Industrial internet platform factory inspection application system for home elevator

Abstract

The invention relates to the technical field of industrial internet platform factory inspection, and discloses an industrial internet platform factory inspection application system for a home elevator, which comprises a mobile terminal and an industrial internet platform server which are in communication connection with each other, wherein the industrial internet platform server is configured to generate an electronic work order and push the electronic work order to a mobile terminal of a corresponding user based on user identity information of an inspector and inspection task distribution information so as to replace a traditional paper work order; the mobile terminal is configured to collect inspection data of the home elevator. The electronic work order is generated and pushed to the mobile terminal through the industrial internet platform server, a traditional paper work order is completely replaced, automatic distribution and receiving of the work order are achieved, inspection personnel do not need to manually carry, search or fill in paper receipts, the system automatically matches inspection specifications and task information, the work order processing period is greatly shortened, and the work order processing efficiency is improved. And the overall inspection efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of industrial internet platform factory inspection technology, specifically to an industrial internet platform factory inspection application system for home elevators. Background Technology

[0002] With the acceleration of urbanization and the increasing demands for quality of life, home elevators are being used more and more widely in modern residences, especially villas and duplexes. Unlike commercial elevators, home elevators are characterized by a high degree of customization, complex installation scenarios, and a non-professional user base. This places higher demands on product quality, safety, reliability, and after-sales service. The factory inspection process before the elevator leaves the factory is a crucial step in ensuring that product quality meets design and safety standards.

[0003] Currently, the factory inspection process of home elevator manufacturers generally relies on the traditional model. Inspectors typically carry paper work orders and manually check and record each item according to the product model and inspection specifications. Test data, such as operating noise, leveling accuracy, door operator force, and safety component action values, are mostly read manually by observing instruments or simple tools and then manually entered into forms. This model is inefficient and prone to errors. Manual recording is slow, and in a large amount of repetitive work, problems such as data misfilling, missed inspections, or lost documents are very likely to occur, affecting the accuracy of inspection. Secondly, the data is isolated and difficult to trace, making it difficult to conduct systematic data archiving, statistics, and analysis. When quality disputes arise or recalls are required after elevator delivery, tracing the original factory inspection records of specific batches or specific components is extremely difficult, and the determination of responsibility is unclear. Based on this, this invention designs an industrial internet platform factory inspection application system for home elevators to solve the above problems. Summary of the Invention

[0004] The purpose of this invention is to provide an industrial internet platform factory inspection application system for home elevators, which solves the problems of low efficiency and data silos in the background technology.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] An industrial internet platform factory inspection application system for home elevators includes mobile terminals and an industrial internet platform server that are interconnected, wherein:

[0007] The industrial internet platform server is configured to generate electronic work orders based on the user identity information and inspection task allocation information of the inspection personnel, and push the electronic work orders to the mobile terminals of the corresponding users to replace traditional paper work orders and improve work order processing efficiency.

[0008] The mobile terminal is configured to collect inspection data of the home elevator and upload the inspection data to the industrial internet platform server in real time. The inspection data includes operating noise, leveling accuracy and door operator force, realizing automatic data collection and real-time transmission, avoiding errors in manual recording.

[0009] The industrial internet platform server is also configured to: perform multi-dimensional verification and logical validation on the received inspection data, and automatically match the corresponding inspection standards and historical data to solve the problem of data isolation; and generate a digital inspection report based on the verification results, and store the digital inspection report in a cloud database to support full lifecycle data traceability and statistical analysis, thereby overcoming the shortcomings of data traceability difficulties.

[0010] Preferably, the industrial internet platform server is specifically configured as follows when generating and pushing electronic work orders:

[0011] Determine the job type and authority level of the inspection personnel;

[0012] Based on the type of inspection task and the model of the home elevator, the corresponding inspection specifications are extracted from the database;

[0013] The push priority of the electronic work order is calculated based on the following formula:

[0014] ;

[0015] Wherein, P is the level weight corresponding to the user's permission level, ranging from 1 to 3, with a higher value indicating more experienced users; W is the urgency weight determined based on the task deadline, ranging from 1 to 5, with a higher value indicating a tighter task deadline; S is the score automatically generated by the system based on the task complexity; and D is the total number of work orders pending processing in the current system.

[0016] Based on the calculated push priority, electronic work orders with higher priority are pushed to the corresponding inspection personnel first.

[0017] Preferably, the mobile terminal is specifically configured as follows when collecting test data:

[0018] The audio sensor is activated to collect sound signals during the operation of the home elevator in order to obtain operating noise data.

[0019] The leveling accuracy data of the elevator car is determined by a high-precision tilt sensor;

[0020] Elevator door operator force data is measured using a tension sensor;

[0021] The leveling accuracy can be preliminarily judged using the following formula:

[0022] ;

[0023] Where A is the actual measured leveling value, B is the preset standard leveling value, and T is the allowable error tolerance.

[0024] Preferably, when the industrial internet platform server performs multi-dimensional verification and logical checks on the inspection data and automatically matches the corresponding inspection standards and historical data, it is further configured as follows:

[0025] The currently received test dataset C is compared with the historical standard dataset H retrieved based on the elevator model. The historical standard dataset H contains a large amount of historical test data under statistical control.

[0026] The deviation index DI of the current dataset C relative to the historical standard dataset H is calculated based on the following composite algorithm:

[0027] ;

[0028] For the i-th key test item: μ Ci μ is the average of the measurements for this item in the current dataset C. Hi σ is the long-term average of the measurements for this item in the historical standard dataset H; Hi is the standard deviation of the measured values ​​of this item in the historical standard dataset H, representing its normal fluctuation range; k is a preset sensitivity coefficient, dynamically set based on the safety risk level, used to adjust the severity of the warning;

[0029] If the calculated deviation index DI is greater than the preset threshold, it is determined that the current test data deviates significantly from the historical standard, and an anomaly warning is triggered.

[0030] Preferably, the industrial internet platform server is further configured to perform data verification as follows:

[0031] Consistency verification is performed on multiple consecutively collected test data, including calculating the standard deviation σ of multiple measurements of the same test item:

[0032] ;

[0033] Where, x i denoted as a single measurement, μ as the arithmetic mean of multiple measurements, and n as the number of measurements.

[0034] If the standard deviation σ exceeds the allowable fluctuation threshold obtained based on historical data statistics, the data set is determined to have abnormal dispersion.

[0035] Preferably, the industrial internet platform server is specifically configured as follows when generating a digital inspection report:

[0036] In addition to filling in the test results, timestamps, operator identification, and equipment ID information, the report template also automatically marks the data validity identifier and consistency verification results;

[0037] The pass rate Q is calculated by introducing a weighting factor CF based on task complexity. The calculation formula is as follows:

[0038] ;

[0039] Among them, S k Let T be the number of passes for the k-th complexity task. k Let be the total number of checks for the k-th complexity task, CF k For the k-th type of complexity task;

[0040] The generated report is encrypted using an asymmetric encryption algorithm and then stored in a cloud database.

[0041] Preferably, the industrial internet platform server is further configured to store reports as follows:

[0042] Assign a unique, universally recognized identifier to each digital inspection report;

[0043] Set data lifecycle management rules to specify access permissions for report data at different time stages;

[0044] Regularly generate statistical analysis reports based on data from the database, and support cross-departmental data sharing and viewing based on permissions.

[0045] Preferably, the industrial internet platform server is further configured to:

[0046] The identity authentication module reads the employee's IC card information or scans a QR code for identity authentication;

[0047] Based on the product batch information of the home elevators and combined with the maintenance task allocation system, a corresponding list of inspection items is generated.

[0048] Based on the type of home elevator, installation location information, and maintenance cycle, the optimal inspection path plan is generated;

[0049] The optimal time to push a work order is calculated using the following formula:

[0050] ;

[0051] Wherein, BT is the calculated optimal work order push time, CT is the current system time, Dif is the task difficulty coefficient based on the elevator model, historical maintenance records and the complexity of the inspection items, and its value ranges from 1 to 5. The larger the value, the more complex the task. K is the preset time coefficient, which is used to convert the difficulty coefficient into a time offset.

[0052] Preferably, the user identity information is identified through a face recognition module integrated into the mobile terminal;

[0053] The industrial internet platform server is also configured to: intelligently schedule work orders by combining the real-time geographical location information of inspection personnel and the current workload distribution, in order to optimize the allocation of human resources;

[0054] The real-time workload of inspection personnel is assessed using the following formula:

[0055] ;

[0056] Where Load is the assessed workload value, N is the total number of pending work orders currently assigned to the inspector, AD is the average time required to process a single work order based on historical data, and AT is the ratio of the inspector's planned working time to the actual available working time in the current scheduling cycle.

[0057] When the load of an inspector exceeds a preset threshold, the work order will be automatically redistributed or rescheduled.

[0058] Preferably, the mobile terminal is further configured to:

[0059] Sensor signals are acquired at a fixed sampling frequency;

[0060] The collected operating noise signal is subjected to Fourier transform to extract the sound pressure level at a specific center frequency;

[0061] The displacement data collected by the inertial navigation module is processed by Kalman filtering to obtain accurate leveling accuracy data.

[0062] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0063] 1. This invention generates and pushes electronic work orders to mobile terminals via an industrial internet platform server, completely replacing traditional paper work orders and achieving automatic work order allocation and reception. Inspection personnel no longer need to manually carry, search for, or fill out paper documents; the system automatically matches inspection specifications and task information, significantly shortening the work order processing cycle and improving overall inspection efficiency.

[0064] 2. This invention utilizes multiple sensors integrated into a mobile terminal and uploads data to a server in real time, eliminating potential data errors, omissions, or typos that may occur during manual reading, recording, and transcription, thus greatly improving data accuracy and reliability. The system performs multi-dimensional verification and logical consistency checks on the received test data and automatically compares it with historical standard data to identify abnormal trends. By establishing correlations between data, it solves the problems of data isolation and inability to cross-validate in traditional models.

[0065] 3. This invention automatically generates digital inspection reports with unique identification codes and stores them encrypted in a cloud database. Combined with a data lifecycle management mechanism, it achieves complete data chain management for each elevator from factory inspection to subsequent traceability. When quality disputes arise or recalls are necessary, the original inspection records can be quickly and accurately located and retrieved to clarify responsibility. Furthermore, the system intelligently schedules and dynamically allocates work orders based on the real-time location, workload, and task complexity of inspection personnel, avoiding uneven task distribution and optimizing human resource utilization. Attached Figure Description

[0066] Figure 1 This is a flowchart illustrating the overall system workflow of the present invention.

[0067] Figure 2 This is a flowchart of the electronic work order generation and push process of the present invention;

[0068] Figure 3 This is a flowchart of the test data acquisition process for the present invention;

[0069] Figure 4 This is a flowchart of the data verification and logic verification process of the present invention. Detailed Implementation

[0070] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0071] Example 1;

[0072] Please see Figures 1-4 In this embodiment of the invention, an industrial internet platform factory inspection application system for home elevators includes a mobile terminal and an industrial internet platform server that are interconnected, wherein:

[0073] The industrial internet platform server is configured to generate electronic work orders based on the user identity information and inspection task allocation information of the inspection personnel, and push the electronic work orders to the mobile terminals of the corresponding users.

[0074] The mobile terminal is configured to: activate the audio sensor to collect sound signals during the operation of the home elevator to obtain operating sound data; use a high-precision tilt sensor to measure the leveling accuracy data of the elevator car; and use a tension sensor to measure the elevator door operator force data. The test data is then processed by Fourier transform and leveling accuracy Kalman filtering and uploaded to the industrial internet platform server in real time.

[0075] Using a high-precision tilt sensor fixed at the center point of the car top, the tilt angle between the car floor and the floor baseline is measured after the elevator reaches the target floor at its rated speed while unloaded.

[0076] The industrial internet platform server is also configured to: perform multi-dimensional verification and logical validation on the received inspection data; compare the currently received inspection dataset with the historical standard dataset retrieved based on the elevator model; calculate the deviation index according to the composite algorithm to automatically match the corresponding inspection standards and historical data; and generate a digital inspection report based on the verification results, and store the digital inspection report in the cloud database after asymmetric encryption.

[0077] When the industrial internet platform server generates and pushes electronic work orders, the specific configuration is as follows:

[0078] Determine the job type and authority level of the inspection personnel;

[0079] Based on the type of inspection task and the model of the home elevator, the corresponding inspection specifications are extracted from the database.

[0080] The system first generates electronic work orders based on the user identity information of the inspection personnel (such as job type and permission level) and inspection task allocation information, and pushes them to the corresponding user's mobile terminal. This process replaces the traditional paper work orders, reduces manual processing steps, and improves the efficiency and accuracy of work order processing.

[0081] The priority of electronic work orders is calculated based on the following formula:

[0082] ;

[0083] Wherein, P is the level weight corresponding to the user's permission level, ranging from 1 to 3, with higher values ​​indicating more experienced users; W is the urgency weight determined based on the task deadline, ranging from 1 to 5, with higher values ​​indicating tighter deadlines; S represents the system's automatic score, derived from a combination of task complexity and the processing efficiency of similar tasks in the past. The score S is calculated using a weighted average: S = 0.4 * number of inspection items + 0.3 * proportion of special components + 0.3 * deviation of historical average processing time, with a value range of 0-100; D represents the number of work orders currently pending, ranging from 0-100, used to dynamically adjust task allocation.

[0084] A higher priority value indicates that the work order should be processed first. This formula is designed to allocate resources rationally while ensuring task timeliness, preventing high-value or urgent tasks from being delayed. Finally, high-priority work orders are pushed to inspection personnel first. This step effectively improves work efficiency, reduces task delays, and achieves precise personnel scheduling. The formula's design integrates personnel capabilities, task urgency, and system status, optimizing work order allocation through mathematical modeling, thus solving the inefficiencies and prioritization problems caused by manual scheduling in traditional work order processing.

[0085] Based on the calculated push priority, electronic work orders with higher priority are pushed to the corresponding inspection personnel first.

[0086] The mobile terminal is specifically configured as follows when collecting test data:

[0087] The audio sensor is activated to collect sound signals during the operation of the home elevator in order to obtain operating noise data.

[0088] The leveling accuracy data of the elevator car is determined by a high-precision tilt sensor;

[0089] Elevator door operator force data is measured using a tension sensor;

[0090] The leveling accuracy can be preliminarily judged using the following formula:

[0091] ;

[0092] Where A is the actual measured leveling value, B is the preset standard leveling value set based on the elevator model and specifications, and T is the allowable error tolerance determined according to safety standards. If the absolute value difference is less than or equal to T, the leveling accuracy is considered qualified; otherwise, it is unqualified. The formula indicates that the difference between the measured value and the standard value should not exceed the set tolerance. This formula is set based on engineering practice experience to establish a reasonable error range, ensuring that the test results are reliable and applicable.

[0093] When the industrial internet platform server performs multi-dimensional verification and logical checks on inspection data and automatically matches the corresponding inspection standards and historical data, it is further configured as follows:

[0094] The currently received test dataset C is compared with the historical standard dataset H retrieved based on the elevator model. The historical standard dataset H contains a large amount of historical test data under statistical control.

[0095] Dataset H is used to filter inspection data of elevators of the same model within the past 3 years. After removing outliers outside the 3σ range, it is updated on a quarterly basis.

[0096] The deviation index DI of the current dataset C relative to the historical standard dataset H is calculated based on the following composite algorithm:

[0097] ;

[0098] For the i-th key test item: μ Ci μ is the average of the measurements for this item in the current dataset C. Hi σ is the long-term average of the measurements for this item in the historical standard dataset H; Hi is the standard deviation of the measured values ​​of this item in the historical standard dataset H, representing its normal fluctuation range; k is a preset sensitivity coefficient used to adjust the severity of the warning, usually set to 1-3, which can be adjusted according to actual needs.

[0099] A higher DI value indicates a more significant deviation between the current data and historical standard deviations. If the DI exceeds a preset threshold, the system triggers an anomaly warning, indicating potential equipment malfunction or inspection error. This formula integrates historical data through statistical methods, solving the problem of data silos and enabling intelligent diagnosis based on big data.

[0100] When performing data verification, the industrial internet platform server is also configured as follows:

[0101] Consistency verification is performed on multiple consecutively collected test data, including calculating the standard deviation σ of multiple measurements of the same test item:

[0102] ;

[0103] Where, x i For a single measurement, μ is the arithmetic mean of multiple measurements, and n is the number of measurements. If the standard deviation σ exceeds the allowable fluctuation threshold obtained from historical data statistics, the data set is judged to have abnormal dispersion, indicating that there may be unstable factors in the measurement process. This verification mechanism ensures the repeatability and consistency of the data and overcomes the random error problem commonly found in manual recording.

[0104] The industrial internet platform server is specifically configured as follows when generating digital inspection reports:

[0105] In addition to filling in the test results, timestamps, operator identification, and equipment ID information, the report template also automatically marks the data validity identifier and consistency verification results;

[0106] The pass rate Q is calculated by introducing a weighting factor CF based on task complexity. The calculation formula is as follows:

[0107] ;

[0108] Among them, S k Let T be the number of passes for the k-th complexity task. k Let be the total number of checks for the k-th complexity task, CF k This represents the weighting coefficient for the k-th complexity task; the higher the complexity, the larger the CF value. The formula uses weighting to ensure the pass rate more accurately reflects the actual quality level, avoiding statistical bias caused by uneven task difficulty. The report is encrypted using an asymmetric encryption algorithm and stored in a cloud database, supporting full lifecycle data traceability and statistical analysis, thus solving the problem of difficult data traceability in traditional factory inspections. The algorithm uses RSA-2048 encryption, the key pair is uniformly issued by the platform's CA center, the private key is stored in the HSM hardware module, and the public key is embedded in the mobile app.

[0109] Example 2;

[0110] Please see Figures 1-4 In this embodiment of the invention, when storing reports, the industrial internet platform server is also configured to: assign a unique universal identifier to each digital inspection report; set data lifecycle management rules to specify access permissions for report data at different time stages; periodically generate statistical analysis reports based on report data in the database, and support cross-departmental data sharing and viewing based on permissions. This design realizes structured management and efficient utilization of data, and solves the problem of data archiving and sharing difficulties in traditional factory inspection.

[0111] Before generating electronic work orders, the industrial internet platform server is also configured to: read the employee's IC card information or scan the QR code for identity authentication through the identity authentication module; generate a corresponding inspection item list based on the product batch information of the home elevator and in conjunction with the maintenance task allocation system; and generate the optimal inspection path plan based on the type of home elevator, installation location information, and maintenance cycle.

[0112] The optimal time to push a work order is calculated using the following formula:

[0113] ;

[0114] In this system, BT represents the calculated optimal time to push the work order, CT represents the current system time, and Dif represents the task difficulty coefficient, which is determined based on the elevator model, historical maintenance records, and the complexity of the inspection items. Dif ranges from 1 to 5, with higher values ​​indicating more complex tasks. For routine inspections, Dif=1; for complex inspections involving safety components, Dif=5. K is a preset time coefficient used to convert the difficulty coefficient into a time offset. The time coefficient K is set to 0.5 hours per unit of difficulty, meaning that when Dif=5, the push is delayed by 2.5 hours. This coefficient is determined based on a human resource model simulation. This formula dynamically adjusts the push time to ensure sufficient preparation time for complex tasks, avoiding work scheduling chaos caused by pushing work orders too early or too late.

[0115] User identity information is identified through a facial recognition module integrated into the mobile terminal, providing a higher level of security authentication; the industrial internet platform server is also configured to: combine the real-time geographical location information of inspection personnel and the current workload distribution to perform intelligent scheduling of work orders, thereby optimizing the allocation of human resources;

[0116] The real-time workload of inspection personnel is assessed using the following formula:

[0117] ;

[0118] Where Load is the assessed workload value, N is the total number of pending work orders currently assigned to the inspector, AD is the average time required to process a single work order based on historical data, and AT is the ratio of the inspector's planned working time to the actual available working time in the current scheduling cycle. If the planned working time is 8 hours and the actual available time is 7 hours, then AT = 7 / 8.

[0119] When the load of an inspector exceeds a preset threshold, the work order is automatically redistributed or scheduled to ensure a balanced workload, avoid overloading of individual personnel, and improve the efficiency of human resource utilization.

[0120] When collecting data, the mobile terminal uses a fixed sampling frequency to acquire sensor signals, ensuring data consistency and comparability. Fourier transform is performed on the acquired operational noise signals to extract the sound pressure level at a specific center frequency, enabling frequency domain analysis and a more accurate assessment of noise levels. Kalman filtering is applied to the displacement data acquired by the inertial navigation module to eliminate random noise, resulting in precise leveling accuracy data. These signal processing techniques improve the accuracy and reliability of data acquisition, resolving data distortion issues caused by sensor errors in traditional factory inspections.

[0121] Example 3;

[0122] Please see Figures 1-4To provide a specific example, an inspector's mobile terminal on an industrial internet platform receives an electronic work order pushed by the system at 8:30 AM, corresponding to a factory inspection task for a villa elevator of model HE-V02. When the system generates the work order, it calculates the push priority according to a preset algorithm. The user permission level weight P is set to 1.2, the task deadline urgency weight W is set to 1.5, the task complexity score S is 85 points, and the total number of work orders currently pending in the system is 12. Substituting into the priority calculation formula:

[0123] Priority = (1.2 × 1.5 + 85) / 12 = 86.8 / 12 ≈ 7.23.

[0124] This value is higher than the system's priority threshold of 5.0, so the work order is given priority push permission.

[0125] After the inspector completes identity authentication via the facial recognition module integrated into the mobile terminal, the system automatically loads the inspection item list corresponding to the elevator model. The mobile terminal connects to a high-precision sensor kit and begins collecting three key inspection data: For operational noise detection, an audio sensor collects the elevator's operating sound wave signal at a sampling rate of 48kHz. After Fast Fourier Transform processing, the sound pressure level at the 125Hz center frequency is extracted as 42.3dB. For leveling accuracy detection, a high-precision tilt sensor measures the elevator's stopping position at three floors, obtaining measurements of 5.02mm, 4.98mm, and 5.15mm respectively. The system's preset standard leveling value is 5.00mm, with an allowable error tolerance of 0.20mm. The system automatically verifies the results.

[0126] The difference in the first group is 0.02 mm, which is less than the tolerance limit;

[0127] The difference in the second group was 0.02 mm, which is less than the tolerance limit.

[0128] The difference in the third group was 0.15 mm, which is less than the tolerance value.

[0129] All three measurement results met the specifications.

[0130] The door operator force detection, using a tension sensor, measured the elevator door's running resistance to be 132N, which is within the standard range of 120N to 150N.

[0131] After all inspection data is uploaded to the industrial internet platform server in real time via the 5G network, the system immediately initiates a multi-dimensional verification process. The current dataset is compared with historical standard datasets of the same elevator model. The long-term average of operating noise is 41.8 dB, with a standard deviation of 1.2 dB; the long-term average of leveling accuracy is 5.01 mm, with a standard deviation of 0.08 mm. Taking a sensitivity coefficient k=2, the deviation index is calculated:

[0132] Operating noise deviation index = 0.21;

[0133] Leveling accuracy deviation index = 0.25;

[0134] The comprehensive deviation index is the arithmetic mean of the deviation indices of the above items, which is 0.23. This is lower than the system's preset threshold of 1.0, and the current test data is determined to meet the historical standards.

[0135] The system synchronously performs data consistency verification, calculating the standard deviation of three measurements of leveling accuracy:

[0136] Arithmetic mean = (5.02 + 4.98 + 5.15) / 3 = 5.05 mm;

[0137] Variance = [(5.02 - 5.05)] 2 +(4.98-5.05) 2 +(5.15-5.05) 2 ] / 2=0.0158 / 2=0.0079;

[0138] The standard deviation is approximately 0.089 mm, which is less than the historical allowable fluctuation threshold of 0.15 mm, indicating that the data dispersion is normal.

[0139] After successful verification, the system automatically generates a digital inspection report. In the pass rate calculation, this task is classified as level 2 complexity, with a weighting factor of 1.2. According to the weighted pass rate calculation formula:

[0140] Pass rate = (1 × 1.2) / (1 × 1.2) × 100% = 100%;

[0141] The report is encrypted using the RSA-256 algorithm and stored in a cloud database, with a unique identifier assigned to it. According to data lifecycle management rules, the report has full access for the first three months, after which it automatically switches to read-only mode.

[0142] Regarding work order scheduling optimization, the system monitors the work status of inspection personnel in real time. At 14:00 on the same day, the system detected that Zhang San currently had 3 work orders pending processing, with an average processing time of 1.5 hours per work order. The planned work time for the current scheduling cycle was 8 hours, while the actual available time was 6 hours. Substituting into the workload calculation formula:

[0143] Total workload to be processed = 3 × 1.5 = 4.5 hours;

[0144] Time resource ratio = 6 / 8 = 0.75;

[0145] Workload value = 4.5 / 0.75 = 6.0;

[0146] When the value exceeds the system's set load threshold of 5.0, the system immediately activates the intelligent scheduling program to reassign the newly arrived work order to another inspector with a load rate of 3.2.

[0147] Working principle: The system digitizes and intelligently allocates work orders through an industrial internet platform server. It generates electronic work orders based on verified personnel identity and task information, and uses a priority algorithm that integrates personnel permissions, task urgency, complexity, and system load to determine the order in which work orders are pushed, thereby efficiently assigning suitable tasks to suitable personnel.

[0148] Secondly, the system integrates multiple sensors through mobile terminals to automatically collect key performance data of home elevators. After the data is uploaded to the server, the system initiates multi-dimensional intelligent verification: First, it compares the current data with historical standard datasets of elevators of the same model, and determines whether there are significant anomalies by calculating the "deviation index"; second, it performs "consistency verification" on multiple measurements of the same item, and judges the stability of the measurement process by calculating its standard deviation, effectively identifying random errors or equipment instability.

[0149] The system automatically generates digital inspection reports based on the verification results. When calculating the overall pass rate, a task complexity weighting factor is introduced for correction, making the evaluation results more objective and accurate. The reports are ultimately encrypted and stored in a cloud database, laying a solid foundation for full lifecycle data traceability and statistical analysis.

[0150] Regarding process optimization, before generating a work order, the system comprehensively considers the task difficulty and uses a calculation model to determine the optimal time to push the work order, allowing sufficient preparation time for complex tasks and avoiding chaos in work arrangements. Furthermore, the system enhances the security of identity authentication by integrating technologies such as facial recognition.

[0151] The system continuously monitors the real-time geographical location and workload of each inspection personnel. Through an evaluation model, it quantifies their current workload. Once it detects that a person's workload is too high, the system automatically reassigns new tasks to employees with lower workloads, achieving dynamic optimization and balanced allocation of human resources and ensuring overall inspection efficiency.

[0152] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An industrial internet platform factory inspection application system for home elevators, characterized in that, This includes interconnected mobile terminals and industrial internet platform servers, among which: The industrial internet platform server is configured to generate electronic work orders based on the user identity information and inspection task allocation information of the inspection personnel, and push the electronic work orders to the mobile terminals of the corresponding users to replace traditional paper work orders. The mobile terminal is configured to collect inspection data of the home elevator and upload the inspection data to the industrial internet platform server in real time. The inspection data includes operating noise, leveling accuracy and door operator force, and is used to realize automatic data collection and real-time transmission. The industrial internet platform server is also configured to: perform multi-dimensional verification and logical validation on the received inspection data, and automatically match the corresponding inspection standards and historical data to solve the problem of data isolation; and generate a digital inspection report based on the verification results, and store the digital inspection report in the cloud database for full lifecycle data traceability and statistical analysis to overcome the shortcomings of data traceability difficulties.

2. The industrial internet platform factory inspection application system for home elevators according to claim 1, characterized in that, The industrial internet platform server is specifically configured as follows when generating and pushing electronic work orders: Determine the job type and authority level of the inspection personnel; Based on the type of inspection task and the model of the home elevator, the corresponding inspection specifications are extracted from the database; The push priority of the electronic work order is calculated based on the following formula: ； Where P is the level weight corresponding to the user permission level, W is the urgency weight determined based on the task deadline, S is the score automatically generated by the system according to the task complexity, and the score S is obtained by weighted calculation: S=0.4*number of inspection items+0.3*proportion of special parts+0.3*deviation value of historical average processing time, with a value range of 0-100, and D is the total number of work orders to be processed in the current system. Based on the calculated push priority, electronic work orders with higher priority are pushed to the corresponding inspection personnel first.

3. The industrial internet platform factory inspection application system for home elevators according to claim 1, characterized in that, The mobile terminal is specifically configured as follows when collecting test data: The audio sensor is activated to collect sound signals during the operation of the home elevator in order to obtain operating noise data. The leveling accuracy data of the elevator car is determined by a high-precision tilt sensor; Elevator door operator force data is measured using a tension sensor; The leveling accuracy can be preliminarily judged using the following formula: ； Where A is the actual measured leveling value, B is the preset standard leveling value, and T is the allowable error tolerance.

4. The industrial internet platform factory inspection application system for home elevators according to claim 1, characterized in that, When the industrial internet platform server performs multi-dimensional verification and logical checks on the inspection data and automatically matches the corresponding inspection standards and historical data, it is further configured as follows: The currently received test dataset C is compared with the historical standard dataset H retrieved based on the elevator model. The historical standard dataset H contains a large amount of historical test data under statistical control. The deviation index DI of the current dataset C relative to the historical standard dataset H is calculated based on the following composite algorithm: ； For the i-th key test item: μ Ci μ is the average of the measurements for this item in the current dataset C. Hi σ is the long-term average of the measurements for this item in the historical standard dataset H; Hi is the standard deviation of the measured values ​​of this item in the historical standard dataset H, representing its normal fluctuation range; k is a preset sensitivity coefficient used to adjust the severity of the warning. If the calculated deviation index DI is greater than the preset threshold, it is determined that the current test data deviates significantly from the historical standard, and an anomaly warning is triggered.

5. The industrial internet platform factory inspection application system for home elevators according to claim 1, characterized in that, The industrial internet platform server is also configured to perform data verification as follows: Consistency verification is performed on multiple consecutively collected test data, including calculating the standard deviation σ of multiple measurements of the same test item: ； Where, x i denoted as a single measurement, μ as the arithmetic mean of multiple measurements, and n as the number of measurements. If the standard deviation σ exceeds the allowable fluctuation threshold obtained based on historical data statistics, the data set is determined to have abnormal dispersion.

6. The industrial internet platform factory inspection application system for home elevators according to claim 1, characterized in that, The industrial internet platform server is specifically configured as follows when generating digital inspection reports: In addition to filling in the test results, timestamps, operator identification, and equipment ID information, the report template also automatically marks the data validity identifier and consistency verification results; The pass rate Q is calculated by introducing a weighting factor CF based on task complexity. The calculation formula is as follows: ； Among them, S k Let T be the number of passes for the k-th complexity task. k Let be the total number of checks for the k-th complexity task, CF k For the k-th type of complexity task; The generated report is encrypted using an asymmetric encryption algorithm and then stored in a cloud database.

7. The industrial internet platform factory inspection application system for home elevators according to claim 6, characterized in that, The industrial internet platform server is also configured to store reports as follows: Assign a unique, universally recognized identifier to each digital inspection report; Set data lifecycle management rules to specify access permissions for report data at different time stages; Regularly generate statistical analysis reports based on data from the database, and support cross-departmental data sharing and viewing based on permissions.

8. The industrial internet platform factory inspection application system for home elevators according to claim 1, characterized in that, Before generating electronic work orders, the industrial internet platform server is also configured as follows: The identity authentication module reads the employee's IC card information to perform identity authentication; Based on the product batch information of the home elevators and combined with the maintenance task allocation system, a corresponding list of inspection items is generated. Based on the type of home elevator, installation location information, and maintenance cycle, the optimal inspection path plan is generated; The optimal time to push a work order is calculated using the following formula: ； Wherein, BT is the calculated optimal work order push time, CT is the current system time, Dif is the task difficulty coefficient based on the elevator model, historical maintenance records and the complexity of the inspection items, and its value ranges from 1 to 5. The larger the value, the more complex the task. K is the preset time coefficient, which is used to convert the difficulty coefficient into a time offset.

9. The industrial internet platform factory inspection application system for home elevators according to claim 1, characterized in that, The user's identity information is identified through a facial recognition module integrated into the mobile terminal; The industrial internet platform server is also configured to: intelligently schedule work orders by combining the real-time geographical location information of inspection personnel and the current workload distribution, in order to optimize the allocation of human resources; The real-time workload of inspection personnel is assessed using the following formula: ； Where Load is the assessed workload value, N is the total number of pending work orders currently assigned to the inspector, AD is the average time required to process a single work order based on historical data, and AT is the ratio of the inspector's planned working time to the actual available working time in the current scheduling cycle. When the load of an inspector exceeds a preset threshold, the work order will be automatically redistributed.

10. The industrial internet platform factory inspection application system for home elevators according to claim 1, characterized in that, When collecting data, the mobile terminal is also configured to: Sensor signals are acquired at a fixed sampling frequency; The collected operating noise signal is subjected to Fourier transform to extract the sound pressure level at a specific center frequency; The displacement data collected by the inertial navigation module is processed by Kalman filtering to obtain accurate leveling accuracy data.

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