Logistics management data processing method, system and equipment

Abstract

The invention is suitable for the technical field of data processing, and particularly relates to a logistics management data processing method, system and equipment, and the method comprises the steps: continuously receiving state data sent by public equipment in a school according to a time requirement; analyzing the continuously received state data of the public equipment to obtain analysis data corresponding to the public equipment; wherein the analysis data is used for indicating the workable coefficient of the public equipment; determining abnormal data according to the workable coefficient indicated by the analysis data; wherein the abnormal data is used for indicating the public equipment with an abnormal condition; logistics self-maintenance data is generated based on the abnormal data; wherein the logistics self-maintenance data is used for indicating a processing scheme obtained after corresponding processing is carried out on the public equipment with the abnormal condition. According to the logistics management data processing method provided by the invention, the situation that logistics service depends on manual inspection is reduced, and the situation that response is lagged and logistics work arrangement cannot be processed in time is prevented.

Description

Technical Field

[0001] This application belongs to the field of data processing technology, and in particular relates to a logistics management data processing method, system and equipment. Background Technology

[0002] Data processing refers to a series of operations, including processing, organizing, analyzing, and storing, collected data to transform it into valuable information. The transformation from raw data to usable information involves multiple stages and technical methods.

[0003] In related technologies, companies and enterprises have management departments, while schools have logistics management departments. The logistics management departments of schools are mainly responsible for scheduling, approval, attendance, file management, campus security (such as management of visitors and fire hazards), and logistics services (such as repair requests and energy consumption management). Logistics services are the focus of school logistics management. Logistics services usually rely on manual inspections, which leads to delayed response and the inability to handle logistics work arrangements in a timely manner. Summary of the Invention

[0004] This application provides a logistics management data processing method, system, and device that can solve the problem of delayed response caused by logistics services typically relying on manual inspections.

[0005] In a first aspect, embodiments of this application provide a logistics management data processing method, including: The system continuously receives status data sent by public equipment within the school according to time requirements; wherein the status data is used to indicate the operating status of the public equipment within the school, the time requirements are used to indicate a preset duration, and the public equipment includes at least one of laboratory equipment, campus alarm terminal, multimedia equipment, smart street light, smart water pipe and smart air conditioner; The continuously received status data of the public device is analyzed to obtain analysis data corresponding to the public device; wherein, the analysis data is used to indicate the workability coefficient of the public device; Abnormal data is determined based on the workability coefficient indicated by the analyzed data; wherein, the abnormal data is used to indicate the public equipment where an abnormal situation has occurred; Logistics self-maintenance data is generated based on the abnormal data; wherein, the logistics self-maintenance data is used to indicate the processing scheme obtained by processing the corresponding public equipment.

[0006] The logistics management data processing method provided in this application continuously receives status data sent by public equipment within the school according to time requirements. It analyzes this continuously received status data to obtain corresponding analytical data for each public equipment. This improves the efficiency and accuracy of monitoring the operational status of public equipment, enabling timely detection and handling of anomalies. By identifying anomalies based on the workability coefficient indicated by the analytical data, it can accurately locate faulty public equipment, reducing unnecessary interference with normally operating equipment. Furthermore, it generates logistics self-maintenance data based on the anomaly data, improving the efficiency and focus of logistics maintenance, reducing reliance on manual inspections for logistics services, and preventing delayed responses and untimely handling of logistics work arrangements.

[0007] Secondly, embodiments of this application provide a logistics management data processing system, including: A receiving unit is used to continuously receive status data sent by public equipment within the school according to time requirements; wherein, the status data is used to indicate the operating status of the public equipment within the school, the time requirements are used to indicate a preset duration, and the public equipment includes at least one of laboratory equipment, campus alarm terminal, multimedia equipment, smart street light, smart water pipe and smart air conditioner; An analysis unit is configured to analyze the continuously received status data of the public device to obtain analysis data corresponding to the public device; wherein the analysis data is used to indicate the workability coefficient of the public device; A determining unit is configured to determine abnormal data based on the workability coefficient indicated by the analyzed data; wherein the abnormal data is used to indicate the common equipment where an abnormal situation has occurred; A generation unit is used to generate logistics self-maintenance data based on the abnormal data; wherein the logistics self-maintenance data is used to indicate the processing scheme obtained by processing the corresponding public equipment.

[0008] Thirdly, embodiments of this application provide a logistics management data processing device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the method as described in any of the first aspects above.

[0009] Fourthly, embodiments of this application provide a computer program product that, when run on a logistics management data processing device, causes the logistics management data processing device to execute the logistics management data processing method described in any one of the first aspects.

[0010] It is understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a flowchart illustrating a logistics management data processing method provided in an embodiment of this application; Figure 2 This is a schematic diagram of the implementation flow of step S300 in the logistics management data processing method provided in an embodiment of this application; Figure 3 This is a schematic diagram of the implementation process of step S200 in the logistics management data processing method provided in an embodiment of this application; Figure 4 This is a schematic diagram of the implementation flow of step S240 in the logistics management data processing method provided in an embodiment of this application; Figure 5 This is a flowchart illustrating steps S3301 to S3305 in a logistics management data processing method provided in an embodiment of this application. Figure 6 This is a schematic diagram of the structure of the logistics management data processing system provided in the embodiments of this application; Figure 7 This is a schematic diagram of the structure of the logistics management data processing device provided in the embodiments of this application. Detailed Implementation

[0013] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0014] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0015] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0016] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0017] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0018] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0019] In related technical fields, various companies and enterprises have dedicated management departments to ensure the smooth operation and management of their daily operations. Similarly, schools also establish corresponding logistics management departments. Specifically, the logistics management departments in schools mainly shoulder a number of key responsibilities, including but not limited to course scheduling and scheduling, approval processes for various matters, verification and management of student and faculty attendance records, organization and preservation of student and faculty files, comprehensive supervision of campus safety affairs (such as strict control of outsiders, investigation and prevention of fire hazards), and comprehensive support for logistics services (such as timely reporting of damage to various school equipment, effective management of campus energy consumption, etc.). As the most important aspect of school logistics management, the quality and efficiency of logistics services directly affect the normal teaching order and the daily lives of teachers and students. However, in practice, logistics services often rely on manual, regular or irregular inspections of equipment. This method inevitably leads to a lag in response speed, making it impossible to handle logistics work arrangements in a timely and effective manner when encountering emergencies, thus affecting the smoothness and efficiency of the overall operation of the school to a certain extent.

[0020] To address the aforementioned issues, this application provides a logistics management data processing method, system, and device. In this method, status data sent by public equipment within the school according to time requirements is continuously received. The continuously received status data is analyzed to obtain corresponding analytical data for each public equipment. This improves the efficiency and accuracy of monitoring the operational status of public equipment, enabling timely detection and handling of anomalies. Anomalies are identified based on the workability coefficient indicated by the analytical data, allowing for precise location of faulty public equipment and reducing unnecessary interference with normally operating equipment. Furthermore, logistics self-maintenance data is generated based on the anomaly data, improving the efficiency and targeting of logistics maintenance, reducing reliance on manual inspections for logistics services, and preventing delayed responses and untimely processing of logistics work arrangements.

[0021] The logistics management data processing method provided in this application embodiment can be applied to a logistics management data processing device. In this case, the logistics management data processing device is the executing entity of the logistics management data processing method provided in this application embodiment. This application embodiment does not impose any restrictions on the specific type of logistics management data processing device.

[0022] For example, logistics management data processing equipment can be terminal devices such as tablets, laptops, netbooks, desktop computers, smart screens, smart TVs, computers, and laptops.

[0023] To better understand the logistics management data processing method provided in the embodiments of this application, the specific implementation process of the logistics management data processing method provided in the embodiments of this application will be described by way of example below.

[0024] Figure 1 This illustration shows a schematic flowchart of a logistics management data processing method provided in an embodiment of this application. The logistics management data processing method includes: S100 continuously receives status data sent by public equipment within the school according to time requirements; wherein, the status data is used to indicate the operating status of public equipment within the school, and the time requirements are used to indicate the preset duration, and public equipment includes at least one of laboratory equipment, campus alarm terminal, multimedia equipment, smart street light, smart water pipe and smart air conditioner.

[0025] It can be understood that the operational status of public facilities within a school includes the operational status of laboratory equipment, campus alarm terminals, multimedia equipment, smart streetlights, smart water pipes, and smart air conditioners. The time requirement indicates a preset duration, which can be a fixed set duration or a different preset duration determined based on the different characteristics of the public facilities.

[0026] For example, continuously receiving status data sent by public devices within the school according to time requirements can be achieved through wireless communication technologies such as networks, Bluetooth, and infrared, or through wired connections. The receiving unit can be a standalone hardware module or part of a software module, used to receive data from various public devices in real time and without interruption.

[0027] S200 analyzes the continuously received status data of the public equipment to obtain the analysis data corresponding to the public equipment; the analysis data is used to indicate the workability coefficient of the public equipment.

[0028] As is understandable, the workability factor is an indicator that measures the operating status of public equipment. The higher the value, the more normally the equipment is operating, and vice versa, it indicates that the equipment may have a malfunction or performance degradation.

[0029] For example, the received status data of public equipment is evaluated. That is, the status data may include multiple data, each of which corresponds to a time requirement. The multiple status data are arranged in chronological order, and the status data corresponding to each time is extracted. All the received status data is evaluated. S200, parameters such as rate and temperature are evaluated. The campus alarm terminal may include data such as the number of alarm triggers and false alarm rate. That is, laboratory equipment is one type, and campus alarm terminal is another type.

[0030] For example, each status data contains a unique device identifier (ID). According to a preset "device ID-type" mapping table (e.g., device IDs starting with AC are for air conditioners, and those starting with LT are for lighting), the received data packets are sorted into different type queues. The classification method can use in-memory data structures (e.g., HashMap) or database queries to group and store data by device type. This enables structured organization of the data, facilitating subsequent targeted analysis by device type (e.g., focusing on temperature and energy consumption for air conditioners; focusing on the number of runs and fault codes for elevators), improving the efficiency and targeting of the analysis, and laying the foundation for subsequent analysis.

[0031] S230, determine time data from multiple types of status data; wherein, the time data is used to indicate the various time points at which the status data of each type is received.

[0032] For example, the timestamp field contained in each status data packet is parsed. This timestamp is generated by the device when collecting data and sent with the data packet, or is timestamped by the receiving gateway when data is received. Time information is extracted from the data packet metadata or specific fields (such as timestamp).

[0033] S240: Based on the time points indicated by the time data, simulate and analyze the status data of each public device to obtain the analysis data corresponding to each public device.

[0034] For example, for each device, its status data points (such as temperature, pressure, current, number of switching times, fault codes, etc.) over a period of time (e.g., the most recent 24 hours) are sorted by timestamps to form a time series, and then the simulated operating trajectory and health status evolution are applied.

[0035] This configuration enables dynamic assessment and prediction of equipment status, going beyond simple threshold alarms and providing a basis for preventative maintenance and precise repair.

[0036] In one possible implementation, please refer to Figure 4 S240, based on the time points indicated by the time data, simulates and analyzes the status data of each common device to obtain the analysis data corresponding to each common device, including: S241, Obtain the equipment status model of each public device; wherein, the equipment status model is a model in three-dimensional space, and the equipment status model is established based on historical status data and equipment maintenance records, and is used to evaluate the current operating status of the equipment.

[0037] For example, the equipment status model of each public device can be constructed by 3D modeling software based on the structural parameters and operating parameters of each public device. These structural parameters may include information such as the size, shape, and material of the device, while the operating parameters may cover the working principle, performance indicators, historical fault records, etc. of the device. Alternatively, a "device status model" can be pre-set or learned online for each type of device (or key device). The model maps the key status parameters of the device (such as temperature, vibration amplitude, and running time) into a three-dimensional (or multi-dimensional) space, with each dimension representing a key health indicator.

[0038] S242, evaluate the multiple status data of each public device received in sequence according to the time points indicated by the time data to obtain the device score value of each public device.

[0039] For example, the device's status data over a recent period (e.g., the past 10 data points) is input into its corresponding "device status model." The model calculates a numerical health score (e.g., 0-100, with 100 representing the best) based on the current status point's position in a preset three-dimensional space (or its distance / compliance to the center of the health region). For instance, the Euclidean distance from the current status point to the centroid of the health region is calculated and mapped to 0-100. The model then incorporates historical data trends for scoring (e.g., deducting points for a continuous downward trend) to quantify complex, multi-dimensional status data into a single, easily understood and comparable health score (device score value). This facilitates the rapid identification of problematic devices (low-scoring devices) and the quantification of device health levels.

[0040] S243, based on the equipment rating and equipment status model, calculate the workability coefficient of each public equipment to obtain analysis data; among which, the workability coefficient is used to represent the reliability of the equipment during normal working time, and the value is proportional to the reliability of the equipment.

[0041] For example, based on the device rating, a preset calculation formula is applied to calculate the probability or reliability that the device will be able to work normally in the future (e.g., next week), i.e., the workability coefficient. For example: Workability coefficient = min(100, device rating) / 100 (simple linear).

[0042] This setup transforms health scores into predictive indicators of future equipment reliability. Equipment with low availability ratings requires priority handling, while equipment with high availability ratings can have extended maintenance cycles and optimized resource allocation.

[0043] S300, determine abnormal data based on the workability coefficient indicated by the analysis data; wherein, abnormal data is used to indicate public equipment where abnormal conditions have occurred.

[0044] For example, a threshold for the operability coefficient can be set (e.g., below 70%). The system scans the analysis data of all devices, marks devices with operability coefficients below the threshold as "abnormal," and generates "abnormal data" records containing information such as device ID, location, operability coefficient, and possible fault type. Determining abnormal data can be done by comparing the operability coefficient of each common device with the preset threshold; if the operability coefficient of a common device is below the preset threshold, then the status data of that common device is determined to be abnormal data. The preset threshold can be set based on factors such as device type, historical operating data, maintenance records, and the school's requirements for device performance.

[0045] In one possible implementation, please refer to Figure 2 S300, after identifying anomalous data based on the workability factor indicated by the analysis data, includes: S310 monitors the location data of logistics management personnel in real time; the location data is used to indicate the location of logistics management personnel at the logistics management data processing equipment.

[0046] It can be understood that the location of logistics management personnel and logistics management data processing equipment can be understood as the location between logistics management personnel and logistics management data processing equipment. Real-time monitoring of the location data of logistics management personnel can specifically mean real-time monitoring of whether logistics management personnel are in a location that can be monitored by logistics management data processing equipment.

[0047] For example, image acquisition devices or RFID can be used to obtain the current location information of logistics management personnel in real time. This information can be transmitted to the system backend in real time via wireless network. The location data may include the floor, room or specific coordinates of the logistics management personnel, so that the system can accurately determine the distance between the logistics management personnel and abnormal equipment.

[0048] S320 continuously receives pending data; the pending data is used to indicate logistical matters that need to be arranged.

[0049] It is understandable that the logistical matters that need to be arranged include, but are not limited to, reports of public equipment failures, repair requests, equipment replacement needs, abnormal energy consumption alerts, and safety warning information. The data comes from public equipment in various corners of the school and is uploaded to the logistics management data processing system in real time via wireless network.

[0050] For example, continuously receiving data to be processed can be done by receiving data from various public devices within the school in real time via wireless network, wired network or other data transmission methods. The data is continuously monitored by the logistics management data processing system and the system obtains the data to be processed.

[0051] S330: Upon receiving data to be processed and when the location data indicates that the logistics management personnel are not at the location of the logistics management data processing device, a reminder data is sent; wherein, the reminder data is used to instruct or remind the logistics management personnel to process the matter on the mobile terminal or return to the location of the logistics management data processing device to process the matter.

[0052] It is understandable that the reminder data can be sent to the mobile devices of logistics management personnel (mobile devices can be mobile phones, tablets or other smartwatches, etc., and the mobile phones, tablets or other smartwatches are connected to the logistics management data processing system) via SMS, email, APP push, etc. The reminder content can include specific information about the matters to be handled, the urgency level, location information, etc.

[0053] For example, when data to be processed is generated, the current location of the target administrative personnel is immediately checked. If the location is not within the preset geofence range (e.g., a radius of 50 meters) of their logistics management data processing equipment, a reminder is sent to their mobile terminal via APP push notification, SMS, or telephone. The reminder contains brief information about the matter to be processed (e.g., air conditioning malfunction on floor XX, please handle it as soon as possible). Here, the geofence is a virtual boundary that can be dynamically adjusted in size or shape based on the location of the logistics management data processing equipment to adapt to different application scenarios and needs. The preset geofence range can be understood as a virtual range set centered on the logistics management data processing equipment to determine whether logistics management personnel are nearby, so as to quickly respond to abnormal equipment or matters to be processed.

[0054] S340 continuously calculates the reminder duration after the reminder data has been sent.

[0055] For example, the timer starts from the moment the reminder message is successfully sent. The reminder duration can be calculated by using a built-in timer module or by calling a third-party timer service to record the accumulated time since the reminder data was successfully sent.

[0056] S350 determines basic data if the reminder duration exceeds the preset duration; the basic data is used to reflect the specific information of the public equipment where the abnormal situation occurred.

[0057] It is understandable that basic data includes, but is not limited to, the name, model, location, fault type, and historical maintenance records of public equipment.

[0058] For example, determining the basic data can be done by querying a pre-set equipment information database to obtain the basic data of the public equipment corresponding to the abnormal situation. The pre-set equipment information database stores detailed information about each public device, including the device's name, model, location, manufacturer, purchase date, warranty period, historical fault records, and maintenance status.

[0059] In one possible implementation, S350 determines basic data, including: if the reminder duration exceeds a preset duration. S351 continuously receives location data and fault data of public equipment that has encountered abnormal situations; the location data is used to indicate the coordinates of the public equipment within the campus, and the fault data is used to indicate the fault code of the public equipment.

[0060] It's understandable that location data is obtained through satellite navigation systems such as GPS and BeiDou to determine the specific location of public equipment within the campus (e.g., building, floor, room number, GPS coordinates, or indoor beacon ID). Fault data, on the other hand, is detected by the sensors or detection systems built into the public equipment, reflecting the type and severity of the fault. For example, smart streetlights may experience insufficient brightness, frequent flickering, or failure to turn on; their fault codes will correspond to these specific fault symptoms. By receiving both location and fault data, the location and type of faulty equipment can be quickly pinpointed.

[0061] S352, determine the equipment ownership data based on the coordinates of the public equipment within the campus indicated by the positioning data; wherein, the equipment ownership data is used to indicate the college to which the public equipment belongs.

[0062] It is understandable that identifying the data belonging to the equipment would enable the transmission of fault information to the relevant administrative and logistical management personnel of the college.

[0063] For example, based on the coordinates of public equipment within the campus and combined with the school's Geographic Information System (GIS), the college area where the equipment is located can be determined, and the college name of that area can be extracted to identify the college to which the public equipment is responsible.

[0064] S353, determine replacement data based on the fault code indicated by the fault data; wherein, the replacement data is used to indicate the parts that need to be replaced.

[0065] Understandably, based on the fault codes indicated by the fault data, a pre-defined fault code-to-part correspondence table is used to determine the type, model, and specifications of the parts that need to be replaced. This pre-defined fault code-to-part correspondence table is built upon historical maintenance experience and information provided by the equipment manufacturer, and includes the correspondence between various fault types and potentially damaged parts, enabling quick and accurate identification of the parts that need to be replaced.

[0066] For example, based on the equipment model and specific fault code, you can look up a list of spare parts that need to be replaced for that fault (such as fault code Err05, which may correspond to a temperature sensor or compressor starting capacitor), but not limited to this.

[0067] S354, determine the equipment ownership data and replacement data as the base data.

[0068] For example, the college responsible for a public device whose data indicates an abnormality and the parts that need to be replaced according to the data indication are determined as the basic data.

[0069] This setup allows for the rapid identification of the responsible college and individual administrator, while providing logistics management personnel with comprehensive information about the malfunctioning equipment, including its location, the college to which it belongs, the type of malfunction, and the parts requiring replacement. This enables logistics management personnel to respond quickly and arrange repairs or replacements. Once the basic data is established, further automation is possible, such as automatically sending repair requests to the relevant college's administrative departments or dispatching work orders to repair personnel, along with detailed malfunction information and equipment location, to improve repair efficiency and accuracy.

[0070] S360 generates automatic processing commands based on basic data.

[0071] For example, based on information such as equipment location, affiliated college, fault type, and required replacement parts in the basic data, an automated processing command is generated, containing details of the specific maintenance task, required spare parts, maintenance personnel arrangements, and estimated completion time. This command can automatically trigger the system's internal maintenance process, such as sending work orders to maintenance personnel or requesting spare parts from the spare parts warehouse, enabling rapid allocation and execution of maintenance tasks. Simultaneously, the automated processing command can also include notifications to logistics management personnel, informing them of the progress and status of the maintenance task, allowing them to track and monitor the completion of the maintenance work.

[0072] This setup allows for automatic initiation of maintenance procedures when manual responses time out or during non-working hours, significantly reducing downtime of faulty equipment and improving the automation level and response speed of logistical support.

[0073] In one possible implementation, please refer to Figure 5 The methods also include: S3301, upon receiving data to be processed and the location data indicating that the logistics management personnel are at the location of the logistics management data processing device, generates a file to be processed for each item indicated by the received data to be processed; wherein, the file to be processed is a file generated based on the content and information of the item to be processed indicated by the data to be processed.

[0074] It is understandable that pending documents can be in the form of electronic documents, tables, etc., depending on the content and nature of the matter at hand. For example, for a public equipment malfunction report, the pending document could be an electronic document containing the equipment name, malfunction description, location information, and urgency level; for a maintenance request, the pending document could be a table containing equipment information, required spare parts, maintenance personnel arrangements, and estimated completion time. There can be multiple pending documents.

[0075] S3302 displays all files to be processed.

[0076] It is understandable that logistics management data processing equipment also includes display devices, which can be displays, connected television screens, or projectors, etc., used to display the content and information of the documents to be processed.

[0077] For example, sending each file to be processed to the logistics management data processing device can be done via a wireless network, a wired network, or other data transmission methods, and the file to be processed can be transmitted to the logistics management data processing device and displayed on the display device of the logistics management data processing device.

[0078] S3303, Monitor the clicked object; where the clicked object is the pending file clicked by the current logistics manager, and the pending file clicked by the current logistics manager is any one of all pending files.

[0079] It can be understood that monitoring the clicked object can be understood as identifying the pending file clicked by the current logistics manager, which can be any pending file displayed on the display device that is clicked by the mouse.

[0080] For example, all the pending files displayed on the display device can be arranged sequentially in chronological order or in order of severity; when a logistics manager clicks on a pending file using a mouse or other input device, the action can be captured in real time, and the clicked pending file and its related information can be recorded.

[0081] S3304, Obtain adjacent file data based on the clicked object; wherein, the adjacent file data is used to indicate the files to be processed that have the same basic data as the clicked object, and the number of adjacent file data is greater than or equal to 0.

[0082] It is understandable that a pending file with the same underlying data as the clicked object can be understood as another pending file that is the same as the college responsible for the damaged public equipment and the parts that need to be replaced, as indicated by the clicked pending file. When a user clicks on a pending file, the system searches the pending file list for other work order files with the same or highly similar basic data based on information in the basic data (such as equipment location (building or floor), equipment type, fault code, and department). Highly similar basic data can be any two identical work order files, such as those with the same building or floor, the same equipment type, or the same department. These three factors—same building or floor, same equipment type, and same department—are considered highly similar. If there are 5, 8, 10, or more factors, the work order files must have 3 or 4 identical factors (for 5 factors), 6 or 7 identical factors (for 8 factors), and 8 or 9 identical factors (for 10 factors) to be considered highly similar. The system intelligently discovers related pending tasks, such as multiple lighting faults on the same floor or identical fault codes for the same model of air conditioner. This facilitates batch processing.

[0083] S3305, Generate file processing data associated with the clicked object and adjacent file data based on the clicked object and adjacent file data; wherein, the file processing data is used to instruct maintenance personnel to repair the public equipment that is malfunctioning.

[0084] It is understandable that, based on the clicked object and adjacent file data, relevant information is integrated to generate comprehensive file processing data. The file processing data not only includes details such as the specific repair tasks, required spare parts, and repair personnel arrangements for the clicked file to be processed, but also covers the related information of other files to be processed that have the same or highly similar basic data.

[0085] For example, when a logistics manager clicks on a pending file about a lighting malfunction on a certain floor, the system can automatically search and list pending files about other lighting malfunctions on that floor, as well as pending files about lighting malfunctions on other floors or areas with the same fault code. These related pending files are then integrated to form a maintenance work order containing all relevant maintenance tasks. This maintenance work order includes information such as the specific location of each maintenance task, fault description, required spare parts, maintenance personnel arrangements, and estimated completion time. The clicked work order and the found adjacent work orders can be combined into a maintenance work order group. Based on the information of all work orders in this group (location concentration, same fault, same required spare parts), an optimized maintenance dispatch suggestion or a merged maintenance instruction can be automatically generated.

[0086] In one possible implementation, the method also includes: S3301A, upon receiving data to be processed and when the location data indicates that the logistics management personnel are not at the location of the logistics management data processing device, determines the current time data; wherein, the time data is used to indicate specific time information.

[0087] It is understandable that the time information indicated by the time data includes, but is not limited to, specific time information such as date, hour, and minute.

[0088] For example, determining the current time data can be achieved by obtaining the current time information through an internal clock. The internal clock can be a clock module built into the logistics management data processing system, or it can be a clock service synchronized with a network time server.

[0089] S3302A determines whether the current time is within a working period based on the time information indicated by the time data.

[0090] For example, it is determined whether the current time falls within a working period on a weekday based on a preset work schedule. The preset work schedule can be set based on the school's daily work hours, such as 8:00 AM to 5:00 PM from Monday to Friday; the determination of whether the current time falls within a working period is made by comparing time ranges.

[0091] S3303A: If the current time is outside of a working period, generate an automatic processing command.

[0092] It is understandable that during off-peak hours, logistics management personnel may not be able to respond to pending matters in a timely manner. Therefore, an automated processing command is generated that includes specific maintenance tasks, required spare parts, maintenance personnel arrangements, and estimated completion time. This command can automatically trigger the internal maintenance process of the system, such as sending work orders to maintenance personnel or requesting spare parts to be issued from the spare parts warehouse, so that maintenance work can be carried out in a timely manner.

[0093] For example, if it is determined that the current time is non-working time (such as evening, weekend, or holiday), the steps of waiting for manual response (steps S330-S340) and timeout judgment (step S350) are skipped, and an automatic processing command is directly generated based on the data to be processed (usually urgent abnormal equipment data), such as automatically dispatching the order to the on-duty maintenance personnel or triggering an alarm.

[0094] This setup allows for timely and effective handling of maintenance work even during off-peak hours, improving the automation level and response speed of logistical support.

[0095] In one possible implementation, S3305, file processing data associated with the clicked object and adjacent file data is generated based on the clicked object and adjacent file data, including: S33051 compares the pending items indicated by the clicked object with the pending items indicated by adjacent file data to determine whether there are the same equipment failures or maintenance needs.

[0096] It is understandable that the pending items indicated by the clicked object are compared one by one with the pending items indicated by adjacent file data to determine whether they have the same equipment fault type, fault code, required replacement parts, or other maintenance needs. For example, if two pending files report insufficient brightness of the lighting equipment on the same floor and have the same fault code, then these two pending items are considered to have the same equipment fault or maintenance need.

[0097] For example, by comparing information such as fault description, fault code, and required spare parts, it is possible to intelligently identify pending items with the same or similar fault characteristics and classify them into the same maintenance task group.

[0098] S33052 If there are the same equipment failures or maintenance needs, the clicked object and adjacent file data will be classified into the same batch of processing files.

[0099] For example, the clicked object and adjacent file data can be grouped into the same batch of processing files by integrating them into a maintenance task group and assigning the group a unique identifier or number. This allows multiple related maintenance tasks to be processed at once, without having to process each individual file individually, reducing the complexity and repetition of the operation.

[0100] S33053, Obtain all maintenance data; whereby maintenance data is used to indicate the skill type and current workload of maintenance personnel.

[0101] For example, all maintenance data can be obtained by querying maintenance personnel information in real time, including their skill types (e.g., electricians, plumbers, etc.) and current workload (e.g., the number of maintenance tasks being processed, estimated completion time, etc.). The method of obtaining this data can be through internal database queries, real-time communication interfaces, or manual input. Maintenance data is dynamic and therefore needs to be updated in real time or periodically to improve the accuracy and timeliness of the information.

[0102] S33054, determine document processing data based on basic data and maintenance data.

[0103] For example, based on basic data (including equipment location, fault type, required spare parts, etc.) and maintenance data (including maintenance personnel's skill type and current workload), maintenance personnel are assigned to the same batch of processed documents. The allocation of maintenance tasks is optimized by considering factors such as whether the maintenance personnel's professional skills match the fault type, whether the maintenance personnel's current workload allows them to take on new maintenance tasks, and the distance between the maintenance personnel's location and the location of the faulty equipment. At the same time, the generated document processing data also includes information such as specific maintenance task arrangements, required spare parts lists, maintenance personnel contact information, and estimated completion time.

[0104] This setup enables intelligent and optimized dispatching of maintenance tasks, maximizes the utilization rate and response speed of maintenance resources, and improves the efficiency of logistics management.

[0105] In one possible implementation, S33054, after determining the document processing data based on the basic data and maintenance data, includes: S330541, Based on the basic data, generate a maintenance requirement matrix; wherein, the maintenance requirement matrix includes row vectors representing the equipment to be maintained, and column vectors including equipment location coordinates, affiliated college, fault level, required parts, and teaching relevance.

[0106] For example, the teaching relevance can represent the degree of impact of the equipment to be repaired on teaching activities. For instance, if the equipment is a projector in the classroom, the teaching relevance is high; if the equipment is a printer in the office, the teaching relevance is low. Given limited repair resources, priority can be given to repairing equipment that has a greater impact on teaching. The repair demand matrix can be generated based on the system's internal basic data, forming a structured data matrix.

[0107] S330542, obtain the current teaching calendar data, and calculate the teaching correlation weight coefficient of each device based on the current teaching calendar data.

[0108] It is understandable that academic calendar data includes information such as the school's teaching plans, course schedules, and exam dates, in order to reflect the importance and urgency of teaching activities at different times.

[0109] For example, based on the current teaching calendar data, the teaching relevance weight coefficient of each device is calculated, which is to assess the importance of the device to be repaired to the teaching activities in the current time period. For example, if the device is located in a classroom or laboratory where classes are being held and the failure affects the current teaching, the weight coefficient is 1.5; if the device is located in a place where teaching is scheduled in the next 24 hours, the weight coefficient is 1.2; and the weight coefficient for other scenarios is 1.0, etc., but not limited to these.

[0110] S330543, Constructing a dynamic vector of maintenance resources based on the integration of maintenance data with teaching correlation weight coefficients.

[0111] It is understandable that constructing a dynamic vector of maintenance resources involves matching each piece of equipment in the maintenance demand matrix with maintenance data such as the skill type, location information, and workload of current maintenance personnel, and combining this with a teaching relevance weighting coefficient to form a dynamic maintenance resource allocation scheme. The dynamic vector of maintenance resources not only includes the real-time status of maintenance personnel but also the urgency of maintenance needs in relation to teaching activities.

[0112] For example, the skill matching degree of maintenance personnel is the intersection of the personnel skill list and the required component repair skills; the path optimization coefficient = 1 / (Euclidean distance from the current location of the personnel to the location of the equipment); the load balancing factor = 1 / (number of pending work orders + 1). The path optimization coefficient is used to measure the ease with which maintenance personnel can reach the location of the equipment to be repaired; the load balancing factor is used to assess the current workload of maintenance personnel in order to reduce the decline in maintenance efficiency caused by over-distribution of tasks.

[0113] S330544: Calculate the overall dispatch priority according to the dynamic vector of maintenance resources using a formula, and sort the files to be processed in the same batch in descending order of overall dispatch priority to generate a maintenance path navigation sequence.

[0114] It is understandable that the formula for calculating the overall dispatch priority can be: Priority P = (Fault level × 0.4 + Teaching relevance weight × 0.3 + Skill matching degree × 0.2 + Path optimization coefficient × 0.1) × Load balancing factor; The maintenance path navigation sequence is generated by sorting the files to be processed in the same batch in descending order according to the comprehensive work assignment priority based on the calculation results of the maintenance resource dynamic vector. The sequence can guide maintenance personnel to the location of each piece of equipment to be maintained in the optimal order.

[0115] S330545: When the college to which the device belongs is involved in cross-departmental collaboration, it automatically generates college collaboration instructions.

[0116] It is understandable that college collaboration instructions are used to guide different colleges in collaboratively completing maintenance tasks. When the equipment to be maintained is located at the intersection of multiple colleges, or when the fault involves equipment shared by multiple colleges, cooperation between different colleges is required to complete the maintenance task. College collaboration instructions can clarify the maintenance responsibilities, task allocation, and coordination mechanisms of each college, so as to ensure that the maintenance work can proceed smoothly.

[0117] For example, the college collaboration instructions may include information such as the description of the maintenance task, the responsible college, the list of maintenance personnel, the maintenance time node, the coordinator and contact information. By automatically generating college collaboration instructions, the cost and time of manual communication can be reduced, and the execution efficiency of maintenance tasks can be improved.

[0118] With this setup, the traditional solution only dispatches work based on the severity of the fault, lacking consideration for the real-time impact on teaching activities. However, the above steps can solve the unique problems of teaching priority conflicts and inefficient cross-college coordination in campus administration. By calculating maintenance paths with dynamic weights, maintenance response time can be shortened.

[0119] S400 generates logistics self-maintenance data based on abnormal data; wherein, the logistics self-maintenance data is used to indicate the processing scheme obtained by the corresponding processing of public equipment.

[0120] It is understandable that logistics self-maintenance data is a processing plan generated based on data and information during equipment failure handling. This plan includes the malfunctioning public equipment, the responsible college, the assigned maintenance personnel within that college, the parts that need to be replaced, and the time required for maintenance. Logistics self-maintenance data can include basic information about the faulty equipment (e.g., equipment name, model, installation location), fault type and description, handling process and measures taken (e.g., replacing parts, adjusting parameters), maintenance personnel information, and maintenance time.

[0121] This setup allows for the recording of data throughout the entire process of public equipment maintenance, from fault reporting to repair completion. This facilitates subsequent data analysis and experience summarization, thereby optimizing logistics management processes. For example, by analyzing self-maintenance data, the types or locations of frequently malfunctioning equipment can be identified, enabling the implementation of corresponding preventative measures, such as increased inspections and timely replacement of vulnerable parts. This reduces the occurrence of equipment failures, allows for faster fault location and resolution, and provides precise location of malfunctioning public equipment, minimizing interference with normally operating equipment. This improves the efficiency and focus of logistics maintenance, reduces reliance on manual inspections for logistics services, and prevents delayed responses and untimely handling of logistics work arrangements.

[0122] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0123] Corresponding to the logistics management data processing method described in the above embodiments, this application also provides a logistics management data processing system, in which each unit can implement each step of the logistics management data processing method. Figure 6A structural block diagram of the logistics management data processing system provided in the embodiments of this application is shown. For ease of explanation, only the parts related to the embodiments of this application are shown.

[0124] Reference Figure 6 The logistics management data processing system includes: The receiving unit is used to continuously receive status data sent by public equipment within the school according to time requirements; wherein, the status data is used to indicate the operating status of the public equipment within the school, and the time requirements are used to indicate the preset duration, and the public equipment includes at least one of laboratory equipment, campus alarm terminal, multimedia equipment, smart street light, smart water pipe and smart air conditioner; The analysis unit is used to analyze the continuously received status data of the public equipment to obtain the analysis data corresponding to the public equipment; wherein, the analysis data is used to indicate the workability coefficient of the public equipment; The determination unit is used to determine abnormal data based on the workability coefficient indicated by the analysis data; wherein, the abnormal data is used to indicate the common equipment where an abnormal situation has occurred; The generation unit is used to generate logistics self-maintenance data based on abnormal data; wherein, the logistics self-maintenance data is used to indicate the processing scheme obtained by corresponding processing of public equipment.

[0125] It should be noted that the information interaction and execution process between the above systems / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0126] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0127] This application also provides a logistics management data processing device. Figure 7 This is a schematic diagram of the structure of a logistics management data processing device provided in one embodiment of this application. Figure 7As shown, the logistics management data processing device 6 of this embodiment includes: at least one processor 60 ( Figure 7 Only one is shown in the image), at least one memory 61 ( Figure 7 (Only one is shown in the image) and a computer program 62 stored in the at least one memory 61 and executable on the at least one processor 60, wherein when the processor 60 executes the computer program 62, it causes the logistics management data processing device 6 to implement the steps in any of the above-described logistics management data processing method embodiments, or causes the logistics management data processing device 6 to implement the functions of each module / unit in the above-described system embodiments.

[0128] For example, the computer program 62 may be divided into one or more modules / units, which are stored in the memory 61 and executed by the processor 60 to complete this application. The one or more modules / units may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program 62 in the logistics management data processing device 6.

[0129] The logistics management data processing device 6 can be a desktop computer, laptop, or other computing device. This logistics management data processing device may include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art will understand that... Figure 7 This is merely an example of the logistics management data processing device 6 and does not constitute a limitation on the logistics management data processing device 6. It may include more or fewer components than shown in the figure, or combine certain components, or different components, such as input / output devices, network access devices, buses, etc.

[0130] The processor 60 can be a Central Processing Unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0131] In some embodiments, the memory 61 may be an internal storage unit of the logistics management data processing device 6, such as a hard disk or memory of the logistics management data processing device 6. In other embodiments, the memory 61 may be an external storage device of the logistics management data processing device 6, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the logistics management data processing device 6. Further, the memory 61 may include both internal storage units and external storage devices of the logistics management data processing device 6. The memory 61 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory 61 can also be used to temporarily store data that has been output or will be output.

[0132] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in any of the above method embodiments.

[0133] This application provides a computer program product that, when run on a logistics management data processing device, enables the logistics management data processing device to implement the steps in any of the above method embodiments.

[0134] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a logistics management data processing device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, such as a USB flash drive, a portable hard drive, a magnetic disk, or an optical disk.

[0135] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0136] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0137] In the embodiments provided in this application, it should be understood that the disclosed logistics management data processing system, device, and method can be implemented in other ways. For example, the logistics management data processing system and device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0138] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0139] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for processing logistics management data, characterized in that, include: The system continuously receives status data sent by public equipment within the school according to time requirements; wherein the status data is used to indicate the operating status of the public equipment within the school, the time requirements are used to indicate a preset duration, and the public equipment includes at least one of laboratory equipment, campus alarm terminal, multimedia equipment, smart street light, smart water pipe and smart air conditioner; The continuously received status data of the public device is analyzed to obtain analysis data corresponding to the public device; wherein, the analysis data is used to indicate the workability coefficient of the public device; Abnormal data is determined based on the workability coefficient indicated by the analyzed data; wherein, the abnormal data is used to indicate the public equipment where an abnormal situation has occurred; Logistics self-maintenance data is generated based on the abnormal data; wherein, the logistics self-maintenance data is used to indicate the processing scheme obtained by processing the corresponding public equipment.

2. The logistics management data processing method as described in claim 1, characterized in that, After determining the abnormal data based on the workability coefficient indicated by the analyzed data, the method further includes: Real-time monitoring of the location data of logistics management personnel; wherein, the location data is used to indicate the location of the logistics management personnel at the logistics management data processing equipment; Continuously receive data to be processed; wherein the data to be processed is used to indicate logistical matters that need to be arranged; Upon receiving the data to be processed, and if the location data indicates that the logistics management personnel are not at the location of the logistics management data processing device, a reminder data is sent; wherein, the reminder data is used to instruct or remind the logistics management personnel to handle the matter on the mobile terminal or return to the location of the logistics management data processing device to handle the matter. The reminder duration is continuously calculated after the reminder data is sent. If the reminder duration exceeds a preset duration, basic data is determined; wherein, the basic data is used to reflect the specific information of the public equipment where the abnormal situation occurred; Based on the aforementioned basic data, automatic processing commands are generated.

3. The logistics management data processing method as described in claim 1, characterized in that, The step of analyzing the continuously received status data of the public device to obtain the analysis data corresponding to the public device includes: It is confirmed that all the aforementioned status data has been received; All the status data are classified according to device type to obtain multiple status data for each type; wherein, one type corresponds to one type of common device; Time data is determined from a plurality of state data of each of the aforementioned types; wherein the time data is used to indicate a particular point in time when the state data of each of the aforementioned types is received; The status data of each of the public devices are simulated and analyzed according to the time point indicated by the time data to obtain the analysis data corresponding to each of the public devices.

4. The logistics management data processing method as described in claim 3, characterized in that, The step of simulating and analyzing the status data of each of the public devices according to the time point indicated by the time data to obtain the analysis data corresponding to each of the public devices includes: Obtain the equipment status model of each of the aforementioned public devices; wherein, the equipment status model is a model in three-dimensional space, and the equipment status model is established based on historical status data and equipment maintenance records, and is used to evaluate the current operating status of the equipment; The multiple status data of each of the public devices are evaluated sequentially according to the time points indicated by the time data to obtain the device score value of each public device; Based on the equipment rating and the equipment status model, the workability coefficient of each of the common devices is calculated to obtain the analysis data; wherein, the workability coefficient is used to represent the reliability of the equipment during normal working time, and the value is proportional to the reliability of the equipment.

5. The logistics management data processing method as described in claim 2, characterized in that, When the reminder duration exceeds a preset duration, the basic data is determined, including: The system continuously receives location data and fault data of the public equipment that is experiencing abnormal conditions; wherein, the location data is used to indicate the coordinates of the public equipment within the campus, and the fault data is used to indicate the fault code of the public equipment; The location data indicates the coordinates of the public equipment within the campus, and the equipment belongs to a specific college or department. Replacement data is determined based on the fault code indicated by the fault data; wherein, the replacement data is used to indicate the component that needs to be replaced; The data belonging to the device and the replacement data are determined as the basic data.

6. The logistics management data processing method as described in claim 2, characterized in that, The method further includes: Upon receiving the data to be processed, and with the location data indicating the location of the logistics management personnel at the logistics management data processing device, the logistics management personnel generate a file for each item to be processed indicated by the received data to be processed; wherein, the file for processing is a file generated based on the content and information of the item to be processed indicated by the data to be processed. Display each of the aforementioned files to be processed; Monitor the clicked object; wherein the clicked object is the pending file clicked by the current logistics manager, and the pending file clicked by the current logistics manager is any one of all the pending files; The adjacent file data is obtained based on the clicked object; wherein, the adjacent file data is used to indicate the files to be processed that are the same as the basic data of the clicked object, and the number of adjacent file data is greater than or equal to 0; Based on the clicked object and the adjacent file data, file processing data is generated that is associated with the clicked object and the adjacent file data; wherein, the file processing data is used to instruct maintenance personnel to repair the public equipment that is malfunctioning.

7. The logistics management data processing method as described in claim 6, characterized in that, The step of generating file processing data associated with the clicked object and the adjacent file data based on the clicked object and the adjacent file data includes: The pending items indicated by the clicked object are compared with the pending items indicated by the adjacent file data to determine whether there are the same equipment failures or maintenance needs. If the same equipment failure or maintenance requirement exists, the clicked object and the adjacent file data will be classified into the same batch of processed files; Acquire all maintenance data; wherein the maintenance data is used to indicate the skill type and current workload of maintenance personnel; The file processing data is determined based on the basic data and the maintenance data.

8. The logistics management data processing method as described in claim 2, characterized in that, The method further includes: Upon receiving the data to be processed, and if the location data indicates that the logistics management personnel are not at the location of the logistics management data processing device, the current time data is determined; wherein, the time data is used to indicate specific time information; Determine whether the current time is a working period based on the time information indicated by the time data; If the current time is outside of a working period, the automatic processing command is generated.

9. The logistics management data processing method as described in claim 7, characterized in that, After determining the file processing data based on the basic data and the maintenance data, the process includes: Based on the aforementioned basic data, a maintenance requirement matrix is ​​generated; wherein, the maintenance requirement matrix includes row vectors representing the equipment to be maintained, and column vectors including equipment location coordinates, affiliated college, fault level, required parts, and teaching relevance. Obtain the current teaching calendar data, and calculate the teaching relevance weight coefficient of each device based on the current teaching calendar data; A dynamic vector of maintenance resources is constructed by integrating the maintenance data with the teaching relevance weight coefficients. The overall dispatch priority is calculated according to the formula based on the dynamic vector of maintenance resources, and the files to be processed in the same batch are sorted in descending order according to the overall dispatch priority to generate a maintenance path navigation sequence. When the college to which the device belongs is involved in cross-departmental collaboration, college collaboration instructions are automatically generated.

10. A logistics management data processing device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 9.

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