A work order pushing method, device, equipment, medium and program

Abstract

A work order pushing method, device, equipment, medium and program are disclosed. The method comprises: obtaining task attribute information of a to-be-processed work order, and evaluating a task level of the to-be-processed work order according to the task attribute information; the task attribute information comprises a task type label and a task geographic position; based on the task geographic position, a candidate object matching the task type label is determined; a corresponding priority score is determined according to multi-dimensional feature information of the candidate object; a pushing strategy is determined according to the priority score, and the task attribute information and the task level are pushed to the candidate object according to the pushing strategy. The technical solution of the present application can improve the pushing efficiency and matching accuracy of work orders.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) technology, and in particular to a work order push method, apparatus, device, medium, and program. Background Technology

[0002] As electricity users continue to demand higher reliability from power users, the response speed and service quality of distribution network fault work orders have become key indicators for measuring the level of power supply services.

[0003] Currently, existing technologies generally employ a manual dispatching model, where managers manually sort fault work orders and assign them to corresponding frontline repair personnel via telephone or instant messaging tools based on information such as work order type and geographical location. However, the manual dispatching model is prone to problems such as unreasonable dispatching, cross-regional dispatching, and mismatch of personnel resources, resulting in low push efficiency and poor matching accuracy.

[0004] Therefore, there is an urgent need for a work order push method that can improve push efficiency and matching accuracy. Summary of the Invention

[0005] This invention provides a work order push method, apparatus, device, medium, and program, which can improve the push efficiency and matching accuracy of work orders.

[0006] According to a first aspect of the present invention, a work order push method is provided, comprising: Obtain the task attribute information of the pending work order, and evaluate the task level of the pending work order based on the task attribute information; the task attribute information includes task type tag and task geographical location; Based on the geographical location of the task, determine the candidate objects that match the task type tag; Based on the multidimensional feature information of the candidate objects, the corresponding priority scores are determined; Based on the priority score, a push strategy is determined, and the task attribute information and task level are pushed to the candidate objects according to the push strategy.

[0007] According to a second aspect of the present invention, a work order pushing device is provided, comprising: The acquisition module is used to acquire the task attribute information of the work order to be processed, and to evaluate the task level of the work order to be processed based on the task attribute information; the task attribute information includes task type tag and task geographical location; The first determining module is used to determine candidate objects that match the task type tag based on the task's geographical location; The second determining module is used to determine the corresponding priority score based on the multidimensional feature information of the candidate object; The push module is used to determine a push strategy based on the priority score, and push the task attribute information and the task level to the candidate objects according to the push strategy.

[0008] According to a third aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to execute the work order push method according to any embodiment of the present invention.

[0009] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions, the computer instructions being configured to cause a processor to execute and implement the work order push method according to any embodiment of the present invention.

[0010] According to a fifth aspect of the present invention, a computer program product is provided, the computer program product comprising a computer program that, when executed by a processor, implements the work order push method according to any embodiment of the present invention.

[0011] The technical solution of this invention can obtain the task attribute information of a work order to be processed, and evaluate the task level of the work order based on the task attribute information. The task attribute information includes a task type tag and a task geographical location. Then, based on the task geographical location, candidate objects matching the task type tag are determined, and a corresponding priority score is determined based on the multi-dimensional feature information of the candidate objects. Finally, a push strategy is determined based on the priority score, and the task attribute information and task level are pushed to the candidate objects according to the push strategy. This can avoid problems such as unreasonable order assignment, cross-regional order assignment, and mismatch of personnel resources, and improve the efficiency and accuracy of work order push.

[0012] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a flowchart of a work order push method provided in Embodiment 1 of the present invention; Figure 2 This is a flowchart of a work order push method provided according to Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of the structure of a work order push device according to Embodiment 3 of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device that implements the work order push method of this invention. Detailed Implementation

[0015] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0016] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0017] Example 1 Figure 1 This is a flowchart of a work order push method provided in Embodiment 1 of the present invention. This embodiment is applicable to situations where work orders are automatically pushed to suitable objects. The method can be executed by a work order push device, which can be implemented in hardware and / or software and can be configured in electronic equipment or a power distribution management system. Figure 1 As shown, the method includes: S101. Obtain the task attribute information of the work order to be processed, and evaluate the task level of the work order to be processed based on the task attribute information; the task attribute information includes task type tag and task geographical location.

[0018] Among them, pending work orders can be business work orders in the distribution network management system that require assignment to business personnel for processing. For example, pending work orders can be fault repair work orders or equipment operation and maintenance work orders. Pending work orders can originate from fault reporting information issued by the superior dispatch system, or they can be manually created by the managed object through the terminal.

[0019] Task attribute information can be information that represents the relevant attributes of the work order to be processed, and can include at least the task type label and the task's geographical location.

[0020] The task type tag can be a classification identifier used to identify the professional skill field to which the work order belongs. For example, the task type tag could be fault handling or equipment maintenance. The task geolocation can be the geographic coordinates of the on-site location corresponding to the work order, which can be automatically extracted from the work order source system through location services.

[0021] Task level can be used to measure the difficulty or urgency of executing the pending work order.

[0022] For example, after receiving or generating a work order to be processed, task attribute information such as task type tags and task geographical location can be extracted from the work order. Then, based on a preset rating logic, the task information reflected in the task attribute information can be analyzed and processed to automatically classify the work order into corresponding task levels, thereby achieving differentiated labeling of different work orders.

[0023] S102. Based on the geographical location of the task, determine the candidate objects that match the task type label.

[0024] The candidate entities can be those that have already registered in the system and possess the corresponding professional skills. It should be noted that each registered candidate entity is associated with corresponding attribute information, which may include at least the professional skill tags submitted or certified during registration and real-time status information. The real-time status information indicates whether the entity currently meets the requirements for receiving new tasks.

[0025] For example, after obtaining the task's geographical location, registered objects can be searched at the task's geographical location, and entities whose professional skill tags match the task type tags and whose current real-time status information is idle can be selected as candidates.

[0026] S103. Determine the corresponding priority score based on the multidimensional feature information of the candidate objects.

[0027] The multidimensional feature information can be a set of parameters including the service capabilities, spatial location, and current working status of the candidate object. The priority score can be used to characterize the suitability between the candidate object and the work order to be processed, thereby reflecting the relative priority of each candidate object when accepting the work order.

[0028] For example, after identifying candidate objects, features of multiple dimensions can be extracted for each candidate object to obtain multi-dimensional feature information. Then, the extracted multi-dimensional features can be aggregated and calculated according to a preset comprehensive evaluation algorithm to generate a corresponding priority score for each candidate object. The higher the priority score, the more likely the candidate object is to be recommended in the allocation of the current pending work orders.

[0029] S104. Determine the push strategy based on the priority score, and push the task attribute information and the task level to the candidate objects according to the push strategy.

[0030] In this context, the push strategy can be understood as the order and method by which task information is sent to candidate objects. The push strategy can include the message delivery order or message delivery range among candidate objects. For example, the push strategy could be to push messages in order of priority score.

[0031] For example, the candidate objects can be sorted in descending order according to their priority scores to generate a push sequence, and task messages containing task attribute information and task level can be pushed to the terminal devices of the candidate objects in sequence according to the push sequence.

[0032] The technical solution of this embodiment can obtain the task attribute information of the work order to be processed, and evaluate the task level of the work order based on the task attribute information. The task attribute information includes task type tags and task geographical location. Then, based on the task geographical location, candidate objects matching the task type tags are determined, and the corresponding priority scores are determined based on the multi-dimensional feature information of the candidate objects. Finally, based on the priority scores, a push strategy is determined, and the task attribute information and task level are pushed to the candidate objects according to the push strategy. This can avoid problems such as unreasonable order assignment, cross-regional order assignment, and mismatch of personnel resources, and improve the efficiency and accuracy of work order push.

[0033] Example 2 Figure 2 This is a flowchart of a work order push method provided in Embodiment 2 of the present invention. This embodiment can be further optimized and explained based on the above embodiments. Figure 2 As shown, the method includes: S201. Obtain the task attribute information of the work order to be processed, and evaluate the task level of the work order to be processed based on the task attribute information; the task attribute information includes task type tag and task geographical location.

[0034] S202. Using the geographical location of the task as the center, within a preset radius, search for registered objects whose professional skill tags match the task type tags and whose current status is idle, and use them as candidate objects. The preset radius coverage area can be a geographical area centered on the geographical location of the work order to be processed, with a default distance as the radius.

[0035] For example, by default, a geofence with a radius of 5 kilometers centered on the task's geographical location can be used to search for registered objects, and those whose professional skill tags match the task type tags and whose current status is available to accept orders can be selected as candidates.

[0036] S203. If the candidate object is not found within the preset radius coverage area, the preset radius coverage area is expanded by a preset step size until the candidate object is found or the preset upper limit radius is reached.

[0037] The preset step size can be the radius increased each time the search range is expanded. The preset upper limit radius can be the maximum allowable radius of the spatial search area.

[0038] For example, if no suitable candidate object is found within the initial preset radius coverage area, the search radius can be gradually expanded in preset step sizes. After each expansion, the search operation is re-executed within the updated coverage area until at least one candidate object is successfully found, or the search radius reaches the preset upper limit radius. For instance, if no candidate object is found within a preset radius coverage area of ​​5 kilometers, the search radius can be gradually expanded in preset step sizes of 2 kilometers until a candidate object is found or the preset upper limit radius of 15 kilometers is reached.

[0039] S204. Determine the corresponding priority score based on the multidimensional feature information of the candidate objects.

[0040] S205. Based on the priority score, determine the push strategy, and push the task attribute information and the task level to the candidate object according to the push strategy.

[0041] S206. Receive work order confirmation information in response to the task message, assign the pending work order to the corresponding target object, and stop pushing the task message to other candidate objects.

[0042] The work order confirmation information can be the order acceptance confirmation data submitted by the candidate after responding to the task message. The target object can be the executor who ultimately succeeds in accepting the order and is assigned to process the work order.

[0043] It should be noted that once the first candidate successfully submits the work order confirmation information, the pending work order is immediately assigned to that target candidate and the push to other candidate candidates is stopped, which can effectively avoid duplicate work orders and resource conflicts.

[0044] For example, after pushing task messages to candidate objects, response information from the corresponding terminals of each candidate object can be received in real time. When the confirmation information of the first successfully submitted work order is received, the candidate object corresponding to the confirmation information is determined as the target object. At the same time, the work order to be processed is assigned to the target object, and the pushing of task messages to other candidate objects is immediately stopped.

[0045] S207. Receive the task completion certificate information uploaded by the target object after executing the pending work order.

[0046] The task completion certificate information can be data used to prove that the pending work order has been completed, and it can include at least one of on-site image data and equipment operating parameter data.

[0047] For example, after the target object completes the processing of the pending work order at the task execution location, it can upload the on-site collected image data and / or equipment operating parameter data as task completion credentials through its terminal device.

[0048] S208. Send the task completion certificate information to the work order publishing object associated with the work order to be processed, and receive the evaluation information fed back by the work order publishing object.

[0049] The recipient of a work order can be either the customer or manager who initiated the work order. Evaluation information can be feedback data regarding the task execution status of the target recipient, such as multi-dimensional ratings of task completion quality, service attitude, and processing speed.

[0050] For example, after receiving the task completion certificate information, a service confirmation request can be sent to the terminal of the work order issuing object associated with the pending work order, and the task completion certificate information can be presented to the work order issuing object's terminal. Then, the work order issuing object can verify the completion status of this service through its terminal and submit evaluation information containing multi-dimensional scores.

[0051] S209. Update the historical service level parameters of the target object according to the basic incentive value corresponding to the task level, the task completion certificate information, and the evaluation information.

[0052] The base incentive value can be an incentive base pre-associated with the task level. The historical service level parameter can be a cumulative evaluation index used to characterize the long-term service capability level of the target object.

[0053] For example, in this embodiment, the corresponding basic incentive value can be determined first based on the task level of the work order to be processed. Then, the basic incentive value can be adjusted and calculated based on the work quality reflected in the task completion voucher information and the service satisfaction reflected in the evaluation information to obtain the actual incentive value for this task.

[0054] Then, the actual incentive value can be credited to the target's personal account, and the target's historical service level parameters can be updated by combining the actual incentive value accumulated within the preset period, evaluation statistics, and task response success rate. The updated historical service level parameters can be used as the basis for calculating the priority score of the target during subsequent work order pushes, thereby forming a positive feedback loop between service quality and push priority.

[0055] The technical solution of this invention searches for matching objects within a preset radius centered on the geographical location of the task, and expands the search range with a preset step size until candidate objects are determined. Then, priority scores are determined based on multi-dimensional feature information, and a push strategy is determined. After receiving work order confirmation information, tasks are assigned and push is stopped. Finally, task completion voucher information and evaluation information are received, and historical service level parameters are updated. This can realize intelligent work order scheduling, automated data flow, digital performance accounting, and dynamic service level, effectively improving the efficiency of work order push, matching accuracy, and resource optimization and allocation capabilities.

[0056] Based on the above embodiments, as one possible approach, assessing the task level of the work order to be processed based on the task attribute information may include: Based on the task type tag of the pending work order, determine the professional skill complexity, estimated operation time, number of required collaborators, and urgency index of the pending work order. The task level is generated by weighting the complexity of the professional skills, the estimated task time, the number of required collaborators, and the urgency level.

[0057] Among these, professional skill complexity can be used to characterize the technical difficulty of the professional skill domain required to complete the pending work order. Estimated operation time can be the expected processing time obtained based on historical data of similar work orders. The number of required collaborators can be the number of personnel needed to complete the pending work order. The urgency index can be determined based on the source of the work order and the scope of the failure's impact.

[0058] It should be noted that traditional task difficulty assessment often relies on the human experience of managers, lacks digital standards, and fixed weighting rules cannot adapt to the changing distribution of work orders in different regions and seasons. Therefore, this step addresses these issues by weighting work orders according to professional skill complexity, estimated task time, number of required collaborators, and urgency, thus classifying them into different levels.

[0059] For example, after extracting the task type tag of the work order to be processed, the professional skill complexity, estimated operation time, number of required collaborators, and urgency index corresponding to the task type tag can be parsed according to the preset task type and attribute mapping relationship.

[0060] Then, corresponding weight values ​​can be assigned to the professional skill complexity, estimated task time, number of required collaborators, and urgency level indicators. The comprehensive score is obtained by multiplying each indicator by its corresponding weight value and summing the results.

[0061] Finally, based on the preset mapping relationship between the overall score and the task level, the overall score can be converted into the corresponding task level.

[0062] Furthermore, this embodiment can also employ a machine learning model to assist in predicting the task level. The input features of the machine learning model can include fault type codes, average processing time for similar historical tasks, weather conditions, the skill structure of current on-duty personnel, and work order complaint tags. The output can include a task star rating and a corresponding suggested base score. Furthermore, the prediction results can be integrated with the comprehensive score obtained through the aforementioned weighted calculation, making the task level assessment more closely reflect the actual execution difficulty.

[0063] Based on the above embodiments, as an implementation method, the multidimensional feature information includes: the distance between the candidate object and the geographical location of the task, the historical service level parameters of the candidate object, the historical task execution evaluation records of the candidate object for the task type tag, the recent task saturation of the candidate object, and the degree of matching between the candidate object's professional skills and the task type tag.

[0064] Accordingly, determining the corresponding priority score based on the multidimensional feature information of the candidate objects may include... Determine the weight coefficients corresponding to the interval distance, the historical service level parameters, the historical task execution evaluation records, the recent task saturation, and the matching degree, respectively. The priority score is obtained by weighted summation based on the weight coefficients.

[0065] The interval distance refers to the spatial distance between the candidate's current location and the task's geographical location. The historical service level parameter is a service level identifier dynamically calculated based on the candidate's historical cumulative points, average rating stars, and task success rate. Historical task execution evaluation records are historical quality evaluation data for similar tasks performed by the candidate. Recent task saturation refers to the number of tasks or workload the candidate has undertaken in a recent period. Matching degree refers to the degree of fit between the candidate's professional skill tags and task type tags.

[0066] It should be noted that the traditional distance-first push method is prone to skill mismatch. This step, however, sets skill matching as the highest weight and takes into account multiple factors such as distance, service level, historical evaluation and saturation, to prioritize pushes to higher-quality and more suitable personnel, thus guiding the optimal allocation of resources.

[0067] For example, for each candidate, the distance between its location and the geographical location of the task can be obtained, its historical service level parameters can be retrieved, its historical task execution evaluation records for the task type tag can be retrieved, its recent task saturation can be calculated, and the matching degree between its professional skill tag and the task type tag can be calculated.

[0068] Furthermore, based on preset weight configuration rules, corresponding weight coefficients can be determined for interval distance, historical service level parameters, historical task execution evaluation records, recent task saturation, and matching degree. Among them, the matching degree can serve as a core indicator for measuring whether the business capabilities of the candidate are suitable for the task requirements, and its corresponding weight coefficient can be set to the highest value among the multi-dimensional feature information to highlight the priority of professional skill suitability in candidate selection.

[0069] Finally, the interval distance, historical service level parameters, historical task execution evaluation records, recent task saturation, and matching degree of each candidate can be multiplied by their corresponding weight coefficients, and the results of each multiplication can be summed to obtain the priority score corresponding to the candidate.

[0070] Example 3 Figure 3 This is a schematic diagram of a work order pushing device provided in Embodiment 3 of the present invention. Figure 3 As shown, the device includes: The acquisition module 301 can be used to acquire the task attribute information of the work order to be processed, and to evaluate the task level of the work order to be processed based on the task attribute information; the task attribute information includes task type tag and task geographical location. The first determining module 302 can be used to determine candidate objects that match the task type tag based on the task's geographical location; The second determining module 303 can be used to determine the corresponding priority score based on the multidimensional feature information of the candidate object; The push module 304 can be used to determine a push strategy based on the priority score, and push the task attribute information and the task level to the candidate object according to the push strategy.

[0071] The technical solution of this invention can obtain the task attribute information of a work order to be processed, and evaluate the task level of the work order based on the task attribute information. The task attribute information includes a task type tag and a task geographical location. Then, based on the task geographical location, candidate objects matching the task type tag are determined, and a corresponding priority score is determined based on the multi-dimensional feature information of the candidate objects. Finally, a push strategy is determined based on the priority score, and the task attribute information and task level are pushed to the candidate objects according to the push strategy. This can avoid problems such as unreasonable order assignment, cross-regional order assignment, and mismatch of personnel resources, and improve the efficiency and accuracy of work order push.

[0072] Optionally, the acquisition module 301 may include: an acquisition unit and an evaluation unit; The evaluation unit can be used to determine the professional skill complexity, estimated operation time, number of required collaborators, and urgency index of the work order to be processed based on the task type label of the work order to be processed; and to generate the task level by weighting the professional skill complexity, the estimated operation time, the number of required collaborators, and the urgency index.

[0073] Optionally, the first determining module 302 can be specifically used to search for registered objects whose professional skill tags match the task type tags and whose current status is idle within a preset radius coverage area, centered on the task's geographical location, as candidate objects; if no candidate object is found within the preset radius coverage area, the preset radius coverage area is expanded by a preset step size until the candidate object is found or the preset upper limit radius is reached.

[0074] Optionally, the multidimensional feature information may include: the distance between the candidate object and the geographical location of the task, the historical service level parameters of the candidate object, the historical task execution evaluation records of the candidate object for the task type tag, the recent task saturation of the candidate object, and the degree of matching between the candidate object's professional skills and the task type tag.

[0075] Correspondingly, the second determining module 303 can be specifically used to determine the weight coefficients corresponding to the interval distance, the historical service level parameters, the historical task execution evaluation records, the recent task saturation, and the matching degree, respectively; and perform a weighted summation based on the weight coefficients to obtain the priority score.

[0076] Optionally, the device may further include: a first receiving module, which can be used to receive work order confirmation information in response to the task message, assign the work order to be processed to the corresponding target object, and stop pushing the task message to other candidate objects.

[0077] Optionally, the device may further include: The second receiving module can be used to receive the task completion certificate information uploaded by the target object after executing the work order to be processed; The third receiving module can be used to send the task completion certificate information to the work order publishing object associated with the work order to be processed, and to receive the evaluation information fed back by the work order publishing object. The update module can be used to update the historical service level parameters of the target object based on the basic incentive value corresponding to the task level, the task completion certificate information, and the evaluation information.

[0078] The work order push device provided in this embodiment of the invention can execute the work order push method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0079] Example 4 Figure 4 A schematic diagram of an electronic device 400 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0080] like Figure 4As shown, the electronic device 400 includes at least one processor 401 and a memory, such as a read-only memory (ROM) 402 or a random access memory (RAM) 403, communicatively connected to the at least one processor 401. The memory stores computer programs executable by the at least one processor. The processor 401 can perform various appropriate actions and processes based on the computer program stored in the ROM 402 or loaded into the RAM 403 from storage unit 408. The RAM 403 may also store various programs and data required for the operation of the electronic device 400. The processor 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0081] Multiple components in electronic device 400 are connected to I / O interface 405, including: input unit 406, such as keyboard, mouse, etc.; output unit 407, such as various types of displays, speakers, etc.; storage unit 408, such as disk, optical disk, etc.; and communication unit 409, such as network card, modem, wireless transceiver, etc. Communication unit 409 allows electronic device 400 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0082] Processor 401 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 401 performs the various methods and processes described above, such as the work order push method.

[0083] In some embodiments, the work order push method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 400 via ROM 402 and / or communication unit 409. When the computer program is loaded into RAM 403 and executed by processor 401, one or more steps of the work order push method described above may be performed. Alternatively, in other embodiments, processor 401 may be configured to perform the work order push method by any other suitable means (e.g., by means of firmware).

[0084] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0085] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0086] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0087] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0088] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0089] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0090] In one embodiment, the present invention further includes a computer program product, which includes a computer program that, when executed by a processor, implements the work order push method of any embodiment of the present invention.

[0091] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0092] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0093] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A work order push method, characterized in that, include: Obtain the task attribute information of the work order to be processed, and evaluate the task level of the work order to be processed based on the task attribute information. The task attribute information includes task type tags and task geographic location; Based on the geographical location of the task, determine the candidate objects that match the task type tag; Based on the multidimensional feature information of the candidate objects, the corresponding priority scores are determined; Based on the priority score, a push strategy is determined, and the task attribute information and task level are pushed to the candidate objects according to the push strategy.

2. The method according to claim 1, characterized in that, The step of assessing the task level of the pending work order based on the task attribute information includes: Based on the task type tag of the pending work order, determine the professional skill complexity, estimated operation time, number of required collaborators, and urgency index of the pending work order. The task level is generated by weighting the complexity of the professional skills, the estimated task time, the number of required collaborators, and the urgency level.

3. The method according to claim 1, characterized in that, The step of determining candidate objects that match the task type tag based on the task's geographical location includes: Using the geographical location of the task as the center, within a preset radius, search for registered objects whose professional skill tags match the task type tags and whose current status is idle, and use them as candidate objects; If no candidate object is found within the preset radius coverage area, the preset radius coverage area is expanded by a preset step size until the candidate object is found or the preset upper limit radius is reached.

4. The method according to claim 1, characterized in that, The multidimensional feature information includes: the distance between the candidate object and the geographical location of the task, the historical service level parameters of the candidate object, the historical task execution evaluation records of the candidate object for the task type tag, the recent task saturation of the candidate object, and the degree of matching between the candidate object's professional skills and the task type tag; Accordingly, determining the corresponding priority score based on the multidimensional feature information of the candidate objects includes: Determine the weight coefficients corresponding to the interval distance, the historical service level parameters, the historical task execution evaluation records, the recent task saturation, and the matching degree, respectively. The priority score is obtained by weighted summation based on the weight coefficients.

5. The method according to claim 1, characterized in that, The method further includes: Receive work order confirmation information in response to the task message, assign the pending work order to the corresponding target object, and stop pushing the task message to other candidate objects.

6. The method according to claim 5, characterized in that, The method further includes: Receive the task completion certificate information uploaded by the target object after executing the pending work order; The task completion certificate information is sent to the work order publishing object associated with the work order to be processed, and the evaluation information fed back by the work order publishing object is received. The historical service level parameters of the target object are updated based on the basic incentive value corresponding to the task level, the task completion certificate information, and the evaluation information.

7. A work order pushing device, characterized in that, include: The acquisition module is used to acquire the task attribute information of the work order to be processed, and to evaluate the task level of the work order to be processed based on the task attribute information. The task attribute information includes task type tags and task geographic location; The first determining module is used to determine candidate objects that match the task type tag based on the task's geographical location; The second determining module is used to determine the corresponding priority score based on the multidimensional feature information of the candidate object; The push module is used to determine a push strategy based on the priority score, and push the task attribute information and the task level to the candidate objects according to the push strategy.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the work order push method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that are used to cause a processor to execute the work order push method according to any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the work order push method according to any one of claims 1-6.

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