Endoscope maintenance management system based on cloud

Abstract

The invention relates to the technical field of medical equipment management, in particular to a cloud-based endoscope maintenance management system, which comprises a load monitoring module, a risk early warning module, a work order scheduling module, a maintenance tracking module and a data linkage module. According to the invention, by collecting the use time, starting frequency and environmental data of the equipment and combining load curve fluctuation analysis, high-precision abnormity identification is realized, the equipment state sensing capability is improved, the equipment identifier and frequency are uploaded, the abnormal equipment set is constructed, data support is provided for high-risk equipment positioning, the abnormal frequency is compared with the mean value of the same model, and the positioning accuracy is improved. High-frequency abnormal equipment is screened, pre-recognition of maintenance risks is achieved, skill levels, geographic positions and task loads are integrated in scheduling, resource allocation is optimized, maintenance logs, accessory use and time nodes are recorded, the traceability of the maintenance process is enhanced, the accessory use frequency and replacement intervals are counted, a prediction model is established, and refined maintenance is perfected.

Description

Technical Field

[0001] The present invention relates to the technical field of medical equipment management, and in particular to a cloud-based endoscope maintenance management system. Background Art

[0002] The field of medical equipment management technology includes the whole process management of the use, maintenance, care, repair and resource allocation of medical equipment. Its core content is to manage the life cycle of medical equipment through information technology, and to carry out systematic and standardized management from equipment procurement, warehousing, use, regular maintenance to fault repair, so as to ensure the stable operation and safe use of medical equipment. With the continuous development of medical technology, the types and quantities of medical equipment are increasing, and the complexity of equipment management is also increasing. Therefore, the field of medical equipment management continues to develop new technical means to improve the efficiency and accuracy of equipment management, reduce maintenance costs and extend the service life of equipment.

[0003] Among them, the endoscope maintenance management system refers to a system that manages the entire process of maintenance and repair of endoscope equipment, mainly covering technical matters such as fault diagnosis, maintenance records, accessories management and equipment status tracking of endoscope equipment. By classifying the types of endoscope equipment failures, standardizing the maintenance process, and managing maintenance personnel and parts, it solves the problem of information islands in the maintenance of endoscope equipment, ensures the traceability of the equipment maintenance and maintenance process, and completes the effective management of endoscope equipment through information entry, status monitoring, maintenance records and alarm prompts, ensuring the efficiency and accuracy of maintenance work.

[0004] Existing technologies lack refined recognition capabilities for real-time monitoring of operational data, making it difficult to effectively capture the load characteristics of equipment during use. This leads to delayed abnormal state identification, inability to achieve rapid response and fault prediction, and a failure to generate structured data for equipment abnormalities, impacting the accuracy of subsequent maintenance strategy formulation. Furthermore, it is difficult to establish a risk classification model based on abnormality frequency, resulting in a lack of targeted maintenance resource allocation. Personnel scheduling relies on manual judgment or simple shift information, failing to factor in personnel skill levels and geographic location. This can easily lead to an imbalance in resource distribution, impacting the timeliness and quality of maintenance responses. Information recording during maintenance relies on manual entry, resulting in widespread data loss and information lags, severely hindering the traceability and evaluation of maintenance processes. A data correlation mechanism has not been established for accessory replacement frequency and maintenance cycles, making it impossible to accurately assess accessory lifespans. This can lead to wasted accessory resources or increased equipment failure rates. In actual operation, this can result in equipment interruptions, delayed repair responses, increased maintenance costs, and decreased management efficiency, severely impacting the overall service level and safety assurance capabilities of endoscopy equipment. Summary of the Invention

[0005] In order to solve the technical problems of the prior art, an embodiment of the present invention provides a cloud-based endoscope maintenance management system. The technical solution is as follows:

[0006] In one aspect, a cloud-based endoscope maintenance management system is provided, comprising:

[0007] Based on endoscope equipment data, the load monitoring module extracts the usage time, startup frequency, and ambient temperature and humidity data of the imaging module and operating handle, analyzes the real-time load curve, determines abnormal segments based on the fluctuation amplitude and the set stable range, and simultaneously uploads the data to the cloud to generate a load abnormality device set;

[0008] The risk warning module calls the device identification information in the load abnormality device set, retrieves the device abnormality records in the cloud work order, compares the abnormality frequency of each device with the average value of devices of the same model, selects the device numbers with high abnormality frequency, and generates a maintenance risk trigger list;

[0009] The work order scheduling module calls the equipment number in the maintenance risk trigger list, queries the cloud maintenance personnel's schedule, skill level and number of orders, identifies the geographical distance of the equipment location, compares the task load, selects the best personnel and updates the scheduling information to the cloud work order pool to obtain the scheduling-bound work order item;

[0010] The maintenance tracking module is based on the scheduling and binding work order items, records three types of information: operation logs uploaded during the maintenance process, parts usage, and execution time nodes, organizes the work order process nodes according to the timeline and synchronizes the cloud status panel to obtain a snapshot of the endoscope maintenance process record.

[0011] As a further solution of the present invention, the load abnormality equipment set includes equipment operation stability indicators, abnormality trigger time period, and equipment performance degradation characteristics; the maintenance risk trigger list includes high-frequency abnormality alarm weights, equipment original maintenance ratios, and model abnormality deviation degrees; the scheduling binding work order items include personnel skill matching, geographical task coverage efficiency, and scheduling load balancing coefficients; the endoscope maintenance process record snapshot includes maintenance task progress nodes, accessory replacement type statistics, and operation behavior pattern characteristics.

[0012] As a further solution of the present invention, the load monitoring module includes:

[0013] The operation record extraction submodule is based on endoscope equipment data, including the usage time and startup frequency data of the imaging module and operating handle, and collects equipment ambient temperature and humidity records. It is integrated into a single data structure according to the equipment number, and after eliminating missing items, it generates a equipment operation status record table;

[0014] The load abnormality determination submodule determines whether the fluctuation section exceeds the standard range based on the equipment operation status record table, including the usage time data and temperature and humidity values, combined with the set upper and lower limits of the usage time stability range and the environmental parameter thresholds, and screens the corresponding equipment number and abnormal frequency to generate a list of abnormal equipment operations;

[0015] The maintenance task generation submodule calls the abnormal equipment operation list, classifies and summarizes the equipment numbers whose frequency values are higher than the set maintenance start frequency threshold, matches the idle time periods in the maintenance plan and assigns corresponding maintenance personnel to generate a load abnormal equipment set.

[0016] As a further solution of the present invention, the risk warning module includes:

[0017] The abnormal frequency extraction submodule calls the load abnormality device set, retrieves the original maintenance records of the corresponding devices in the cloud work order, counts the number of maintenance records of each device, and establishes a maintenance frequency information set;

[0018] The same-type deviation identification submodule extracts the equipment maintenance frequency by model based on the maintenance frequency information set, calculates the average number of maintenance times as a reference value, identifies the deviation of each device from the reference value, filters the device numbers whose deviation exceeds the threshold, and generates a maintenance frequency anomaly list;

[0019] The maintenance work order trigger submodule calls the maintenance frequency exception list, queries the scheduling status of the equipment in the current maintenance plan, filters out the equipment that has not entered the schedule and marks the maintenance emergency status, combines the equipment number, maintenance frequency and emergency status, and generates a maintenance risk trigger list.

[0020] As a further solution of the present invention, the work order scheduling module includes:

[0021] The maintenance candidate matching submodule calls the equipment number in the maintenance risk trigger list, queries the cloud maintenance personnel's schedule, skill level and number of orders, combines the equipment location and service area distance, sorts according to skill matching and task load differences, selects high-priority personnel numbers, and generates an endoscope maintenance matching list;

[0022] The work order task binding submodule analyzes the work order binding relationship between personnel and equipment according to the personnel number and the equipment number to be dispatched in the endoscope maintenance matching list, updates the binding result to the cloud work order pool and marks the status as scheduled, and obtains the scheduled binding work order item.

[0023] As a further solution of the present invention, the task scheduling matching value adopts the formula:

[0024]

[0025] Among them, R represents the task scheduling matching value, N represents the number of personnel numbers in the matching list, D represents the number of equipment numbers to be dispatched, and T z represents the optimal scheduling time value corresponding to the z-th device, Y z represents the response time offset of the z-th person, and M represents the total number of valid binding candidates.

[0026] As a further solution of the present invention, the maintenance tracking module includes:

[0027] The process log collection submodule collects maintenance logs, parts usage records and time node data based on the scheduling and binding work order items, formats and integrates them according to the work order number, and generates a maintenance process data set;

[0028] The node snapshot identification submodule categorizes and organizes the log content and parts usage items according to the time nodes in the maintenance process data set, marks the maintenance behaviors and sorts them in chronological order, and generates a work order execution timeline entry set;

[0029] The status panel synchronization submodule calls the time nodes, maintenance actions and accessory usage status of the work order execution timeline entries, calculates the time field mapping correction value, writes the node information into the record table after checking the synchronization status between fields, and updates the panel display status to the current node to obtain a snapshot of the endoscope maintenance process record.

[0030] As a further solution of the present invention, the time field mapping correction value adopts the formula:

[0031]

[0032] Where T represents the time field mapping correction value, t ij represents the original time value of the jth unit in the i-th node, w ij Represents the time weight factor of the jth unit of the i-th node, m ij Represents the usage quantity of the material field in the j-th unit of the i-th node, Represents the average usage quantity of the material field in all units of the i-th node, s ij Represents the progress status code value of the jth unit in the i-th node, and n represents the number of units included in the i-th node.

[0033] As a further solution of the present invention, the system also includes a data linkage module:

[0034] The data linkage module reads the accessory usage details in the maintenance process record snapshot, summarizes the usage frequency and replacement interval by accessory type, binds it with the equipment number and uploads it to the cloud prediction port to generate an endoscope accessory maintenance list;

[0035] The endoscope accessories maintenance list includes accessory usage trend categories, replacement cycle prediction values, and accessory life distribution models.

[0036] As a further solution of the present invention, the data linkage module includes:

[0037] The accessory details arrangement submodule reads the accessory usage details in the maintenance process record snapshot, summarizes the replacement frequency and replacement time interval by accessory type, binds the corresponding equipment number, and generates an endoscope accessory replacement record table;

[0038] The maintenance list generation submodule matches the cloud prediction port configuration item and writes the prediction field according to the endoscope accessories replacement record table, identifies the accessory task items of the device group, and generates an endoscope accessories maintenance list.

[0039] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0040] By continuously collecting the usage time, startup frequency, and ambient temperature and humidity data of the imaging module and operating handle, and conducting real-time analysis of the load curve fluctuation amplitude, it is possible to achieve dynamic perception of the equipment's operating status and high-precision anomaly identification, thereby improving the sensitivity and reliability of load status anomaly judgments. By uploading the equipment identification and frequency to the cloud, a traceable set of load abnormality equipment is established, providing a data basis for the subsequent precise positioning of high-risk equipment. The equipment abnormality frequency is cross-compared with the average value of equipment of the same model, and the equipment numbers with abnormally high failure frequencies are screened out. This allows for pre-judgment of maintenance risks at the data level and strengthens the targeted nature of maintenance plans. The skill level of maintenance personnel, geographical distance, and number of task orders are comprehensively considered during the scheduling process to ensure the balance and response efficiency of task distribution, while improving the scientific nature of resource allocation. The maintenance process records include operation logs, parts usage and time nodes, effectively capturing maintenance behavior paths and parts replacement characteristics, making the maintenance trajectory analyzable and verifiable. Through statistics on parts usage details and replacement intervals, a parts maintenance data chain for equipment numbers is established, forming a parts usage prediction model based on real maintenance scenarios, realizing forward-looking management and refined control of maintenance strategies. Through the interconnection of equipment operation data and maintenance data, a five-in-one management mechanism of anomaly identification, risk prediction, resource scheduling, maintenance tracking and maintenance prediction is constructed, which significantly improves the closed-loop management capability of the maintenance process and enhances the efficient operation and resource utilization of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0042] Figure 1 is a schematic diagram of a cloud-based endoscope maintenance management system provided by an embodiment of the present invention;

[0043] Figure 2 Schematic diagram of the system framework of the present invention;

[0044] Figure 3 This is a flow chart of the load monitoring module in the present invention;

[0045] Figure 4 This is a flow chart of the risk warning module in the present invention;

[0046] Figure 5 This is a flow chart of the work order scheduling module in the present invention;

[0047] Figure 6 This is a flow chart of the maintenance tracking module in the present invention;

[0048] Figure 7 This is a flow chart of the data linkage module in the present invention. DETAILED DESCRIPTION

[0049] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0050] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0051] In the embodiments of the present invention, the terms "image" and "picture" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same. The terms "of," "corresponding," and "corresponding" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same.

[0052] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0053] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0054] The embodiment of the present invention provides a cloud-based endoscope maintenance management system, such as Figure 1-2 The schematic diagram of the cloud-based endoscope maintenance management system shown in FIG. includes:

[0055] Based on endoscope equipment data, the load monitoring module extracts the usage time, startup frequency, and ambient temperature and humidity data of the imaging module and operating handle, analyzes the real-time load curve, determines abnormal segments based on the fluctuation amplitude and the set stable range, extracts the device identification and frequency, and uploads them to the cloud simultaneously to generate a load abnormality device set;

[0056] The risk warning module uses the device identification information in the load abnormality device collection to retrieve the device abnormality records in the cloud work order, compares the abnormality frequency of each device with the average value of devices of the same model, selects the device numbers with high abnormality frequency, and generates a maintenance risk trigger list;

[0057] The work order scheduling module calls the equipment number in the maintenance risk trigger list, queries the cloud maintenance personnel's schedule, skill level, and number of orders, identifies the geographical distance of the equipment location, compares the task load, selects the best personnel, and updates the scheduling information to the cloud work order pool to obtain the scheduling-bound work order item;

[0058] The maintenance tracking module is based on scheduling and binding work order items. It records three types of information: operation logs uploaded during the maintenance process, parts usage, and execution time nodes. It organizes the work order process nodes according to the timeline and synchronizes them with the cloud status panel to obtain a snapshot of the endoscope maintenance process record.

[0059] The data linkage module reads the accessory usage details in the maintenance process record snapshot, summarizes the usage frequency and replacement interval by accessory type, binds it with the equipment number and uploads it to the cloud prediction port to generate an endoscope accessory maintenance list.

[0060] The load abnormality equipment set includes equipment operation stability indicators, abnormality triggering time period, and equipment performance degradation characteristics. The maintenance risk trigger list includes high-frequency abnormality alarm weights, equipment original maintenance ratio, and model abnormality deviation degree. The scheduling binding work order items include personnel skill matching, geographical task coverage efficiency, and scheduling load balancing coefficient. The endoscope maintenance process record snapshot includes maintenance task progress nodes, accessory replacement type statistics, and operation behavior pattern characteristics. The endoscope accessory maintenance list includes accessory usage trend categories, replacement cycle prediction values, and accessory life distribution models.

[0061] Specifically, if Figure 2 、 3 As shown, the load monitoring module includes:

[0062] The operation record extraction submodule is based on endoscope equipment data, including the usage time and startup frequency data of the imaging module and operating handle, and collects equipment ambient temperature and humidity records. It is integrated into a single data structure according to the equipment number, and after eliminating missing items, it generates a equipment operation status record table;

[0063] By collecting the usage time and startup frequency data of the operating handle of the endoscope equipment, as well as the equipment environmental temperature and humidity records, a set of data structures is formed, which will then be integrated according to the equipment number. When integrating the data, attention should be paid to the processing of the original data. First, through verification, the missing items in the data are eliminated, and then the valid data are summarized. The data types used include usage time in timestamp format, number of equipment startups, etc., and environmental temperature and humidity data. The temperature and humidity data are represented by real-time monitored temperature (for example, 24°C) and humidity (for example, 55%). The data is collected once every certain time (for example, every hour). To ensure accuracy, if the temperature data of some equipment is missing or the startup frequency record is incomplete, the equipment record will be eliminated. The remaining data is integrated by equipment number to form a complete operation record of the equipment, and then generate an equipment operation status record table. The generated record table should include information such as equipment number, operation time, number of startups, ambient temperature and humidity records, etc., for subsequent load abnormality judgment and analysis.

[0064] The load abnormality judgment submodule uses the equipment operation status record table, including usage time data and temperature and humidity values, combined with the set upper and lower limits of the usage time stability range and environmental parameter thresholds, to determine whether the fluctuation section exceeds the standard range, screen the corresponding equipment number and abnormal frequency, and generate a list of abnormal equipment operations;

[0065] By analyzing the device's usage time data and combining it with the set upper and lower limits of the usage time stability range, it is determined whether the device has been in an abnormal operating state for a long time. For example, the set usage time stability range is 20 hours to 30 hours. When the device is in use for more than 30 hours or less than 20 hours, it is an abnormal state. The ambient temperature and humidity data are analyzed and the temperature and humidity thresholds are set. For example, the temperature threshold is 20℃ to 30℃, and the humidity threshold is 45% to 65%. If the device's operating status exceeds the set range, for example, the temperature reaches 35℃ or the humidity is 80%, the device's operating status is considered abnormal. All abnormal devices will be screened out and a list of abnormal device operations will be generated. The list lists each device number and its abnormal frequency. The urgency of the device can be determined based on the abnormal frequency of the device.

[0066] The maintenance task generation submodule calls the abnormal equipment operation list, classifies and summarizes the equipment numbers whose frequency values are higher than the set maintenance start frequency threshold, matches the idle time periods in the maintenance plan and assigns corresponding maintenance personnel to generate a load abnormality equipment set;

[0067] This list is used to filter out devices with a high frequency of abnormalities. The device frequency value is compared with a preset maintenance initiation frequency threshold (assuming the threshold is 5). If a device's abnormality frequency exceeds 5, it requires maintenance. Devices with high frequencies are aggregated by device number. Based on the idle time periods and personnel arrangements in the maintenance plan, devices and maintenance personnel are matched. If a maintenance personnel has free time within a specific time period, the maintenance task for that device is assigned to them. This ultimately generates a set of load abnormality devices, providing a list for subsequent maintenance operations.

[0068] Specifically, if Figure 2 、 4 As shown, the risk warning module includes:

[0069] The abnormal frequency extraction submodule calls the load abnormality equipment set, retrieves the original maintenance records of the corresponding equipment in the cloud work order, counts the number of maintenance records of each equipment, and establishes a maintenance frequency information set;

[0070] Retrieve the original maintenance records of the corresponding equipment in the cloud work order. When the equipment load is abnormal, first retrieve the equipment ID and the corresponding abnormal time point from the load monitoring database. Then, based on the equipment ID, query all maintenance work orders related to the equipment ID in the cloud work order database. The maintenance work order includes the specific fault condition of the equipment, maintenance time, maintenance measures and other information. The information is extracted and each maintenance record is analyzed. The record will include the specific time of the fault, the maintenance measures taken and the status of the equipment after the repair. The data is used to calculate the maintenance frequency of the equipment. For example, equipment A has recorded 5 repairs in the past year. Each repair dealt with a different problem, but the operating status of the equipment returned to normal after the repair. The maintenance frequency of each device is calculated through the data, and the information is stored in the maintenance frequency information set to generate a maintenance frequency anomaly list.

[0071] The same-type deviation identification submodule extracts the equipment maintenance frequency by model based on the maintenance frequency information set, calculates the average number of maintenance times as a reference value, identifies the deviation of each device from the reference value, filters the device numbers with deviations exceeding the threshold, and generates a maintenance frequency anomaly list;

[0072] Extract equipment maintenance frequency by model. Classify all equipment by model, for example, classify all Model X equipment. Then, for each type of equipment, extract all maintenance frequency data for that model from the maintenance frequency information set. Perform statistical analysis on the data and calculate the average number of maintenance times for each model. This average will serve as the benchmark for subsequent comparisons. For example, if the average number of maintenance times for Model X equipment is 3 times / year, compare the maintenance times of each device with the average number of maintenance times for that model and calculate the deviation value. For example, if the maintenance times of a Model X device are 5 times / year, the deviation value is 2 times / year. If this deviation value exceeds the set threshold, such as 1.5 times / year, the device number will be filtered out and marked as having an abnormal maintenance frequency, and a maintenance frequency abnormality list will be generated.

[0073] The maintenance work order trigger submodule calls the maintenance frequency exception list, queries the scheduling status of the equipment in the current maintenance plan, filters out the equipment that is not scheduled and marks it as maintenance emergency. It combines the equipment number, maintenance frequency and emergency status to generate a maintenance risk trigger list;

[0074] Query the scheduling status of the equipment in the current maintenance plan, read all the equipment numbers in the maintenance frequency exception list, and for each equipment in the list, query the current maintenance plan database to view the scheduling status of the equipment, including the time of the maintenance plan, the person in charge, and the expected maintenance measures. For those equipment that have not yet entered the scheduling plan, mark them as maintenance emergency status. For example, if equipment B number is in the exception list and the query result shows that the equipment has not yet been scheduled in any maintenance plan, then mark equipment B as emergency status and record it. Combine the three pieces of information: equipment number, maintenance frequency, and emergency status to generate a maintenance risk trigger list. This list will be used for subsequent maintenance scheduling and priority determination to ensure that high-risk equipment is given priority.

[0075] Specifically, if Figure 2 、 5 As shown, the work order scheduling module includes:

[0076] The maintenance candidate matching submodule calls the equipment number in the maintenance risk trigger list, queries the cloud maintenance personnel's schedule, skill level, and number of orders, combines the equipment location and service area distance, sorts according to skill matching and task load differences, selects high-priority personnel numbers, and generates an endoscope maintenance matching list;

[0077] First, a cloud query is performed to locate the device by device number. The cloud-based maintenance personnel data associated with the device, including shift information, skill level, and number of orders received, is retrieved. The device's location information is then compared with the distance to the maintenance personnel's service area, and the distance difference between each maintenance personnel and the device is calculated. For each maintenance personnel, a comprehensive assessment is performed based on their skill match and current task load variance. Skill match refers to the degree of alignment between the maintenance personnel's skill level and the required skill level of the device. Task load variance refers to the difference between the maintenance personnel's current number of orders and their ideal number of orders. After the calculation is completed, each maintenance personnel is scored according to the comprehensive data. The higher the score, the more suitable the maintenance personnel is to perform the maintenance task of the equipment. The maintenance personnel are sorted from high to low according to their comprehensive scores to screen out maintenance personnel with high priority. Assuming that the equipment number is E001, the equipment is located in area A, and the maintenance personnel P1 has an advanced skill level in area A, a small number of orders, and is 5 kilometers away from the equipment. P2 has intermediate skills, a large number of orders, and is 10 kilometers away from the equipment. P1's priority is set to higher and P2's priority is lower, and finally an endoscope maintenance matching list is generated.

[0078] The work order task binding submodule calculates the task scheduling matching value based on the personnel number in the endoscope maintenance matching list and the equipment number to be dispatched, analyzes the work order binding relationship between the personnel and the equipment, updates the binding result to the cloud work order pool and marks the status as scheduled, and obtains the scheduling binding work order item;

[0079] According to the association between the personnel number in the endoscope maintenance matching list and the number of the equipment to be dispatched, the task scheduling matching value is calculated. The task scheduling matching value is determined by considering the personnel number, equipment number and the work order binding relationship between the two. It analyzes whether the personnel has the skills and experience required to complete the current equipment maintenance. It is also necessary to determine whether the personnel has received similar tasks, whether the equipment failure type is suitable for the maintenance personnel's skills, etc. Based on the personnel's skill matching and task load, the task allocation is further optimized to ensure the rationality and balance of task allocation. Assume that the work order binding relationship between maintenance personnel P1 and equipment E001 is strong, because P1's skills are highly matched with the equipment failure type, and P1 currently has a small number of orders, while the maintenance task of equipment E001 requires more complex technical support. Through calculation, it is determined that P1 is the best choice for the task, and the work order task is bound to P1. The binding result is updated to the cloud work order pool, and the work order task is marked as scheduled;

[0080] Task scheduling matching value, using the formula:

[0081]

[0082] Among them, R represents the task scheduling matching value, N represents the number of personnel numbers in the matching list, D represents the number of equipment numbers to be dispatched, and T z represents the optimal scheduling time value corresponding to the z-th device, Y z represents the response time offset of the zth person, and M represents the total number of valid binding candidates;

[0083] N: The number of personnel numbers in the matching list, obtained by counting the list of maintenance personnel currently waiting to be dispatched in the work order system. According to actual operation data, there are currently 12 maintenance personnel waiting to be dispatched, so N = 12;

[0084] D: The number of devices to be dispatched, obtained by counting the number of devices currently waiting for repair. According to the equipment management system data, there are currently 10 devices waiting for repair, so D = 10;

[0085] T z : The optimal scheduling time value corresponding to the z-th device is calculated by analyzing the original maintenance data and combining the device type, fault level, and maintenance complexity. Taking the first device as an example, the optimal scheduling time is 4 hours;

[0086] Y z : The response time offset of the zth person. The response time of each maintenance person is calculated based on the person's work schedule, geographical location, and current task load. Taking the first person as an example, the response time offset is 1 hour;

[0087] M: The total number of valid binding candidates. The number of valid binding candidates is determined by matching feasible combinations of personnel and equipment. According to the current data, there are 5 valid sets of personnel and equipment binding candidates, so M = 5.

[0088] Specific calculation process:

[0089] Calculate N·D: N·D=12·10=120;

[0090] calculate

[0091] Assume that there are 5 sets of binding candidates T z and Y z They are:

[0092] Group 1: T1 = 4 hours, Y1 = 1 hour;

[0093] Group 2: T2 = 5 hours, Y2 = 2 hours;

[0094] Group 3: T3 = 6 hours, Y3 = 1.5 hours;

[0095] Group 4: T4 = 3.5 hours, Y4 = 1 hour;

[0096] Group 5: T5 = 4.5 hours, Y5 = 1.2 hours;

[0097] Compute the difference for each group:

[0098] Group 1: 4-1 = 3 hours;

[0099] Group 2: 5-2 = 3 hours;

[0100] Group 3: 6-1.5=4.5 hours;

[0101] Group 4: 3.5-1=2.5 hours;

[0102] Group 5: 4.5-1.2=3.3 hours;

[0103] Sum:

[0104] calculate

[0105] Calculate the numerator:

[0106] Calculate the denominator: 1 + |ND| = 1 + |12 - 10| = 1 + 2 = 3;

[0107] Calculate the final result R:

[0108] The results show that the current matching degree between personnel and equipment is high, and the task scheduling matching value is 38.654, indicating that the system scheduling efficiency is relatively ideal under the current number of personnel and equipment and their corresponding scheduling time differences. This value can be used to further optimize the allocation strategy of personnel and equipment and improve the overall maintenance efficiency.

[0109] Specifically, if Figure 2 、 6 As shown, the maintenance tracking module includes:

[0110] The process log collection submodule collects maintenance logs, parts usage records, and time node data based on the scheduling and binding of work order items, formats and integrates them according to the work order number, and generates a maintenance process data set;

[0111] Call the original maintenance log entries associated with the work order number in the maintenance management database, filter the records whose time fields are between the start time of the scheduling work order and the current time, and sort the log content entries in ascending order by the time field. Read the parts call records under the work order number in the parts management in turn, including the parts number, call time, inventory entry and exit status and the corresponding equipment number. Then compare and splice the above log entries with the parts records in the time dimension, and align them through the field "event occurrence time". When multiple records are found to overlap at a certain time point, it is necessary to call the priority field for sorting, and give priority to selecting records marked as "on-site operation" to insert into the final data set. If there is no "on-site operation" mark, select the first record in the lexicographical order of the parts number and insert it. Enter, then call the node event record in the scheduling, extract the node data generated by the work order item during the scheduling process, including operation signs such as "start", "pause", "continue", and "complete" and their timestamps, and convert them into a standard format. The fields are unified into "node type" and "node time". The above maintenance log content, parts record and node data are connected with "work order number" as the primary key, and the data format fields are uniformly processed, including converting the operator number in the log entry into a name, matching the equipment number in the parts call record with the corresponding equipment name, and converting the node type from a digital tag to a text description. After completion, the three types of data are merged into a unified format record set to generate a maintenance process data set, which is assigned a unique identification number and stored in the process data set.

[0112] The node snapshot identification submodule categorizes the log content and parts usage items according to the time nodes in the maintenance process dataset, marks the maintenance behaviors and sorts them in chronological order, and generates a set of work order execution timeline entries;

[0113] Classify and organize the log content and accessory usage items. When reading each log record in the data set, extract its "event occurrence time" field and compare it with all time nodes. For records with a time difference of less than or equal to 1 minute, they are considered to match the node and the log entry is attached to the corresponding node. If a log record appears near multiple node times, calculate each time difference and select the node with the minimum difference to attach it. Perform the same operation on the accessory usage record, compare its "call time" field with all node times, and attach it to the closest node in the above manner. If the difference is the same, select the first node in the node type priority order "Start>Continue>Pause>Complete" for attribution. After the arrangement is completed, the logs and accessory items attached to each node are assigned. Perform internal sorting and arrange in ascending order according to the "event occurrence time" field. If the time field is repeated, sort the log by operation type "disassembly>replacement>installation", and arrange the accessories in ascending order by equipment number. Then generate an execution behavior label for each node, call the defined operation code and match it with the keyword field in the log content. When the log description contains keywords such as "replacement", "installation", and "detection", it is marked as the corresponding maintenance behavior. The same treatment is applied when keywords such as "use" and "removal" appear in the accessory record. Combine the behavior label and time sequence to generate a work order execution timeline entry set, which includes fields such as "node time", "behavior description", "accessory change information", and "operator information", and is archived in the entry set library according to the work order number.

[0114] The status panel synchronization submodule calls the time nodes, maintenance actions, and accessory usage status of the work order execution timeline entries, calculates the time field mapping correction value, checks the synchronization status between fields, writes the node information into the record table, and updates the panel display status to the current node to obtain a snapshot of the endoscope maintenance process record;

[0115] The time field maps the correction value using the formula:

[0116]

[0117] Where T represents the time field mapping correction value, t ij represents the original time value of the jth unit in the i-th node, w ij Represents the time weight factor of the jth unit of the i-th node, m ij Represents the usage quantity of the material field in the j-th unit of the i-th node, Represents the average usage quantity of the material field in all units of the i-th node, s ij represents the progress status code value of the jth unit in the i-th node, and n represents the number of units included in the i-th node;

[0118] Time value t ij:Assume that the original time value t of the jth unit in the i-th node ij It is obtained based on past event records, for example: the time value of node 1 is t 11 = 2.5 hours, t 12 =3.0 hours;

[0119] Time weight factor w ij :w ij To adjust the weight of the time value, set it according to the priority of the node. For example, if the task of unit 1 of node 1 is more urgent, then w 11 =1.5, and unit 2 is less urgent, then w 12 =0.5, the weight factor is set based on the urgency of the task and the priority of execution;

[0120] Material usage quantity m ij :m ij Indicates the quantity of material used in the jth unit of the i-th node. For example, if unit 1 of node 1 uses 5 units of material, m 11 =5, unit 2 uses 3 units, m 12 =3, data is directly obtained through the material inventory management system;

[0121] Average material usage It is the average value of the quantity of all unit materials used in the node, and the calculation method is (m 11 +m 12 ) / 2=(5+3) / 2=4;

[0122] Progress status code value s ij :s ij Indicates progress status, for example: if unit 1 is 50% complete, then s 11 = 0.5, Unit 2 is 75% complete, then s 12 =0.75, the value is automatically updated based on the progress tracking system;

[0123] Number of elements n: In this example, n = 2 means the node consists of two elements;

[0124] Calculate the numerator of the time mapping correction value T:

[0125]

[0126] Calculate the denominator of the time mapping correction value T:

[0127] The final time mapping correction value T:

[0128] The calculated value of T is 2.05, indicating that after accounting for material usage deviation, time weight, and progress status, the time mapping correction value for the i-th node is 2.05, representing the time efficiency adjustment coefficient for that node. This result is used to adjust the cloud status panel display to ensure real-time synchronization and accurate reflection of time, progress, and material fields, thereby optimizing resource allocation and process monitoring.

[0129] Specifically, if Figure 2 、 7 As shown, the data linkage module includes:

[0130] The accessory details sorting submodule reads the accessory usage details in the maintenance process record snapshot, summarizes the replacement frequency and replacement time interval by accessory type, binds the corresponding equipment number, and generates an endoscope accessory replacement record sheet;

[0131] By calling the interface with the maintenance process record database, the accessory usage record corresponding to the work order is obtained, including key data such as accessory number, usage time, and replacement frequency. To ensure data accuracy, SQL query statements are used for data filtering. The statement includes the work order number and maintenance date as filtering conditions to ensure that the obtained data is consistent with the actual maintenance record. The extracted data is classified according to the accessory type. The specific data used for each type of accessory is loaded into the memory data table for processing. The replacement frequency and replacement time interval of each type of accessory are calculated using the built-in algorithm. This calculation process involves parsing the timestamp and calculating the time difference. By looping through all accessory records, the average replacement time interval of each type of accessory is calculated. The calculation results are then formatted into a report format and summarized by accessory type and its corresponding equipment number. The final report not only includes the detailed usage records of the accessories, but also integrates the statistical results of the replacement frequency and time interval to form an endoscope accessory replacement record table, which is stored in the database for subsequent query and analysis.

[0132] The maintenance list generation submodule matches the cloud-based prediction port configuration items and writes the prediction fields based on the endoscope accessories replacement record table, identifies the accessories task items grouped by equipment, and generates an endoscope accessories maintenance list;

[0133] The endoscope accessory replacement record table is retrieved from the database and the data in the table is read. This process includes extracting the replacement frequency and replacement interval of each accessory from the SQL database, connecting to the cloud prediction port through the built-in API, and sending the obtained accessory replacement data to the cloud. The cloud prediction port uses a preset machine learning model based on the received data to predict future maintenance needs. The prediction process covers time series analysis and regression models to ensure the accuracy of the prediction results. After the prediction is completed, the cloud returns the prediction results to the maintenance list. The received data includes not only the predicted maintenance needs, but also the predicted time point and the type of accessory to be replaced. Based on the equipment number to which the accessory belongs, the accessory replacement tasks for different equipment groups are identified. During the identification process, the accessory tasks are grouped according to the equipment number. The grouping algorithm is optimized based on the equipment usage frequency and the original maintenance records to ensure the rationality of the task grouping. Based on the grouping results and the cloud prediction information, an endoscope accessory maintenance list is generated. The list details the accessories that need to be maintained or replaced in a certain period of time for each equipment group. After the list is generated, it is automatically formatted and saved for use by the maintenance department.

[0134] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. Cloud-based endoscope maintenance management system, characterized by: The system comprises: Based on endoscope equipment data, the load monitoring module extracts the usage time, startup frequency, and ambient temperature and humidity data of the imaging module and operating handle, analyzes the real-time load curve, determines abnormal segments based on the fluctuation amplitude and the set stable range, and simultaneously uploads the data to the cloud to generate a load abnormality device set; The risk warning module calls the device identification information in the load abnormality device set, retrieves the device abnormality records in the cloud work order, compares the abnormality frequency of each device with the average value of devices of the same model, selects the device numbers with high abnormality frequency, and generates a maintenance risk trigger list; The work order scheduling module calls the equipment number in the maintenance risk trigger list, queries the cloud maintenance personnel's schedule, skill level and number of orders, identifies the geographical distance of the equipment location, compares the task load, selects the best personnel and updates the scheduling information to the cloud work order pool to obtain the scheduling-bound work order item; The maintenance tracking module is based on the scheduling and binding work order items, records three types of information: operation logs uploaded during the maintenance process, parts usage, and execution time nodes, organizes the work order process nodes according to the timeline and synchronizes the cloud status panel to obtain a snapshot of the endoscope maintenance process record.

2. The cloud-based endoscope maintenance management system according to claim 1, characterized in that: The load abnormality equipment set includes equipment operation stability indicators, abnormality triggering time period, and equipment performance degradation characteristics. The maintenance risk trigger list includes high-frequency abnormality alarm weights, equipment original maintenance ratios, and model abnormality deviation degrees. The scheduling binding work order items include personnel skill matching, geographical task coverage efficiency, and scheduling load balancing coefficients. The endoscope maintenance process record snapshot includes maintenance task progress nodes, accessory replacement type statistics, and operation behavior pattern characteristics.

3. The cloud-based endoscope maintenance management system according to claim 1, characterized in that: The load monitoring module includes: The operation record extraction submodule is based on endoscope equipment data, including the usage time and startup frequency data of the imaging module and operating handle, and collects equipment ambient temperature and humidity records. It is integrated into a single data structure according to the equipment number, and after eliminating missing items, it generates a equipment operation status record table; The load abnormality determination submodule determines whether the fluctuation section exceeds the standard range based on the equipment operation status record table, including the usage time data and temperature and humidity values, combined with the set upper and lower limits of the usage time stability range and the environmental parameter thresholds, and screens the corresponding equipment number and abnormal frequency to generate a list of abnormal equipment operations; The maintenance task generation submodule calls the abnormal equipment operation list, classifies and summarizes the equipment numbers whose frequency values are higher than the set maintenance start frequency threshold, matches the idle time periods in the maintenance plan and assigns corresponding maintenance personnel to generate a load abnormal equipment set.

4. The cloud-based endoscope maintenance management system according to claim 3, characterized in that: The risk warning module includes: The abnormal frequency extraction submodule calls the load abnormality device set, retrieves the original maintenance records of the corresponding devices in the cloud work order, counts the number of maintenance records of each device, and establishes a maintenance frequency information set; The same-type deviation identification submodule extracts the equipment maintenance frequency by model based on the maintenance frequency information set, calculates the average number of maintenance times as a reference value, identifies the deviation of each device from the reference value, filters the device numbers whose deviation exceeds the threshold, and generates a maintenance frequency anomaly list; The maintenance work order trigger submodule calls the maintenance frequency exception list, queries the scheduling status of the equipment in the current maintenance plan, filters out the equipment that has not entered the schedule and marks the maintenance emergency status, combines the equipment number, maintenance frequency and emergency status, and generates a maintenance risk trigger list.

5. The cloud-based endoscope maintenance management system according to claim 4, characterized in that: The work order scheduling module includes: The maintenance candidate matching submodule calls the equipment number in the maintenance risk trigger list, queries the cloud maintenance personnel's schedule, skill level and number of orders, combines the equipment location and service area distance, sorts according to skill matching and task load differences, selects high-priority personnel numbers, and generates an endoscope maintenance matching list; The work order task binding submodule analyzes the work order binding relationship between personnel and equipment according to the personnel number and the equipment number to be dispatched in the endoscope maintenance matching list, updates the binding result to the cloud work order pool and marks the status as scheduled, and obtains the scheduled binding work order item.

6. The cloud-based endoscope maintenance management system according to claim 5, characterized in that: The task scheduling matching value adopts the formula: Among them, R represents the task scheduling matching value, N represents the number of personnel numbers in the matching list, D represents the number of equipment numbers to be dispatched, and T z represents the optimal scheduling time value corresponding to the z-th device, Y z represents the response time offset of the z-th person, and M represents the total number of valid binding candidates.

7. The cloud-based endoscope maintenance management system according to claim 5, characterized in that: The maintenance tracking module includes: The process log collection submodule collects maintenance logs, parts usage records and time node data based on the scheduling and binding work order items, formats and integrates them according to the work order number, and generates a maintenance process data set; The node snapshot identification submodule categorizes and organizes the log content and parts usage items according to the time nodes in the maintenance process data set, marks the maintenance behaviors and sorts them in chronological order, and generates a work order execution timeline entry set; The status panel synchronization submodule calls the time nodes, maintenance actions and accessory usage status of the work order execution timeline entries, calculates the time field mapping correction value, writes the node information into the record table after checking the synchronization status between fields, and updates the panel display status to the current node to obtain a snapshot of the endoscope maintenance process record.

8. The cloud-based endoscope maintenance management system according to claim 7, characterized in that: The time field mapping correction value adopts the formula: Where T represents the time field mapping correction value, t ij represents the original time value of the jth unit in the i-th node, w ij Represents the time weight factor of the jth unit of the i-th node, m ij Represents the usage quantity of the material field in the j-th unit of the i-th node, Represents the average usage quantity of the material field in all units of the i-th node, s ij Represents the progress status code value of the jth unit in the i-th node, and n represents the number of units included in the i-th node.

9. The cloud-based endoscope maintenance management system according to claim 1, characterized in that: The system also includes a data linkage module: The data linkage module reads the accessory usage details in the maintenance process record snapshot, summarizes the usage frequency and replacement interval by accessory type, binds it with the equipment number and uploads it to the cloud prediction port to generate an endoscope accessory maintenance list; The endoscope accessories maintenance list includes accessory usage trend categories, replacement cycle prediction values, and accessory life distribution models.

10. The cloud-based endoscope maintenance management system according to claim 9, characterized in that: The data linkage module includes: The accessory details arrangement submodule reads the accessory usage details in the maintenance process record snapshot, summarizes the replacement frequency and replacement time interval by accessory type, binds the corresponding equipment number, and generates an endoscope accessory replacement record table; The maintenance list generation submodule matches the cloud prediction port configuration item and writes the prediction field according to the endoscope accessories replacement record table, identifies the accessory task items of the device group, and generates an endoscope accessories maintenance list.

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