Network-free service work order processing method, system, medium and equipment

Abstract

The invention provides a network-free service work order processing method and system, a medium and equipment, and relates to the field of information processing. The method comprises the following steps: a work order server receives a work order synchronization instruction sent by a mobile terminal, and extracts corresponding work order synchronization information based on the work order synchronization instruction; the work order server compresses the work order synchronization information to obtain work order compression information, and sends the work order compression information to the mobile terminal; the mobile terminal receives input work order data information, fills the released work order compression information with the work order data information and stores the work order compression information; and the mobile terminal monitors a network connection recovery state, and if the network is recovered, the work order data stored by the mobile terminal is synchronized to the work order server. Compressed work order synchronization information is pushed to the mobile terminal in advance through the work order server, and the mobile terminal locally stores and supports off-line viewing and data filling and storage, so that the dependence of traditional work order processing on a network is thoroughly eliminated, and the operation flexibility is improved.

Description

Technical Field

[0001] This invention relates to the field of information processing, and in particular to a method, system, medium, and device for processing work orders without a network connection. Background Technology

[0002] In existing technologies, work order processing systems generally rely on a stable network environment to achieve core functions such as work order data loading, processing operation recording, and data synchronization. However, in real-world applications, engineers often need to travel to remote construction sites, underground computer rooms, enclosed factory areas, and other areas with no or weak network access to perform work order tasks such as equipment maintenance and troubleshooting. In these situations, traditional work order processing systems have significant shortcomings: On the one hand, in environments with no or weak network access, traditional systems cannot load key information such as pending work order data, corresponding operation templates, fault solutions, and parts information from the cloud. This prevents engineers from processing work orders in a timely manner, severely delaying repair progress and impacting customer service experience. On the other hand, in environments without network access, engineers need to manually record work order processing information using paper records or local temporary documents, and then re-enter it into the system when they return to a network environment. This not only increases repetitive workload but also easily leads to data omissions and errors, resulting in decreased accuracy of work order data and affecting the reliability of subsequent business statistics and data analysis.

[0003] Therefore, there is an urgent need for a technical solution that can overcome network environment limitations, support full-process work order processing in offline conditions, and ensure data accuracy and process integrity, so as to solve the application pain points of traditional work order processing systems in special scenarios. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, system, medium, and equipment for processing work orders without a network or in a weak network environment to address the problem of delays caused by the inability to synchronize work order processing in a timely manner.

[0005] A method for processing offline service work orders, the method comprising: The work order server receives the work order synchronization command sent by the mobile terminal and extracts the corresponding work order synchronization information based on the work order synchronization command. The work order synchronization information includes a list of pending work orders, associated operation templates, recommended solutions, and historical processing case data. The work order server compresses the work order synchronization information to obtain compressed work order information, and then sends the compressed work order information to the mobile terminal. The mobile terminal receives the input work order data information, fills the released work order compression information with the work order data information, and saves it; The mobile terminal monitors the network connection recovery status. If the network is restored, the work order data saved on the mobile terminal will be synchronized to the work order server.

[0006] In one preferred embodiment, the work order server performs compression processing on the work order synchronization information, including: Prepare a word block array and a prediction probability model. The word block array contains commonly used words in the work order processing scenario, and the prediction probability model is trained using sample data. The work order synchronization information to be compressed is decomposed into word blocks; Based on the prediction probability model, the probability interval of each word block is determined sequentially, and the probability space is gradually narrowed to obtain the probability data representing the entire work order information, so as to obtain the work order compression information.

[0007] In one preferred embodiment, the prediction probability model satisfies:

[0008] Where C is the target word block parameter, S is the next word block parameter after the target word block parameter C, and N is the number of context words.

[0009] In one preferred embodiment, the work order server performs compression processing on the work order synchronization information, including: The prediction probability model dynamically and adaptively adjusts itself based on the newly added word blocks and word block relationships during the work order processing.

[0010] In one preferred embodiment, the dynamic adaptive adjustment satisfies an adaptive adjustment model, which satisfies:

[0011] Where Count(C,S) is the number of times word block S appears after context C, and Count(C,X) is the number of times any word block X appears after context C.

[0012] In one preferred embodiment, the work order server receives a work order synchronization instruction sent by the mobile terminal, including: Verify the work order synchronization command. If the verification passes, verify the permissions corresponding to the work order synchronization command.

[0013] In one preferred embodiment, verifying the permissions corresponding to the work order synchronization instruction includes: Based on the corresponding permissions, obtain the work order synchronization information corresponding to the permissions.

[0014] The method described in this embodiment overcomes network environment limitations and enables offline work order processing throughout the entire process: the work order server pushes compressed work order synchronization information to the mobile terminal in advance, and the mobile terminal stores it locally and supports offline viewing, data filling and saving. This completely eliminates the dependence of traditional work order processing on the network, ensures the continuity of work order processing in remote areas, closed places and other scenarios without network, and improves the flexibility of engineers' work.

[0015] A network-free service work order processing system, comprising: The work order information extraction module is used to receive work order synchronization instructions sent by mobile terminals through the work order server, and extract corresponding work order synchronization information based on the work order synchronization instructions. The work order synchronization information includes a list of pending work orders, associated operation templates, recommended solutions, and historical processing case data. The work order information compression module is used to compress the work order synchronization information through the work order server to obtain compressed work order information, and then send the compressed work order information to the mobile terminal. The work order data receiving module is used to receive input work order data information through a mobile terminal, fill the work order data information into the work order compression information, and save it. The work order data synchronization module is used to monitor the network connection recovery status through the mobile terminal. If the network is restored, the work order data saved on the mobile terminal will be synchronized to the work order server.

[0016] The system described in this embodiment overcomes network environment limitations and enables offline work order processing throughout the entire process: the work order server pushes compressed work order synchronization information to the mobile terminal in advance, and the mobile terminal stores it locally and supports offline viewing, data filling and saving. This completely eliminates the dependence of traditional work order processing on the network, ensures the continuity of work order processing in remote areas, closed places and other scenarios without network, and improves the flexibility of engineers' work.

[0017] A storage medium containing computer-executable instructions, which, when executed by a computer processor, implement the above-described offline service work order processing method.

[0018] The storage medium described in this embodiment overcomes network environment limitations by executing the above method, enabling full-process offline work order processing: the work order server pushes compressed work order synchronization information to the mobile terminal in advance, and the mobile terminal stores it locally and supports offline viewing, data filling and saving, completely eliminating the dependence of traditional work order processing on the network, ensuring the continuity of work order processing in remote areas, closed places and other scenarios without network, and improving the flexibility of engineers' work.

[0019] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-described method for processing offline service work orders.

[0020] The electronic device described in this embodiment overcomes network environment limitations by executing the above method, enabling full-process offline work order processing: the work order server pushes compressed work order synchronization information to the mobile terminal in advance, and the mobile terminal stores it locally and supports offline viewing, data filling and saving, completely getting rid of the dependence of traditional work order processing on the network, ensuring the continuity of work order processing in remote areas, closed places and other scenarios without network, and improving the flexibility of engineers' work. Attached Figure Description

[0021] Figure 1 This is a flowchart of the offline service work order processing method disclosed in the first preferred embodiment of the present invention; Figure 2 This is a flowchart of the work order information flow processing disclosed in the first preferred embodiment of the present invention; Figure 3 This is a schematic diagram of the work order server and mobile terminal disclosed in the first preferred embodiment of the present invention; Figure 4 This is a flowchart of the detailed steps of S20 in the offline service work order processing method disclosed in the first preferred embodiment of the present invention. Figure 5 This is a schematic diagram of the network-free service work order processing system disclosed in the second preferred embodiment of the present invention; Figure 6 This is a schematic diagram of the modules of an electronic device disclosed in another preferred embodiment of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0023] It should be noted that when an element is referred to as being "set on" another element, it can be directly on the other element or there may be an intervening element. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or there may be an intervening element. The terms "vertical," "horizontal," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only possible implementation.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0025] like Figure 1 As shown, the first preferred embodiment of the present invention discloses a method for processing work orders without a network. This method generally includes communication and interaction between a work order server and a mobile terminal via a network. The method includes: S10: The work order server receives the work order synchronization instruction sent by the mobile terminal, and extracts the corresponding work order synchronization information based on the work order synchronization instruction. The work order synchronization information includes a list of pending work orders, associated operation templates, recommended solutions, and historical processing case data.

[0026] Specifically, in combination Figure 2 and Figure 3 As shown, in this step, a work order system is configured on the aforementioned work order server. This work order system is mainly used for work order data management, storage, work order job template management, work order process management, and work order solution recommendation. Operators input work order synchronization commands through mobile terminals. The mobile terminals receive the work order synchronization commands and transmit them to the work order server. The work order server receives the work order synchronization commands and extracts the corresponding work order synchronization information based on the commands.

[0027] The aforementioned work order server maintains all work order data, supports the allocation of pending work orders, status updates, and process control, and provides source support for offline data. It stores various work order operation templates, including fields to fill in, inspection items, and specifications corresponding to different products and fault types, for offline download and use. It integrates fault solutions and historical handling cases from the enterprise knowledge base, supports recommendations related to work order types, and synchronizes them locally for engineers' reference.

[0028] In this embodiment, different mobile terminals can obtain corresponding work order synchronization information based on the corresponding permissions of the verification work order synchronization command. Specifically, step S10 includes: Verify the work order synchronization command. If the verification passes, verify the permissions corresponding to the work order synchronization command. Based on the corresponding permissions, obtain the work order synchronization information corresponding to those permissions.

[0029] Specifically, in this embodiment, the aforementioned work order server first verifies the format validity, timestamp validity (error not exceeding 5 minutes), and signature integrity of the work order synchronization command. This embodiment uses the MD5 encryption algorithm to verify the command signature. If verification fails, an error code is returned, and the mobile terminal is prompted to resend. Next, if verification passes, the access permission level is determined based on the user account bound to the mobile terminal. In this embodiment, the aforementioned permission registration can be divided into engineer level, supervisor level, and administrator level. Engineers can only access the pending work orders and basic operation templates under their responsibility; supervisors can access all work orders and statistical data within their jurisdiction; and administrators can access all work order information and permission configuration data, achieving hierarchical data management.

[0030] More specifically, the above steps of this method configure the synchronization information of corresponding work orders with different permissions through an offline configuration unit. In this embodiment, the offline configuration unit supports custom offline download rules, which can set the download range of pending work orders (such as work orders within a specified time, region, or product type), related data (such as customer contact person, customer equipment information, etc.), the type of operation template to be synchronized (repair, testing, etc.), and whether to download associated solutions and parts information, etc. In this embodiment, the offline configuration unit can also configure priorities, such as allowing configuration of data download priorities, such as prioritizing the download of high-urgency work order data and frequently used operation templates, to ensure that core data is available offline first.

[0031] S20: The work order server compresses the work order synchronization information to obtain compressed work order information, and sends the compressed work order information to the mobile terminal.

[0032] In this step, step S20, based on the aforementioned offline configuration rules, specifically filters the list of pending work orders to be downloaded from the work order server, the operation template (which may include required fields, required checks, and filling specifications), the recommended solutions for the corresponding work orders, historical processing cases, and other data, compresses the data, and sends it to the corresponding mobile terminal.

[0033] In this embodiment, the mobile terminal can be a mobile phone, tablet computer, etc. Generally, these mobile terminals do not have high storage capacity. In typical work order management scenarios, over 70% of the memory is occupied by fault descriptions, repetitive solution terms, etc. Therefore, it is necessary to compress work order synchronization information, especially by compressing recommended unlocking solutions and historical processing cases. This compressed work order information is stored on the mobile terminal. Compressing the filtered data reduces local storage usage. Verification of work order synchronization commands and authorization checks ensures the security of work order data access. Only work order synchronization information within the mobile terminal's authorized scope is pushed to prevent data leakage, while also improving the accuracy of data pushes and reducing the resource consumption of invalid data.

[0034] Specifically, in combination Figure 1 and Figure 4 As shown, the above-mentioned work order server performs compression processing on the work order synchronization information, including: S21: Prepare a word block array and a prediction probability model. The word block array contains commonly used words in the work order processing scenario, and the prediction probability model is trained using sample data.

[0035] In this detailed step, firstly, the commonly used words in the work order processing scenario are divided into blocks and placed into a word block array, such as SET=[word1, word2, word3…]. The above SET is the word block array, and the above word1, word2, word3… are the commonly used word blocks in the work order processing scenario after being divided. The word blocks need to cover more than 90% of the core descriptive words in the scenario to ensure that the text decomposition is unambiguous.

[0036] For example, the above array can be located as SET=[“Power equipment”, “Transformer”, “Line”, “Frequent tripping”, “Voltage detection”, “Parts replacement”, “Overload protection”...]. In this embodiment, the commonly used words in the above work order processing scenario can be scenario-based terms such as “Power equipment”, “Transformer”, “Line”, “Frequent tripping”, “Voltage detection”, “Parts replacement”, and “Overload protection”, which are used as the extracted high-frequency words of the work order to form a basic vocabulary library.

[0037] Next, by training with historical work order data, a probability mapping from contextual word blocks to target word blocks is established. For example, the probability of "screen" followed by "broken" or "not lit" is higher than that of other words. In this embodiment, a portion of sample data is extracted for model training, and after semantic analysis, the probability of the target word block appearing after the contextual word block is obtained. The training model of the above probability satisfies:

[0038] Where C is the target word block parameter, S is the next word block parameter after the target word block parameter C, and N is the number of context words.

[0039] After training, a clear probability distribution needs to be obtained, for example: When the context C1 = "no preceding word block", P("electrical equipment") = 25%, P("line") = 20%, and P(other word blocks) = 55%; When the context C2 = "electrical equipment", P("transformer") = 40%, P("line") = 30%, and P(other words) = 30%; When the context C3 = "Power Equipment - Transformer", P("Frequent Tripping") = 50%, P("Overload Protection") = 30%, and P(Other Blocks) = 20%.

[0040] In this embodiment, the probability training model mentioned above supports dynamic updates. Specifically, after the initial version of the probability prediction model, it dynamically adapts during the compression stage based on usage. For example, when a new word block appears, or when a new word block S appears after the context word block C. The dynamic adaptive adjustment based on the newly added word blocks and word block associations during the work order processing satisfies the adaptive adjustment model, which satisfies:

[0041] Count(C,S) is the number of times word block S appears after context C, and Count(C,X) is the number of times any word block X appears after context C.

[0042] S22: Decompose the work order synchronization information to be compressed into word blocks.

[0043] In this detailed step, the work order description and solution in the work order synchronization information can be broken down into preset word blocks. For example, the work order description "server screen not lit" can be broken down into "server" and "screen not lit"; the solution "check screen connection cable and restart device" can be broken down into "check screen connection cable" and "restart device".

[0044] S23: Based on the prediction probability model, the probability interval of each word block is determined sequentially, and the probability space is gradually narrowed to obtain probability data representing the entire work order information, so as to obtain the work order compression information.

[0045] This step primarily involves probability interval compression based on a predictive probability model. Specifically, the initial probability space is first set to 0-1 as the initial range for all word block selections. For example, according to the model, the probability interval for "electrical equipment" is predicted to be 0-0.25, meaning that this word block has a 25% probability of appearing in the current scenario. Next, the predictive probability model determines the probability interval for each word block one by one, gradually narrowing the probability space to obtain the probability numbers representing the entire work order information, thus achieving data compression. For example, the sub-interval of "transformer" after "power equipment" is 0-0.1, meaning that the probability of this word block appearing in the current scenario is 10%. The sub-interval of "frequent tripping" after "power equipment - transformer" is 0-0.05, meaning that the probability of this word block appearing in the current scenario is 5%. By iterating in this way, the probability number representing the work order information can be, for example, "0.032654". By continuing to iterate in this way, the probability space is reduced once a word block is determined. Finally, the specific number in the sub-interval (such as 0.032654) uniquely corresponds to the complete word block sequence of "power equipment - transformer - frequent tripping", thus achieving the compression of "text to number".

[0046] This method utilizes the steps described above. Engineers' mobile terminals (such as phones and tablets) have limited storage resources. If 100 work orders (each containing a 50-word description and a 100-word solution) need to be downloaded offline, the uncompressed version requires approximately 15KB of storage (150 words / work order × 100 work orders × 1B / word). After compression using this technology, each work order only requires storing one 8-digit decimal (approximately 8B), and 100 work orders only require 800B, reducing storage usage by 99.4%. This allows for offline storage of thousands of work orders without frequent space cleanup. Secondly, if engineers need to synchronize work order data in weak network scenarios (such as remote construction sites with network speeds <100KB / s), the uncompressed 100 work orders require 15KB of transmission, taking approximately 1.5 seconds. After compression, only 800B needs to be transmitted, taking <0.01 seconds, significantly reducing synchronization waiting time and avoiding synchronization failures due to network interruptions. Finally, during the compression process in this step, the work order data is transformed into "meaningless probability numbers." Even if the terminal is lost, a third party that has not obtained the "word block array + prediction model" cannot restore the original text, which can prevent the leakage of sensitive data such as customer information and equipment parameters in the work order. In contrast, general compression technology only encodes the data, and the original text can be viewed directly after decompression, which has lower security.

[0047] S30: The mobile terminal receives the input work order data information, fills the released work order compression information with the work order data information, and saves it.

[0048] The mobile terminal can first download the word block array and the prediction probability model, which is used to reverse reasoning the compressed probability numbers to obtain the original text, so as to release the compressed information of the work order.

[0049] After arriving in the remote mountainous area, the engineer, due to the lack of network signal, launched the application through the offline work order entry on the mobile terminal. The application called the pre-downloaded word block array and prediction probability model. For example, it performed reverse reasoning on the received probability number "0.032654": first, it determined that the initial probability interval where 0.032654 was located corresponded to "power equipment", and then determined the corresponding "transformer" etc. in its sub-interval, and so on, to deduce and restore the complete original work order text.

[0050] Based on the equipment malfunction on site, engineers fill in work order data on their mobile terminals, including "fault occurrence time," "on-site inspection data," "replaced line connector model," and "handling result." The application then fills in this data into the corresponding work order and stores it locally on the mobile terminal with encryption.

[0051] S40: The mobile terminal monitors the network connection recovery status. If the network is restored, the work order data saved on the mobile terminal will be synchronized to the work order server.

[0052] In this step, after the engineer completes the repair work and descends the mountain or arrives at an area with better network signal, the mobile terminal detects that the network signal has returned to normal and automatically triggers a data synchronization request. The mobile terminal uploads the complete work order data (including original information and entered processing data) stored locally to the work order server. After receiving the data, the work order server updates the status and data of the corresponding work order, completing the work order processing loop. The synchronization progress is displayed in real time during the synchronization process, and the result is fed back upon successful synchronization; if synchronization fails, the reason for the failure is recorded and manual resynchronization is supported.

[0053] During synchronization, if the version numbers of local and cloud data are compared, data conflicts are automatically identified and prominently displayed to engineers, highlighting the differences between the local and cloud data and providing explanations for the conflicts. Engineers can view conflict details and manually choose to retain local data, adopt cloud data, or merge and correct the data. After confirmation, the conflict resolution and final synchronization are complete.

[0054] In this step, automatic synchronization is achieved upon network recovery to ensure data consistency: the mobile terminal monitors the network status in real time, and data synchronization is automatically triggered after the network is restored, eliminating the need for manual operation by engineers and reducing repetitive workload; during the synchronization process, the complete work order data processed locally is directly uploaded to the server to ensure the accuracy and integrity of the work order data and ensure a closed loop in the business process.

[0055] The method described in this embodiment overcomes network environment limitations and enables offline work order processing throughout the entire process: the work order server pushes compressed work order synchronization information to the mobile terminal in advance, and the mobile terminal stores it locally and supports offline viewing, data filling and saving. This completely eliminates the dependence of traditional work order processing on the network, ensures the continuity of work order processing in remote areas, closed places and other scenarios without network, and improves the flexibility of engineers' work.

[0056] like Figure 5 As shown, in the second preferred embodiment of the present invention, a network-free service work order processing system 100 is disclosed. The system 100 includes a work order information extraction module 110, a work order information compression module 120, a work order data receiving module 130, and a work order data synchronization module 140.

[0057] The aforementioned work order information extraction module 110 is used to receive work order synchronization instructions sent by mobile terminals through the work order server, and extract corresponding work order synchronization information based on the work order synchronization instructions. The work order synchronization information includes a list of pending work orders, associated operation templates, recommended solutions, and historical processing case data.

[0058] Specifically, the aforementioned work order server is configured with a work order system, which is mainly used for work order data management, storage, work order job template management, work order process management, and work order solution recommendation. Operators input work order synchronization commands through mobile terminals. The mobile terminals receive the work order synchronization commands and transmit them to the work order server. The work order server receives the work order synchronization commands and extracts the corresponding work order synchronization information based on the work order synchronization commands.

[0059] The aforementioned work order server maintains all work order data, supports the allocation of pending work orders, status updates, and process control, and provides source support for offline data. It stores various work order operation templates, including fields to fill in, inspection items, and specifications corresponding to different products and fault types, for offline download and use. It integrates fault solutions and historical handling cases from the enterprise knowledge base, supports recommendations related to work order types, and synchronizes them locally for engineers' reference.

[0060] In this embodiment, different mobile terminals can obtain corresponding work order synchronization information based on the corresponding permissions of the verification work order synchronization command. Specifically, the work order information extraction module 110 verifies the work order synchronization command; if the verification passes, it verifies the permissions corresponding to the work order synchronization command. Based on the corresponding permissions, it obtains the work order synchronization information corresponding to the permissions.

[0061] Specifically, in this embodiment, the aforementioned work order server first verifies the format validity, timestamp validity (error not exceeding 5 minutes), and signature integrity of the work order synchronization command. This embodiment uses the MD5 encryption algorithm to verify the command signature. If verification fails, an error code is returned, and the mobile terminal is prompted to resend. Next, if verification passes, the access permission level is determined based on the user account bound to the mobile terminal. In this embodiment, the aforementioned permission registration can be divided into engineer level, supervisor level, and administrator level. Engineers can only access the pending work orders and basic operation templates under their responsibility; supervisors can access all work orders and statistical data within their jurisdiction; and administrators can access all work order information and permission configuration data, achieving hierarchical data management.

[0062] More specifically, the offline configuration unit configures the synchronization information of corresponding work orders with different permissions. In this embodiment, the offline configuration unit supports custom offline download rules, allowing settings for the download scope of pending work orders (such as work orders within a specified time, region, or product type), related data (such as customer contacts, customer equipment information, etc.), the type of operation template to be synchronized (repair, testing, etc.), and whether to download associated solutions and parts information. In this embodiment, the offline configuration unit can also configure priorities, such as allowing configuration of data download priorities, such as prioritizing the download of high-urgency work order data and frequently used operation templates, ensuring that core data is available offline first.

[0063] The aforementioned work order information compression module 120 is used to compress the work order synchronization information through the work order server to obtain compressed work order information, and then send the compressed work order information to the mobile terminal.

[0064] Specifically, the aforementioned work order information compression module 120, according to the aforementioned offline configuration rules, selects the list of pending work orders to be downloaded from the work order server, operation templates (which may include required fields, required checks and filling specifications, etc.), recommended solutions for the corresponding work orders, historical processing cases, and other data, compresses the data, and sends it to the corresponding mobile terminal.

[0065] In this embodiment, the mobile terminal can be a mobile phone, tablet computer, etc. Generally, these mobile terminals do not have high storage capacity. In typical work order management scenarios, over 70% of the memory is occupied by fault descriptions, repetitive solution terms, etc. Therefore, it is necessary to compress work order synchronization information, especially by compressing recommended unlocking solutions and historical processing cases. This compressed work order information is stored on the mobile terminal. Compressing the filtered data reduces local storage usage. Verification of work order synchronization commands and authorization checks ensures the security of work order data access. Only work order synchronization information within the mobile terminal's authorized scope is pushed to prevent data leakage, while also improving the accuracy of data pushes and reducing the resource consumption of invalid data.

[0066] Specifically, the aforementioned work order information compression module 120 utilizes the work order server to compress the work order synchronization information, including: Prepare a word block array and a prediction probability model to obtain the probability of the target word block appearing after the context word block. The word block array contains commonly used words in the work order processing scenario, and the prediction probability model is trained through sample data.

[0067] First, common words in the work order processing scenario are segmented and placed into a word block array, such as SET=[word1, word2, word3…]. The above SET is the word block array, and the above word1, word2, word3… are the common word blocks in the work order processing scenario after segmentation. The word blocks need to cover more than 90% of the core descriptive words in the scenario to ensure that the text decomposition is unambiguous.

[0068] For example, the above array can be located as SET=[“Power equipment”, “Transformer”, “Line”, “Frequent tripping”, “Voltage detection”, “Parts replacement”, “Overload protection”...]. In this embodiment, the commonly used words in the above work order processing scenario can be scenario-based terms such as “Power equipment”, “Transformer”, “Line”, “Frequent tripping”, “Voltage detection”, “Parts replacement”, and “Overload protection”, which are used as the extracted high-frequency words of the work order to form a basic vocabulary library.

[0069] Next, by training with historical work order data, a probability mapping from contextual word blocks to target word blocks is established. For example, the probability of "screen" followed by "broken" or "not lit" is higher than that of other words. In this embodiment, a portion of sample data is extracted for model training, and after semantic analysis, the probability of the target word block appearing after the contextual word block is obtained. The training model of the above probability satisfies:

[0070] Where C is the target word block parameter, S is the next word block parameter after the target word block parameter C, and N is the number of context words.

[0071] After training, a clear probability distribution needs to be obtained, for example: When the context C1 = "no preceding word block", P("electrical equipment") = 25%, P("line") = 20%, and P(other word blocks) = 55%; When the context C2 = "electrical equipment", P("transformer") = 40%, P("line") = 30%, and P(other words) = 30%; When the context C3 = "Power Equipment - Transformer", P("Frequent Tripping") = 50%, P("Overload Protection") = 30%, and P(Other Blocks) = 20%.

[0072] In this embodiment, the probability training model mentioned above supports dynamic updates. Specifically, after the initial version of the probability prediction model, it dynamically adapts during the compression stage based on usage. For example, when a new word block appears, or when a new word block S appears after the context word block C. The dynamic adaptive adjustment based on the newly added word blocks and word block associations during the work order processing satisfies the adaptive adjustment model, which satisfies:

[0073] Count(C,S) is the number of times word block S appears after context C, and Count(C,X) is the number of times any word block X appears after context C.

[0074] Next, the work order synchronization information to be compressed is decomposed into word blocks. Specifically, the work order description, solution, etc. of the work order synchronization information are broken down into preset word blocks. For example, the work order description "server screen not lit" is broken down into "server" and "screen not lit"; the solution "need to check screen connection cable and restart device" is broken down into "check screen connection cable" and "restart device".

[0075] Based on the predictive probability model, the probability interval of each word block is determined sequentially, and the probability space is gradually narrowed to obtain the probability numbers representing the entire work order information, thereby achieving data compression and obtaining compressed work order information.

[0076] The main approach is to compress probability intervals based on a predictive probability model. Specifically, the initial probability space is first set to 0-1 as the initial range for all word selections. For example, the model predicts that the probability interval for "electrical equipment" is 0-0.25, meaning that this word has a 25% probability of appearing in the current scenario. Then, the predictive probability model determines the probability interval for each word in turn, gradually narrowing the probability space to obtain the probability numbers representing the entire work order information, thus achieving data compression. For example, the sub-interval of "transformer" after "power equipment" is 0-0.1, meaning that the probability of this word block appearing in the current scenario is 10%. The sub-interval of "frequent tripping" after "power equipment - transformer" is 0-0.05, meaning that the probability of this word block appearing in the current scenario is 5%. By iterating in this way, the probability number representing the work order information can be, for example, "0.032654". By continuing to iterate in this way, the probability space is reduced once a word block is determined. Finally, the specific number in the sub-interval (such as 0.032654) uniquely corresponds to the complete word block sequence of "power equipment - transformer - frequent tripping", realizing the compression of "text to number" to obtain the compressed work order information.

[0077] Engineers' mobile devices (such as phones and tablets) have limited storage resources. If 100 work orders (each containing a 50-word description and a 100-word solution) need to be downloaded offline, the uncompressed version requires approximately 15KB of storage (150 words / work order × 100 work orders × 1B / word). After compression using this technology, each work order only requires storing one 8-digit decimal (approximately 8B), and 100 work orders only require 800B, reducing storage usage by 99.4%. This allows for offline storage of thousands of work orders on the terminal without frequent space cleanup. Secondly, if engineers need to synchronize work order data in weak network scenarios (such as remote construction sites with network speeds <100KB / s), the uncompressed 100 work orders require 15KB of transmission, taking approximately 1.5 seconds. After compression, only 800B needs to be transmitted, taking <0.01 seconds, significantly reducing synchronization waiting time and avoiding synchronization failures due to network interruptions. Finally, during the compression process in this step, the work order data is transformed into "meaningless probability numbers." Even if the terminal is lost, a third party that has not obtained the "word block array + prediction model" cannot restore the original text, which can prevent the leakage of sensitive data such as customer information and equipment parameters in the work order. In contrast, general compression technology only encodes the data, and the original text can be viewed directly after decompression, which has lower security.

[0078] The aforementioned work order data receiving module 130 is used to receive input work order data information through a mobile terminal, fill the work order data information into the work order compression information, and save it.

[0079] The mobile terminal downloads a word chunk array and a prediction probability model, which are then used to reverse-engineer the compressed probability figures to obtain the original text. Specifically, After arriving in the remote mountainous area, the engineer, due to the lack of network signal, launched the application through the offline work order entry on the mobile terminal. The application called the pre-downloaded word block array and prediction probability model. For example, it performed reverse reasoning on the received probability number "0.032654": first, it determined that the initial probability interval where 0.032654 was located corresponded to "power equipment", and then determined the corresponding "transformer" etc. in its sub-interval, and so on, to deduce and restore the complete original work order text. Based on the equipment malfunction on site, engineers fill in work order data on their mobile terminals, including "fault occurrence time," "on-site inspection data," "replaced line connector model," and "handling result." The application then fills in this data into the corresponding work order and stores it locally on the mobile terminal with encryption.

[0080] The aforementioned work order data synchronization module 140 is used to monitor the network connection recovery status through the mobile terminal. If the network is restored, the work order data saved on the mobile terminal will be synchronized to the work order server.

[0081] In this step, after the engineer completes the repair work and descends the mountain or arrives at an area with better network signal, the mobile terminal detects that the network signal has returned to normal and automatically triggers a data synchronization request. The mobile terminal uploads the complete work order data (including original information and entered processing data) stored locally to the work order server. After receiving the data, the work order server updates the status and data of the corresponding work order, completing the work order processing loop. The synchronization progress is displayed in real time during the synchronization process, and the result is fed back upon successful synchronization; if synchronization fails, the reason for the failure is recorded and manual resynchronization is supported.

[0082] During synchronization, if the version numbers of local and cloud data are compared, data conflicts are automatically identified and prominently displayed to engineers, highlighting the differences between the local and cloud data and providing explanations for the conflicts. Engineers can view conflict details and manually choose to retain local data, adopt cloud data, or merge and correct the data. After confirmation, the conflict resolution and final synchronization are complete.

[0083] In this step, automatic synchronization is achieved upon network recovery to ensure data consistency: the mobile terminal monitors the network status in real time, and data synchronization is automatically triggered after the network is restored, eliminating the need for manual operation by engineers and reducing repetitive workload; during the synchronization process, the complete work order data processed locally is directly uploaded to the server to ensure the accuracy and integrity of the work order data and ensure a closed loop in the business process.

[0084] The system described in this embodiment overcomes network environment limitations and enables offline work order processing throughout the entire process: the work order server pushes compressed work order synchronization information to the mobile terminal in advance, and the mobile terminal stores it locally and supports offline viewing, data filling and saving. This completely eliminates the dependence of traditional work order processing on the network, ensures the continuity of work order processing in remote areas, closed places and other scenarios without network, and improves the flexibility of engineers' work.

[0085] A storage medium containing computer-executable instructions, which, when executed by a computer processor, implement the above-described method for processing work orders without a network connection.

[0086] like Figure 6 As shown, the electronic device 10 includes at least a processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the processor 11. The memory stores computer programs executable by at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded into the RAM 13 from storage unit 18. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0087] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as a keyboard or mouse; output unit 17, such as various types of displays or speakers; storage unit 18, such as a disk or optical disk; and communication unit 19, such as a network card, modem, or wireless transceiver. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0088] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 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), any suitable processor, controller, or microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the network-free service ticket processing method.

[0089] In some embodiments, the method is based on a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the object-based service group implementation method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured by any other suitable means (e.g., by means of firmware) to perform a network-free service order processing method.

[0090] 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.

[0091] 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 the machine or partially on the machine, or as a standalone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0092] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the appended claims.

Claims

1. A method for processing work orders without network connectivity, characterized in that, The method includes: The work order server receives the work order synchronization command sent by the mobile terminal and extracts the corresponding work order synchronization information based on the work order synchronization command. The work order synchronization information includes a list of pending work orders, associated operation templates, recommended solutions, and historical processing case data. The work order server compresses the work order synchronization information to obtain compressed work order information, and then sends the compressed work order information to the mobile terminal. The mobile terminal receives the input work order data information, fills the released work order compression information with the work order data information, and saves it; The mobile terminal monitors the network connection recovery status. If the network is restored, the work order data saved on the mobile terminal is synchronized to the work order server.

2. The method for processing work orders without network access according to claim 1, characterized in that, The work order server performs compression processing on the work order synchronization information, including: Prepare a word block array and a prediction probability model. The word block array contains commonly used words in the work order processing scenario, and the prediction probability model is trained using sample data. The work order synchronization information to be compressed is decomposed into word blocks; Based on the prediction probability model, the probability interval of each word block is determined sequentially, and the probability space is gradually narrowed to obtain the probability data representing the entire work order information, so as to obtain the work order compression information.

3. The method for processing work orders without network access according to claim 2, characterized in that, The prediction probability model satisfies: Where C is the target word block parameter, S is the next word block parameter after the target word block parameter C, and N is the number of context words.

4. The method for processing work orders without network access according to claim 2, characterized in that, The work order server performs compression processing on the work order synchronization information, and also includes: The prediction probability model dynamically and adaptively adjusts itself based on the newly added word blocks and word block relationships during the work order processing.

5. The method for processing work orders without network access according to claim 4, characterized in that, The dynamic adaptive adjustment satisfies an adaptive adjustment model, which in turn satisfies: Where Count(C,S) is the number of times word block S appears after context C, and Count(C,X) is the number of times any word block X appears after context C.

6. The method for processing work orders without network access according to claim 1, characterized in that, The work order server receives work order synchronization instructions sent by the mobile terminal, including: Verify the work order synchronization command. If the verification passes, verify the permissions corresponding to the work order synchronization command.

7. The method for processing work orders without network access according to claim 6, characterized in that, The verification of the permissions corresponding to the work order synchronization instruction includes: Based on the corresponding permissions, obtain the work order synchronization information corresponding to the permissions.

8. A network-free service work order processing system, characterized in that, include: The work order information extraction module is used to receive work order synchronization instructions sent by mobile terminals through the work order server, and extract corresponding work order synchronization information based on the work order synchronization instructions. The work order synchronization information includes a list of pending work orders, associated operation templates, recommended solutions, and historical processing case data. The work order information compression module is used to compress the work order synchronization information through the work order server to obtain compressed work order information, and then send the compressed work order information to the mobile terminal. The work order data receiving module is used to receive input work order data information through a mobile terminal, fill the work order data information into the work order compression information, and save it. The work order data synchronization module is used to monitor the network connection recovery status through the mobile terminal. If the network is restored, the work order data saved on the mobile terminal will be synchronized to the work order server.

9. A storage medium containing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, implement the offline service work order processing method as described in any one of claims 1-7.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the offline service work order processing method as described in any one of claims 1-7.

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