generates the score difference direction sequence; The fluctuation trend judgment sub-module calls the score difference direction sequence, calculates the direction concentration degree by counting the proportion of the same direction continuously, extracts the absolute value interval of the score difference to judge the fluctuation amplitude, and compares it with the urgency level of the corresponding task to obtain the direction fluctuation trend level; The credit label generation sub-module identifies the service provider tasks with continuously rising scores and stable fluctuations according to the direction fluctuation trend level and marks them as the recommended level. If the score changes and fluctuates, it is marked as the to be inspected level, and generates the rental credit label of the service provider.

4. The credit-based IT equipment leasing management system according to claim 3, wherein, The equipment matching and screening module includes: The level matching and screening submodule extracts the service provider number corresponding to the recommended level based on the recommended level in the service provider's rental credit tag, matches the urgency field registered in the rental task, screens the task items that meet the requirements, and obtains a list of available equipment numbers; The purpose consistency identification submodule calls the available equipment number list, extracts the service provider adaptation record associated with each equipment number, compares the consistency of the equipment purpose field with the rental task requirement field, removes inconsistent entries, and obtains a purpose consistent equipment number list; The priority combination generation submodule counts the number of adaptations of each device to the service party's tasks based on the list of device numbers with consistent purposes, calculates the priority of each combination, arranges the number combinations according to the priority, screens out items with insufficient priority, and generates a rental comparison table for the equipment to be scheduled.

5. The credit-based IT equipment lease management system according to claim 4, characterized in that, The lease behavior monitoring module includes: The equipment scheduling comparison submodule calls the equipment number in the rental comparison table of the equipment to be scheduled, screens the set of equipment numbers in the scheduling state within the current rental period, matches the equipment number with the corresponding rental period for associated storage, and obtains the equipment rental period matching table; The startup behavior analysis submodule collects the startup frequency of the device during the lease period, the continuous running time of each startup, and the temperature change range during the operation period based on the equipment rental period pairing table, performs segmented statistical calculations on the startup frequency and continuous running time, and compares the startup behavior standard threshold to determine whether the startup frequency and continuous running time are within the specified range, screens devices with abnormal startup behavior, and obtains the abnormal startup behavior mark value; The operation status comparison submodule calls the device number in the startup behavior abnormality mark value, reads the device self-test record, extracts the operation abnormality value data within the corresponding time period, and compares it item by item with the parameter allowable deviation range set in the equipment use standard, using the formula: ； Calculate and obtain the abnormality comparison value, list the equipment that exceeds the allowable threshold of operation behavior as the key record object, and obtain the list of abnormal operation equipment; Among them, represents the abnormal degree comparison value, represents the standard value of the device set temperature, represents the average value of the actually measured temperature of the device, represents the actual operation frequency value of the device, represents the threshold value of the specified start frequency of the device, represents the fluctuation value of the continuous duration of a single operation of the device, represents the i-th abnormal value in the device self-check record, represents the i-th allowable value of the device usage standard, and n is the total number of parameters in the device usage standard.

6. The credit-based IT equipment lease management system according to claim 5, characterized in that, The device status review module includes: The abnormal extraction submodule extracts the interface response failure records generated by the device during the lease period based on the abnormal operation device list, detects the interface identifier, response status code and request time, filters the record items of the response status code failure code, and determines whether it is within the lease period in combination with the request time, and obtains the number of interface failure records; The response identification submodule calls the interface failure record quantity value, identifies the response status code type, counts the number of response failures of each device within a fixed time period, compares it with the response frequency limit standard in the device classification description, determines whether the device has a dense response error distribution and the frequency exceeds the limit, and obtains the interface failure density value; The task generation submodule screens the devices that exceed the response frequency limit according to the interface failure density value, extracts the lease number, failure time period and device model, and uses the formula: ； Obtain the maintenance task intensity value through calculation, and compare it with the task assignment rules to generate the maintenance task assignment details; Among them, Q represents the intensity value of the maintenance task, A represents the failure density value of the equipment, X is the start time of the response failure, Y is the end time of the response failure, B is the number of equipment classifications, C is the response frequency limit value set for each type of equipment, and Z is the actually monitored response failure frequency.

7. The credit-based IT equipment lease management system according to claim 6, characterized in that, The rental permission recall module includes: The equipment service identification sub-module extracts the service provider identification and equipment coding information marked in each task according to the maintenance task assignment details, establishes the combined relationship between the service provider and the equipment, and generates a service equipment combination list; The scoring change calculation sub-module retrieves the scoring sequence based on the service provider identification contained in the service equipment combination list, sequentially obtains the adjacent scoring values before and after and calculates the difference, filters the data points with a decreasing score, accumulates the score decrease amplitude and the decrease times respectively, performs a scoring change aggregation operation, and uses the formula: ； Calculate and obtain the service provider scoring decrease degree value; Among them, represents the score value of the th item in the score sequence, is the score value of the next item, is the discriminant item for score decrease, is the number of scores that can form a comparison between the front and back items in the score sequence, is the score decrease degree value; The rental permission suspension judgment sub-module calls the service provider scoring decrease degree value, compares it with the credit tolerance boundary value set for the equipment category. If the scoring decrease degree value is greater than the boundary value, the corresponding equipment is marked as in a suspended rental state, and an equipment lease management list is generated.

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