Work order distribution tracking management system and method based on Internet of Things

Abstract

The invention discloses a work order distribution tracking management system and method based on the Internet of Things, and the system comprises a work order information obtaining module, a work order weight determining module, a work order distribution module, a work order tracking module and a work order analysis module. And the work order weight determination module is used for obtaining work order information submitted by a user and work order information submitted by the user, extracting key feature factors from the obtained work order information, carrying out weighted calculation on the key feature factors through a weight algorithm, and determining a weight value of the work order. According to the invention, the staff with corresponding experience or ability is allocated according to the weight of the work order, so that the overall processing efficiency and completion rate of the work order can be effectively improved, processing reference suggestions are provided for the work order, the work order processing efficiency of the staff can be improved, help can be provided for the staff with less experience, the training period can be shortened, and the training efficiency is improved. And the pressure of trainees is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of work order management, and in particular to a work order distribution tracking management system and method based on the Internet of Things. Background Art

[0002] In manufacturing plants, a large number of production equipment require regular maintenance and repair. IoT sensors can monitor the operating status of equipment in real time. When equipment fails or performance deteriorates, a work order is automatically generated and the maintenance personnel are notified. Maintenance personnel can obtain detailed information and fault descriptions of the equipment through the work order management system, quickly locate the problem and perform repairs. For example, automobile manufacturers can use the IoT work order management system to monitor and maintain robots, stamping equipment, etc. on the production line in real time to ensure the stable operation of the production line.

[0003] For staff, there are various reasons for the generation of work orders, and companies need to train employees to ensure that they can handle various work orders smoothly. However, the training process may take up employees' working time and affect the normal development of the business. At the same time, after the training cycle is over, experienced employees may be required to provide further training or teaching to some inexperienced employees. Some employees may have resistance to training or teaching tasks, which increases their own work pressure under the existing work. This will also cause some employees to only handle simpler work orders, which is not conducive to the work order management system to equally distribute work orders. For example, some more difficult work orders are given to less experienced staff, resulting in the inability to complete the work order resolution in a timely manner, or there are legacy risks. Summary of the invention

[0004] The purpose of the present invention is to provide a work order allocation tracking management system and method based on the Internet of Things to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a work order allocation tracking management system based on the Internet of Things, comprising:

[0006] The work order information acquisition module is used to obtain the work order information automatically uploaded by the IoT device and the work order information submitted by the user;

[0007] A work order weight determination module, which is used to extract key characteristic factors from the acquired work order information, perform weighted calculation on the key characteristic factors through a weight algorithm, and determine the weight value of the work order;

[0008] A work order allocation module, which is used to collect status information of work order processing units in real time, and allocate work orders according to the weight value of the work order information and the status information of the work order processing units;

[0009] A work order tracking module, which is used to continuously update the processing progress of the work order information and record the processing results of the work order information through the real-time data feedback of the IoT device and the operation feedback of the work order processing unit;

[0010] The work order analysis module is used to compare the key feature factors of the work order information with the historical work order information, obtain the work orders with a similarity exceeding a threshold, and further obtain the operation information in the historical work orders, and optimize the operation information in the historical work orders, generate work order processing reference suggestions, and send the work order processing reference suggestions to the work order processing unit.

[0011] Preferably, the work order information acquisition module includes an Internet of Things sensor monitoring unit and a user submission unit;

[0012] The IoT sensor monitoring unit is used to monitor the device operating status, performance parameters and environmental indicators in real time through IoT sensors deployed in IoT devices or systems, and automatically generate work order information when abnormal data is detected;

[0013] The user submission unit is used to receive work order information manually submitted by a user through a mobile terminal or a management interface.

[0014] Preferably, the user submission unit further includes an information verification subunit;

[0015] The information verification subunit is used to verify whether the work order information submitted by the user meets the preset standards;

[0016] The preset standard is used to define whether the work order information is complete and accurate.

[0017] Preferably, the work order weight determination module includes a feature extraction unit and a weight calculation unit;

[0018] The feature extraction unit is used to extract key feature factors from the work order information, including problem type factors, impact scope factors and urgency factors;

[0019] The weight calculation unit is used to perform weighted calculation on the key feature factors through a weight algorithm to determine the weight value of the work order.

[0020] Preferably, the work order allocation module includes a work unit status collection unit and an intelligent allocation unit;

[0021] The work unit status collection unit is used to collect status information of the work order processing unit in real time. The work order processing unit includes staff and controllable work units. The status information of the staff includes work status, skill level, and location information. The status information of the controllable work unit includes operating status, processing capacity, and current task load.

[0022] The intelligent allocation unit is used to allocate work orders according to the weight value of the work order and the status information of the work unit.

[0023] Preferably, the work order allocation module further includes a work order content display unit and a processing suggestion display unit;

[0024] The work order content display unit is used to display detailed work order content to the assigned work unit;

[0025] The processing suggestion display unit is used to display the work order processing reference suggestion generated by the work order analysis module.

[0026] Preferably, the work order tracking module includes a progress updating unit and a result recording unit;

[0027] The progress updating unit is used to continuously update the processing progress of the work order through the real-time data feedback of the Internet of Things device and the operation feedback of the work order processing unit, and dynamically adjust the progress update frequency of the work order according to the weight coefficient of the urgency factor;

[0028] The result recording unit is used to record the processing result of the work order.

[0029] Preferably, the work order analysis module includes a work order comparison unit, a suggestion generation unit and a feedback tracking unit;

[0030] The work order comparison unit is used to compare the key feature factors of the work order information with the historical work order information, obtain the historical work order information whose similarity exceeds a threshold, and send a suggestion generation request;

[0031] The suggestion generation unit is used to, after accepting the suggestion generation request, obtain the historical work order information whose similarity exceeds a threshold, further obtain the operation information of the historical work order information, and then generate a work order processing reference suggestion according to the operation information;

[0032] The feedback tracking unit is used to send the generated work order processing reference suggestion to the work order allocation module in the form of a report, and obtain the processing result of the feedback from the result recording unit, regularly evaluate the execution effect of the work order processing reference suggestion based on the result processing result, and optimize the parameters of the suggestion generation unit based on the evaluation result.

[0033] The present invention also provides a work order distribution tracking management system and method based on the Internet of Things, comprising the following steps:

[0034] Obtain work order information automatically uploaded by IoT devices and work order information submitted by users;

[0035] Extracting key characteristic factors from the acquired work order information, performing weighted calculation on the key characteristic factors through a weight algorithm, and determining a weight value of the work order;

[0036] Collecting status information of work order processing units in real time, and allocating work orders according to weight values ​​of the work order information and status information of the work order processing units;

[0037] By comparing the key feature factors of the work order information with the historical work order information, the work orders with a similarity exceeding a threshold are obtained, and the operation information in the historical work orders is further obtained, and the operation information in the historical work orders is optimized to generate a work order processing reference suggestion, and the work order processing reference suggestion is sent to the work order processing unit;

[0038] Through the real-time data feedback of the Internet of Things device and the operation feedback of the work order processing unit, the processing progress of the work order information is continuously updated, and the processing result of the work order information is recorded.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] 1. By allocating staff with corresponding experience or ability according to the weight of the work order, the overall processing efficiency and completion rate of the work order can be effectively improved;

[0041] 2. By managing robots or other non-human mechanical work units to process work orders, the processing efficiency of low-difficulty repeatable work orders can be improved and human involvement can be reduced;

[0042] 3. At the same time, the present invention also performs information analysis on the processed work orders and provides processing reference suggestions for newly appeared work orders, which can not only improve the work order processing efficiency of the staff, but also provide assistance to the less experienced staff, so that they can shorten the training cycle and reduce the pressure on the trainers. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A structural block diagram of an image classification device based on biased selection pooling provided by an embodiment of the present invention;

[0044] Figure 2 The present invention provides a main flow chart of a work order distribution tracking management method based on the Internet of Things. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] See also Figure 1 The present invention provides a work order distribution tracking management system based on the Internet of Things, comprising:

[0047] A work order information acquisition module 11 is used to acquire work order information automatically uploaded by an IoT device and work order information submitted by a user;

[0048] A work order weight determination module 12 is used to extract key characteristic factors from the acquired work order information, and perform weighted calculation on the key characteristic factors through a weight algorithm to determine the weight value of the work order;

[0049] A work order allocation module 13, which is used to collect status information of work order processing units in real time, and to allocate work orders according to the weight value of the work order information and the status information of the work order processing units;

[0050] A work order tracking module 14, which is used to continuously update the processing progress of the work order information and record the processing result of the work order information through the real-time data feedback of the Internet of Things device and the operation feedback of the work order processing unit;

[0051] The work order analysis module 15 is used to compare the key feature factors of the work order information with the historical work order information, obtain the work orders with a similarity exceeding a threshold, and further obtain the operation information in the historical work orders, and optimize the operation information in the historical work orders, generate work order processing reference suggestions, and send the work order processing reference suggestions to the work order processing unit.

[0052] In an optional embodiment, the work order information acquisition module 11 includes an Internet of Things sensor monitoring unit 111 and a user submission unit 112;

[0053] The IoT sensor monitoring unit 111 is used to monitor the device operation status, performance parameters and environmental indicators in real time through IoT sensors deployed in IoT devices or systems, and automatically generate work order information when abnormal data is detected;

[0054] The user submission unit 112 is used to receive work order information manually submitted by a user through a mobile terminal or a management interface.

[0055] In an optional embodiment, the user submission unit 112 further includes an information verification subunit;

[0056] The information verification subunit is used to verify whether the work order information submitted by the user meets the preset standards;

[0057] The preset standard is used to define whether the work order information is complete and accurate.

[0058] Understandably, IoT sensors deployed in various IoT devices and systems monitor the device operating status, performance parameters and environmental indicators in real time. When the sensor detects abnormal data, it automatically triggers the work order generation mechanism to generate a work order containing detailed problem description, location, device identification, abnormal data value and other information. At the same time, it also receives work order information manually submitted by users through mobile terminals or management interfaces. The work order submitted by users should include clear problem statements, urgency ratings, expected resolution time and other content.

[0059] In an optional embodiment, the work order weight determination module 12 includes a feature extraction unit 121 and a weight calculation unit 122;

[0060] The feature extraction unit 121 is used to extract key feature factors from the work order information, including problem type factors, impact scope factors and urgency factors. For example, the problem types are divided into specific categories such as equipment hardware failure, software system errors, and human operation errors. The impact scope is quantitatively evaluated according to the number of affected devices, business criticality, etc., and the urgency is determined according to the expected interruption time of the business and the degree of loss.

[0061] The weight calculation unit 122 is used to perform weighted calculation on the key feature factors through a weight algorithm to determine the weight value of the work order, wherein the weight coefficients of the problem type factor, the impact range factor and the urgency factor are dynamically adjusted according to actual business needs and historical data. For example, the weight value of a highly urgent and widely affected equipment failure work order is relatively high, while the weight value of a low-urgent operation error work order is relatively low.

[0062] The weighting algorithm is as follows:

[0063] Ticket weight = α × problem type weight + β × impact range weight + γ × urgency weight

[0064] The problem type weight is set to different values ​​according to different problem types. For example, a device hardware failure can be set to a higher value W1, a software system error can be set to a medium value W2, and a human operation error can be set to a lower value W3;

[0065] The impact range weight can be determined based on the number of affected devices N and the business criticality K. The impact range weight = f(N, K). By setting a function, for example, when the number of devices is greater and the business criticality is higher, the weight value is greater. For example, the impact range weight = N × K2;

[0066] The urgency weight is set according to the urgency level, with a high urgency level set to a high value W4, a medium urgency level set to a medium value W5, and a low urgency level set to a lower value W6;

[0067] α, β, and γ are adjustment coefficients, which can be dynamically adjusted according to actual business needs and historical data, and satisfy α+β+γ=1.

[0068] For example, for a work order with a high urgency due to a hardware failure of a device that affects multiple key devices, assuming α=0.4, β=0.3, γ=0.3, problem type weight W1=0.8, impact range weight calculated as 0.7, urgency weight W4=0.9, then the work order weight = 0.4×0.8+0.3×0.7+0.3×0.9=0.78.

[0069] In an optional embodiment, the work order allocation module 13 includes a work unit status collection unit 131 and an intelligent allocation unit 132;

[0070] The work unit status collection unit 131 is used to collect the status information of the work order processing unit in real time. The work order processing unit includes staff and controllable work units (such as robots, automation equipment, etc.). The status information of the staff includes obtaining their work status (busy, idle, vacation, etc.), skill level (determining the proficiency level in different skill fields according to the types of work orders they have processed in the past and their evaluations), and location information (determining their specific location in the work area through positioning equipment or a sign-in system). The status information of the controllable work unit includes operating status (normal operation, failure, maintenance), processing capacity (determining the types and quantities of work orders that can be processed according to equipment performance parameters), and current task load;

[0071] The working status of the staff is realized by connecting with the attendance system and the task management system. The skill level of the staff is obtained through the performance evaluation and skill certification of their historical processing work orders. The location information of the staff is obtained through indoor positioning technology or mobile device positioning. For non-human work units, their operating status is obtained through equipment sensors and monitoring systems. The processing capacity is evaluated based on equipment specification parameters and historical processing task data, and the current task load is monitored through the task queue management system.

[0072] The intelligent allocation unit 132 is used to allocate work orders according to the weight value of the work order and the status information of the work unit. For example, for urgent work orders with high weight, priority is given to workers who are close to the problem location, have high skill levels, and are currently idle, or non-human work units with strong processing capabilities and are idle. At the same time, the rationality of the allocation is ensured to avoid over-allocation to a specific work unit, resulting in overload.

[0073] The intelligent allocation unit 132 allocates work orders based on intelligent allocation strategies of optimization algorithms such as genetic algorithms, simulated annealing algorithms and particle swarm optimization algorithms, and factors such as work order weights, work unit status information and balance of task allocation need to be considered during allocation.

[0074] In an optional embodiment, taking genetic algorithm as an example, the operation steps of the intelligent allocation unit 132 are as follows:

[0075] S1. Encoding and initialization: Represent work orders and work units in the form of chromosomes. For example, the characteristics of work orders (such as problem type, urgency, scope of impact, etc.) and work unit characteristics (such as skill level, location, current task load, etc.) can be encoded as binary strings or real number vectors; initialize a population, that is, a set of randomly generated chromosomes.

[0076] S2. Fitness evaluation: Define a fitness function to evaluate the quality of each chromosome; the fitness function can take into account multiple factors, such as the rationality of work order allocation, load balance of work units, minimization of processing time, etc. For example, the fitness function can be defined as the weighted sum of the total time required to complete all work orders and the load balance of work units.

[0077] S3. Genetic operations: Generate new chromosomes through genetic operations such as selection, crossover and mutation. The selection operation can select excellent chromosomes to enter the next generation according to the fitness function. The crossover operation can exchange some genes of two chromosomes to generate new chromosomes. The mutation operation can randomly change certain genes of chromosomes to increase the diversity of the population.

[0078] S4. Iterative optimization: Repeat the fitness evaluation and genetic operation until the stopping condition is met (such as reaching the preset number of iterations or the fitness function value no longer improves); finally, the chromosome with the highest fitness is selected as the optimal work order allocation solution.

[0079] It should be noted that the above-mentioned operation steps for intelligent allocation of work orders through genetic algorithms are only a possible example. In order to ensure the efficiency and rationality of the allocation results, the specific algorithm to be used is selected according to actual usage and is not limited here.

[0080] In an optional embodiment, the work order assignment module 13 further includes a work order content display unit 133 and a processing suggestion display unit 134;

[0081] The work order content display unit 133 is used to display detailed work order content to the assigned work unit; the display method includes presenting the work order content in a mobile terminal application or web management interface in a graphic and textual form, the problem description can be in a combination of structured text and flow charts, the relevant equipment data information can be displayed through data visualization charts, and the pictures and video materials can be viewed by online playback or downloading;

[0082] The processing suggestion display unit 134 is used to display the work order processing reference suggestions generated by the work order analysis module 15. For example, for similar equipment failure work orders, it provides suggestions such as previous successful processing solutions, a list of tools and spare parts that may be needed, and an estimated processing time range, to assist the work unit in processing the work order quickly and efficiently.

[0083] Understandably, when work orders are assigned, the work order content needs to be clearly displayed to the assigned work unit, including a detailed description of the problem, which can be accompanied by multimedia materials such as pictures and videos to present the problem more intuitively; technical parameters of related equipment, historical maintenance records and other data information; the specific time when the problem occurred and how long it lasted, which is conducive to the work order processing unit to have a clearer understanding of the work order information. At the same time, the work order processing reference suggestions can effectively help the work order processing unit to process the work order, improve work efficiency, and increase the work order completion rate, especially for work order processing units with relatively scarce work experience.

[0084] In an optional embodiment, the work order tracking module 14 includes a progress updating unit 141 and a result recording unit 142;

[0085] The progress updating unit 141 is used to continuously update the processing progress of the work order through the real-time data feedback of the Internet of Things device and the operation feedback of the work order processing unit, and dynamically adjust the progress update frequency of the work order according to the weight coefficient of the urgency factor, so that real-time updates can be achieved for urgent work orders, and regular updates can be achieved for general work orders; for example, when processing a work order, the staff can upload the work progress in real time through the mobile terminal, including the measures taken, the current problem status, the expected completion time and other information; for controllable work units such as robots, the processing progress is fed back through their built-in sensors and communication modules;

[0086] The result recording unit 142 is used to record the processing results of the work order. The processing results include the judgment criteria and verification methods for whether the work order problem is completely solved, the solution time accurate to the minute level, a detailed list of resources actually used, and user satisfaction evaluation collected through questionnaires or online feedback systems and quantitatively analyzed.

[0087] In an optional embodiment, the work order analysis module 15 includes a work order comparison unit 151, a suggestion generation unit 152 and a feedback tracking unit 153;

[0088] The work order comparison unit 151 is used to compare the key feature factors of the work order information with the historical work order information, obtain the historical work order information whose similarity exceeds a threshold, and send a suggestion generation request;

[0089] The suggestion generation unit 152 is used to, after receiving the suggestion generation request, obtain the historical work order information whose similarity exceeds the threshold, further obtain the operation information of the historical work order information, and then generate a work order processing reference suggestion according to the operation information;

[0090] The feedback tracking unit 153 is used to send the generated work order processing reference suggestion in the form of a report to the work order allocation module 13, and obtain the processing result of the feedback from the result recording unit 142, regularly evaluate the execution effect of the work order processing reference suggestion based on the result processing result, and optimize the parameters of the suggestion generation unit 152 based on the evaluation result.

[0091] In an optional embodiment, when the suggestion generation unit 152 generates a reference suggestion for work order processing, it will also analyze whether the work order information is an avoidable work order based on the comparison results of the work order information. For example, for an equipment maintenance work order caused by an operating error, the specific operating link of the error, the training status of the operator and other factors will be analyzed; for an equipment aging work order caused by overheating, problems with the ambient temperature monitoring mechanism, equipment heat dissipation design and other aspects will be analyzed; and when generating suggestions for avoidable work orders, the focus will be on how to prevent the problem from occurring or completely solve the problem to avoid the recurrence of similar work orders. For example: for an operating error work order, it is recommended to optimize the operating process and strengthen employee training and assessment; for an equipment aging work order, it is recommended to improve the equipment cooling system, add temperature monitoring and early warning devices, etc.

[0092] In this embodiment, by allocating staff with corresponding experience or ability according to the weight of the work order, the overall processing efficiency and completion rate of the work order can be effectively improved; by managing robots or other non-human mechanical work units to process the work order, the processing efficiency of low-difficulty repeatable work orders can be improved and human participation can be reduced; at the same time, the present invention also performs information analysis on the processed work orders and provides processing reference suggestions for newly emerging work orders, which can not only improve the work order processing efficiency of the staff, but also provide assistance to less experienced staff, so that they can reduce the training cycle and reduce the pressure on trainers.

[0093] In order to better understand the above embodiments, Figure 2As shown, the present invention also provides a specific step flow chart of a work order allocation tracking management system based on the Internet of Things, and the method at least includes:

[0094] Step 201, obtaining work order information automatically uploaded by the IoT device and work order information submitted by the user;

[0095] Step 202, extracting key characteristic factors from the acquired work order information, performing weighted calculation on the key characteristic factors through a weight algorithm, and determining a weight value of the work order;

[0096] Step 203, collecting status information of the work order processing unit in real time, and allocating the work order according to the weight value of the work order information and the status information of the work order processing unit;

[0097] Step 204, comparing the key feature factors of the work order information with the historical work order information, obtaining the work orders with similarity exceeding the threshold, and further obtaining the operation information in the historical work orders, optimizing the operation information in the historical work orders, generating a work order processing reference suggestion, and sending the work order processing reference suggestion to the work order processing unit;

[0098] Step 205: continuously update the processing progress of the work order information and record the processing result of the work order information through the real-time data feedback of the IoT device and the operation feedback of the work order processing unit.

[0099] In this embodiment, by allocating staff with corresponding experience or ability according to the weight of the work order, the overall processing efficiency and completion rate of the work order can be effectively improved; by managing robots or other non-human mechanical work units to process the work order, the processing efficiency of low-difficulty repeatable work orders can be improved and human participation can be reduced; at the same time, the present invention also performs information analysis on the processed work orders and provides processing reference suggestions for newly emerging work orders, which can not only improve the work order processing efficiency of the staff, but also provide assistance to less experienced staff, so that they can reduce the training cycle and reduce the pressure on trainers.

[0100] Those skilled in the art will appreciate that the modules and method steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0101] In the several embodiments provided in the present application, it should be understood that the disclosed modules, units and systems can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or units can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of modules or units, which can be electrical, mechanical or other forms.

[0102] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0103] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0104] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage media include: U disk, mobile hard disk, read-only storage server, random access storage server, disk or optical disk, and other media that can store program instructions.

[0105] In addition, it should be noted that the combination of the various technical features in this case is not limited to the combination described in the claims of this case or the combination described in the specific embodiments. All technical features described in this case can be freely combined or combined in any way unless there is a contradiction between them.

[0106] It should be noted that the above examples are only specific embodiments of the present invention, and the present invention is obviously not limited to the above examples, and there are many similar variations. All variations directly derived or associated from the contents disclosed by the technicians in this field should fall within the protection scope of the present invention.

[0107] The above are only preferred embodiments of the present invention and are not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A work order distribution tracking management system based on the Internet of Things, characterized in that: include: The work order information acquisition module is used to obtain the work order information automatically uploaded by the IoT device and the work order information submitted by the user; A work order weight determination module, which is used to extract key characteristic factors from the acquired work order information, perform weighted calculation on the key characteristic factors through a weight algorithm, and determine the weight value of the work order; A work order allocation module, which is used to collect status information of work order processing units in real time, and allocate work orders according to the weight value of the work order information and the status information of the work order processing units; A work order tracking module, which is used to continuously update the processing progress of the work order information and record the processing results of the work order information through the real-time data feedback of the IoT device and the operation feedback of the work order processing unit; The work order analysis module is used to compare the key feature factors of the work order information with the historical work order information, obtain the work orders with a similarity exceeding a threshold, and further obtain the operation information in the historical work orders, and optimize the operation information in the historical work orders, generate work order processing reference suggestions, and send the work order processing reference suggestions to the work order processing unit.

2. The work order distribution tracking management system based on the Internet of Things according to claim 1 is characterized in that: The work order information acquisition module includes an Internet of Things sensor monitoring unit and a user submission unit; The IoT sensor monitoring unit is used to monitor the device operating status, performance parameters and environmental indicators in real time through IoT sensors deployed in IoT devices or systems, and automatically generate work order information when abnormal data is detected; The user submission unit is used to receive work order information manually submitted by a user through a mobile terminal or a management interface.

3. The work order distribution tracking management system based on the Internet of Things according to claim 2 is characterized in that: The user submission unit also includes an information verification subunit; The information verification subunit is used to verify whether the work order information submitted by the user meets the preset standards; The preset standard is used to define whether the work order information is complete and accurate.

4. The work order distribution tracking management system based on the Internet of Things according to claim 1 is characterized in that: The work order weight determination module includes a feature extraction unit and a weight calculation unit; The feature extraction unit is used to extract key feature factors from the work order information, including problem type factors, impact scope factors and urgency factors; The weight calculation unit is used to perform weighted calculation on the key feature factors through a weight algorithm to determine the weight value of the work order.

5. The work order distribution tracking management system based on the Internet of Things according to claim 1 is characterized in that: The work order allocation module includes a work unit status collection unit and an intelligent allocation unit; The work unit status collection unit is used to collect status information of the work order processing unit in real time. The work order processing unit includes staff and controllable work units. The status information of the staff includes work status, skill level, and location information. The status information of the controllable work unit includes operating status, processing capacity, and current task load. The intelligent allocation unit is used to allocate work orders according to the weight value of the work order and the status information of the work unit.

6. The work order distribution tracking management system based on the Internet of Things according to claim 5 is characterized in that: The work order allocation module also includes a work order content display unit and a processing suggestion display unit; The work order content display unit is used to display detailed work order content to the assigned work unit; The processing suggestion display unit is used to display the work order processing reference suggestion generated by the work order analysis module.

7. The work order distribution tracking management system based on the Internet of Things according to claim 4 is characterized in that: The work order tracking module includes a progress updating unit and a result recording unit; The progress updating unit is used to continuously update the processing progress of the work order through the real-time data feedback of the Internet of Things device and the operation feedback of the work order processing unit, and dynamically adjust the progress update frequency of the work order according to the weight coefficient of the urgency factor; The result recording unit is used to record the processing result of the work order.

8. The work order distribution tracking management system based on the Internet of Things according to claim 1 is characterized in that: The work order analysis module includes a work order comparison unit, a suggestion generation unit and a feedback tracking unit; The work order comparison unit is used to compare the key feature factors of the work order information with the historical work order information, obtain the historical work order information whose similarity exceeds a threshold, and send a suggestion generation request; The suggestion generation unit is used to, after accepting the suggestion generation request, obtain the historical work order information whose similarity exceeds a threshold, further obtain the operation information of the historical work order information, and then generate a work order processing reference suggestion according to the operation information; The feedback tracking unit is used to send the generated work order processing reference suggestion to the work order allocation module in the form of a report, and obtain the processing result of the feedback from the result recording unit, regularly evaluate the execution effect of the work order processing reference suggestion based on the result processing result, and optimize the parameters of the suggestion generation unit based on the evaluation result.

9. A work order distribution tracking management method based on the Internet of Things, characterized in that: The following steps are involved: Obtain work order information automatically uploaded by IoT devices and work order information submitted by users; Extracting key characteristic factors from the acquired work order information, performing weighted calculation on the key characteristic factors through a weight algorithm, and determining a weight value of the work order; Collecting status information of work order processing units in real time, and allocating work orders according to weight values ​​of the work order information and status information of the work order processing units; By comparing the key feature factors of the work order information with the historical work order information, the work orders with a similarity exceeding a threshold are obtained, and the operation information in the historical work orders is further obtained, and the operation information in the historical work orders is optimized to generate a work order processing reference suggestion, and the work order processing reference suggestion is sent to the work order processing unit; Through the real-time data feedback of the Internet of Things device and the operation feedback of the work order processing unit, the processing progress of the work order information is continuously updated, and the processing result of the work order information is recorded.

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