A work ticket whole-process error-proofing management method and system based on digitization technology

Abstract

This invention discloses a method and system for error prevention management of work orders throughout the entire process based on digital technology, belonging to the field of digital management technology. The method includes: constructing a digital error prevention database; constructing feature vectors based on the digital error prevention database; calculating the matching score of the input text against a standard equipment library and determining the similarity; calculating the logical conflict coefficient between the filling measures and the actual equipment status, and determining whether the filling measures are omitted or invalid based on the logical conflict coefficient; calculating the current task load rate and scheduling personnel based on the task load rate; calculating the predicted waiting time based on the current queue length, average service rate, and arrival rate; and providing early warning of expiration risk based on a risk function of the remaining validity time of the work order. In existing technologies, work order management mainly relies on manpower, which suffers from problems such as lag and susceptibility to errors. Compared to existing technologies, this invention digitizes work order management, thereby effectively improving timeliness and accuracy.

Description

Technical Field

[0001] This invention relates to the field of digital management technology, and in particular to a method and system for error prevention management of work tickets throughout the entire process based on digital technology. Background Technology

[0002] With the rapid development of the power industry, society's demands for the safety and stability of power supply are constantly increasing, and power safety is receiving more and more attention. As the lifeline of modern society, the stable supply of electricity is directly related to the smooth operation of the national economy and the normal order of people's lives. The "two-ticket, three-system" is one of the most fundamental systems in the power industry's safety production guarantee system. It is an experience summarized from years of operational practice in the power industry, and the cause of any human-caused accident can be found in the implementation of the "two-ticket, three-system."

[0003] Work permits are an important component of the "two permits and three systems" framework. They are written orders authorizing work on equipment and serve as written evidence of safety measures. To prevent personnel injuries, equipment damage, and operational abnormalities during maintenance work at power production sites, on equipment, and on systems, and to ensure the safe and smooth progress of maintenance work, the work permit system must be strictly enforced, and the accuracy of work permits must be guaranteed.

[0004] The entire work permit management process generally includes: completion, issuance, receipt, authorization, execution, and termination. Traditional work permit management primarily involves security personnel reviewing, evaluating, compiling, analyzing, and assessing work permits after termination. Problems are reported and corrective measures are proposed at monthly or quarterly safety analysis meetings. This management method has the following drawbacks: First, there is a significant time lag. In the early stages of work permit processing, from filling out and issuing the permit to receiving and authorizing it, problems such as incorrect work location information, omissions or inadequate safety measures, or unqualified personnel are difficult to detect and correct in a timely manner. These potential errors are like "time bombs," which, once introduced into the work permit execution phase, could trigger safety accidents under certain conditions, posing a serious threat to personnel safety and the stable operation of equipment.

[0005] Second, there is a significant concentration of management responsibility. A single work order involves a wealth of information, including work task details, work schedule, and various safety measures. Authorizing personnel must conduct a comprehensive and meticulous review of all this information within a limited timeframe. Over-reliance on the authorization stage can lead to complacency among those involved in the initial stages of filling out and issuing the work order. They may assume that even if problems arise, the authorization personnel will handle it, thus reducing their own commitment to the accuracy of the work order and increasing the difficulty and risk of the authorization stage review.

[0006] Third: The workload is significantly heavy, requiring safety inspectors to invest a great deal of time and energy. In practice, safety inspectors often face mountains of work orders, and in order to complete the review tasks, they can only conduct spot checks, making it difficult to carry out in-depth and detailed statistical analysis of the work orders. They are unable to uncover potential problems and patterns from the large amount of work order data, and therefore cannot formulate targeted and effective management measures.

[0007] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0008] This invention provides a method for error prevention management of work orders throughout the entire process based on digital technology, including: S1: constructing a digital error prevention database based on safety production regulations, safety production processes, and problems found in daily inspections; S2: Construct feature vectors based on the digital error prevention library ; S3: Calculate the matching score between the input text and the standard device library in the SIS system. Based on matching score Determine the similarity; S4: Based on real-time data from the SIS system, calculate the logical conflict coefficient between the filling measures and the actual equipment status. Based on the logical conflict coefficient Determine whether the completed procedures are omitted or invalid; S5: Calculate the current task load factor Based on the task load rate Personnel scheduling; S6: Based on the current queue length... Average processing speed Arrival rate Calculate the predicted waiting time ; S7: Risk function based on the remaining validity time of the work order Provide early warnings about the risk of expiration.

[0009] Furthermore, feature vectors for:

[0010] It is an encoded vector; This is the semantic determination threshold; This is the time decay coefficient; Weighting based on the credibility of the rule source; This is the penalty coefficient.

[0011] Furthermore, the matching score for:

[0012] A text sequence entered by the staff; The name of the standard equipment library; For dynamic time-normalized distance; The length of the string.

[0013] Furthermore, when When it is locked, it enters a locked state; when When S4 is executed, a yellow numerical indicator will be output. when When this happens, it enters a forced interception state and outputs a red correction command.

[0014] Furthermore, the logical conflict coefficient for:

[0015] This is the current operating state vector of the device; The vector of measures to be taken for the work order; For the first One associated device; For the first The process correlation coefficient of each piece of equipment; This is an XOR operation.

[0016] Furthermore, when At that time, an "incomplete measures" alarm is triggered.

[0017] Furthermore, the current task load factor for:

[0018] Let k be the set of unfinished work tickets held by the work supervisor at the current time t; Let m be the remaining safe time for the m-th ticket; Let m be the risk level coefficient of the m-th ticket; This is the decay constant due to time urgency; It is a number decay function.

[0019] Furthermore, when At that time, if Output an alarm to the issuer; for: .

[0020] Furthermore, predict waiting time for:

[0021] The standard deviation is the random fluctuation.

[0022] This is a standard normally distributed random disturbance term.

[0023] This invention also discloses a method for error-proofing management of work orders throughout the entire process based on digital technology, including: Compared with the prior art, the present invention has the following advantages: This invention enables digital error prevention management of the work order process. Throughout the entire work order processing, from filling out, issuing, receiving, and approving the work order to execution and termination, the system performs real-time and comprehensive checks on every item of the work order based on rules in the error prevention database.

[0024] At the same time, when abnormalities occur, staff can be given intuitive and clear prompts, which can guide the person in charge to discover and correct errors in a timely manner and avoid misoperation.

[0025] Therefore, this invention effectively improves the timeliness of work permit review. Furthermore, the review process requires no manual intervention, effectively avoiding additional risks arising from staff negligence. Secondly, it effectively prevents safety supervisors from investing excessive time and energy, and the quantification of the review process and final results greatly facilitates the later identification of potential problems and patterns, thereby enabling the development of targeted and effective management measures.

[0026] Compared to conventional dropdown menus, which are prone to errors and cannot handle fuzzy input, this invention allows for natural language input and automatically calculates the best matching object, significantly reducing the input threshold and error rate.

[0027] Compared to conventional methods that rely on approvers to mentally fill in the technological logic, this invention uses real-time physical signals from the SIS (System-In-Service) to perform weighted XOR algebraic operations, transforming the "technical logic" into a "mathematical equation" and eliminating human error.

[0028] Compared to simply checking whether the quantity is greater than 1, this invention can effectively predict fatigue and overdue risks in advance by introducing factors such as time urgency and risk coefficient.

[0029] This invention can provide confidence intervals accurate to the minute, enabling precise planning of work rhythm.

[0030] This invention integrates biological clock cycles and physical time cycles to issue reminders at precise times when people are most fatigued and at the highest risk.

[0031] This invention statistically analyzes the actual error rate and system alarm rate to obtain actual feedback, and then enables automatic parameter adjustment based on the actual feedback. Attached Figure Description

[0032] Figure 1 Overall process diagram. Detailed Implementation

[0033] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0034] Example 1: This invention provides a method for error-proofing management of work orders throughout the entire process based on digital technology, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the process of this invention. It includes: S1: Constructing a digital error-proofing database based on safety production regulations, safety production processes, and problems identified during daily inspections. Specifically, the content of the safety production regulations is based primarily on important documents such as the "Electric Power Safety Work Regulations." For example, a work supervisor may not act as the supervisor for two or more work tasks during the same on-site operation period.

[0035] The content of the safety production process mainly involves extracting a series of safety production process rules related to work permits from front-line teams and technicians. For example, in the management of boiler maintenance work permits, if the work involves the operation of the boiler low-pressure water supply valve, according to the safety production process, the operating status of both the feedwater pump and the condensate pump must be considered simultaneously. At the same time, it is also connected to the plant-level monitoring information system (SIS system). The SIS system can collect various data from the production site in real time, including equipment operating parameters and status signals.

[0036] The problems identified during routine inspections mainly stem from issues discovered by safety supervisors, work supervisors, and other relevant personnel during their daily work. For example, during a work permit inspection, it was discovered that the work supervisor was simultaneously responsible for multiple hot work permits, and there was a lack of corresponding warning or alarm mechanisms, posing a significant safety hazard.

[0037] S2: Convert each item in the digital error prevention database into a feature vector. .

[0038]

[0039] in, This is a coding vector for the equipment / voltage level / operation type to which the rule applies. For example, boiler, steam turbine, 6kV, 380V.

[0040] This is the semantic threshold, used to determine the degree of matching between the input text and standard terms. Preferably, the value ranges from 0.65 to 0.95.

[0041] This is the time decay factor, indicating how strict the rule is at different times of the day. For example: nighttime work. Increased by 0.2.

[0042] As the source of the rule, the credibility weights are as follows: procedure source = 1.0, process source = 0.85, and historical problem source = 0.75.

[0043] This is a penalty coefficient added to the overall risk score of the work order when this rule is violated. Preferably, the value ranges from 1 to 10.

[0044] S3: Calculate the matching score between the input text and the standard device library in the SIS system. This allows for the determination of the similarity between the two, and thus the automatic identification of logical devices.

[0045]

[0046] in, A text sequence entered by the staff.

[0047] This is the name of the standard equipment library.

[0048] For dynamic time-normalized distance, calculate the minimum cumulative alignment distance between two variable-length string sequences.

[0049] The length of the string.

[0050] The judgment rule is set as follows: like If it does, it will enter a locked state.

[0051] like If so, proceed with the next steps and trigger a yellow numerical indicator.

[0052] like If it does, it will enter a forced interception state and issue a red correction command.

[0053] Therefore, compared to conventional drop-down menu selection, which is prone to errors and cannot handle fuzzy input, this invention allows natural language input and automatically calculates the best matching object, significantly reducing the input threshold and error rate.

[0054] S4: Based on real-time SIS data, calculate the logical conflict coefficient between the filling measures and the actual equipment status. This is to determine whether the filling measures are omitted or invalid.

[0055]

[0056] in, This is the current operating status vector of the equipment. For example: water pump: 1 running / 0 stopped; condensate pump: 1 running / 0 stopped.

[0057] The vector of measures to be taken for the work permit.

[0058] For the first This involves several related devices. For example, boiler feedwater involves feedwater pumps, condensate pumps, etc.

[0059] For the first The process correlation coefficient of each device. Preferably, it is calculated from the process flow diagram topology and takes a value of 0 to 1.

[0060] This is the XOR operation. Specifically, 0 is the same and 1 is different.

[0061] The judgment rule is set as follows: if Then calculate the additional penalty points. And the overall security risk index of the work order. Correct upwards, forcibly triggering the "incomplete measures" alarm.

[0062] Therefore, compared to the conventional approach of relying on approvers to mentally fill in the process logic, this invention uses real-time physical signals from the SIS to perform weighted XOR algebraic operations, transforming the "process logic" into a "mathematical equation" and eliminating human error.

[0063] S5: Real-time calculation of the current task load rate for each work supervisor. This allows for the prediction of the probability of the person in charge of a task being overdue or violating regulations, thus enabling advance personnel scheduling to prevent errors.

[0064]

[0065] in, Let k be the set of unfinished work tickets held by the work manager at the current time t.

[0066] Let m be the remaining safe time for the m-th ticket.

[0067] Let be the risk level coefficient for the m-th ticket. Preferably, a coefficient of 1.5 is used for hot work / confined space operations, and 1.0 is used for ordinary operations.

[0068] This is the time urgency decay constant. Preferably, it is set to 0.01.

[0069] It is a decay function; the shorter the remaining time, the closer this value is to 1, and the greater the load.

[0070] The judgment rule is set as follows: when When (physically equivalent to simultaneously holding 2 ordinary tickets or 1 high-risk ticket + 1 ordinary ticket), ratio normalization is used. ,when At that time, a warning was issued to the signatory: "The person in charge is overloaded, with a 65% probability of violation. It is recommended to replace the person in charge."

[0071] Therefore, compared to simply checking whether the quantity is greater than 1, this invention can effectively predict fatigue work and overdue risks in advance by introducing time urgency and risk coefficients.

[0072] S6: Based on the current queue length Average processing speed Arrival rate Calculate the predicted waiting time .

[0073]

[0074] in, The standard deviation represents the random fluctuation. Preferably, it is derived from historical data.

[0075] This is the random disturbance term from a standard normal distribution, used to provide confidence intervals. For example: 95% confidence interval: .

[0076] Therefore, this invention can provide confidence intervals accurate to the minute, enabling precise planning of work rhythm.

[0077] S7: Risk function for the remaining validity period of the work order A first-order Fourier expansion is performed to approximate the value and provide early warning of maturity risk.

[0078]

[0079] in, This represents the elapsed time since the moment of permission.

[0080] This refers to the total validity period of the work permit.

[0081] This is the baseline risk constant. Preferably, a value of 1 is set.

[0082] The periodic fluctuation coefficient (simulating periods of decreased attention, such as shift changes and meal times).

[0083] The slope represents the linear trend. Specifically, it is the linearly increasing risk coefficient at the end of the period, which is preferably set to 0.02.

[0084] when The calculated risk value exceeds the warning threshold. The system triggers an SMS message at this time. This usually occurs within the last quarter of the effective period and coincides with the night shift handover period, in which case the warning has the highest priority.

[0085] Therefore, this invention integrates the biological clock cycle and the physical time cycle to issue reminders at precise times when people are most fatigued and at the highest risk.

[0086] S8: Based on the actual error rate statistics for this month System alarm rate Update feature vector Current threshold .

[0087]

[0088] in, This represents the actual frequency of errors corresponding to this rule occurring in monthly operations. This value can be obtained through manual safety inspections.

[0089] This represents the frequency at which alarms were triggered last month.

[0090] This is the learning rate factor. Preferably, a value of 0.85 is set to prevent overfitting.

[0091] Therefore, this invention can automatically adjust parameters monthly based on actual feedback.

[0092] In summary, this invention achieves digital error prevention management of the work order process. Throughout the entire work order processing, from filling out, issuing, receiving, approving to execution and termination, the system performs real-time and comprehensive checks on every item of the work order based on rules in the error prevention database.

[0093] At the same time, when abnormalities occur, staff can be given intuitive and clear prompts, which can guide the person in charge to discover and correct errors in a timely manner and avoid misoperation.

[0094] Therefore, this invention effectively improves the timeliness of work permit review. Furthermore, the review process requires no manual intervention, effectively avoiding additional risks arising from staff negligence. Secondly, it effectively prevents safety supervisors from investing excessive time and energy, and the quantification of the review process and final results greatly facilitates the later identification of potential problems and patterns, thereby enabling the development of targeted and effective management measures.

[0095] To facilitate understanding of the above process, the following calculation examples are provided: Assume the feature vector of the i-th rule in the digital error prevention database for:

[0096] in, To correspond to the four specialties of [boiler, steam turbine, electrical, and chemical], but only applicable to boilers and steam turbines, the following rules are provided: .

[0097] It is a dimensionless real number, ranging from [0.65, 0.95].

[0098] It is a dimensionless real number, with a default value of 1.0; it is automatically increased to 1.2 at night (22:00-06:00) and to 1.3 on holidays.

[0099] For dimensionless real numbers, the source of the procedure is 1.0, the source of the process is 0.88, and the source of historical inspection issues is 0.76.

[0100] It is a dimensionless real number, in the range [1, 10].

[0101] The person in charge of the work enters natural language in the "Logical Device Name" field. The known input content is: Text sequence entered by staff Furnace low-pressure adjustment valve. (Character length) ) Standard equipment library name Waste heat boiler low-pressure water supply regulating valve. (Character length) ) After character-level comparison (0 for identical, 1 for different), and matrix filling using dynamic programming, the cumulative edit distance at the endpoint is calculated. (This means that 4 editing operations are required).

[0102] Calculate the matching score :

[0103] current The system determined the input to be invalid and forcibly blocked the pop-up window: "The device name similarity is only 63.6%, which is lower than the minimum threshold of 75%. Please refer to the standard name and re-enter."

[0104] Given input: Equipment under maintenance: "Boiler low-pressure water supply regulating valve". Current operating state vector of the equipment. , respectively, indicate that the feedwater pump is running and the condensate pump is running.

[0105] The proposed measures for the work permit The two numbers indicate that the water supply pump was required to be shut down, but the condensate pump was not mentioned.

[0106] Process correlation tightness coefficient = .

[0107] Calculate the logical conflict coefficient : Item 1 (Water Pump): ,product .

[0108] Item 2 (Condensate Pump): ,product .

[0109]

[0110] The current value is 0.9 > 0.5, triggering a forced latch.

[0111] This rule originates from process requirements. Additional risk penalty points:

[0112] The system pop-up window reads: "Conflict with SIS real-time signal (conflict coefficient 0.9). Condensate pump is still running. Please supplement shutdown measures. Risk penalty score +4.5." Given conditions: The person in charge currently holds two pending votes: Ticket A: Ordinary maintenance ticket, risk level coefficient Remaining safe time .

[0113] Ticket B: Level 1 Hot Work Permit, Risk Level Coefficient Remaining safe time .

[0114] Time urgency decay constant This value is derived from regression fitting of historical data.

[0115] The system's set load safety limit

[0116] Substitute each item one by one: Ticket A: .

[0117] Ticket B: .

[0118] Summation: .

[0119]

[0120] current The system simultaneously sends an alert to both the issuer and the licensor: "The current load index of the person in charge of the work is 1.911 (maximum 2.5), and the predicted probability of violation is 76.4%. It is recommended to change the person in charge or postpone part of the work order." Given input: Current queue length .

[0121] Average processing speed .

[0122] Arrival rate .

[0123] Stochastic fluctuation standard deviation .

[0124]

[0125] 95% confidence interval (random perturbation portion): Interval half-width = 1.96 × σ = 1.96 × 1.3 = 2.548.

[0126]

[0127] The system notification reads: "Currently, there are 16 people in the queue, with an estimated wait time of approximately 53 minutes (95% probability of being between 50.8 and 55.9 minutes). You will be notified via SMS 3 minutes before your turn. Please plan your time accordingly." Given input: Total validity period of work permit Permission time: 08:00.

[0128] The current time is 16:30, which is the elapsed time since the permitted time began. .

[0129] Benchmark risk constant .

[0130] Periodic fluctuation coefficient Periodic fluctuation coefficient .

[0131] linear trend slope .

[0132] Substitute each item one by one:

[0133] First, determine the phase: .

[0134] Trigonometric function values: .

[0135] Substitute each item into the calculation: Constant term: 1.0.

[0136] Cosine term: 0.9 × ( 0.22)= 0.1980.9×( 0.22)= 0.198.

[0137] Sine term: 0.7 × ( 0.98)= 0.6860.7×( 0.98)= 0.686.

[0138] Linear trend term: 0.015 × 510 = 7.65 0.015 × 510 = 7.65.

[0139] Summation:

[0140] current This triggers a Level 3 warning (the highest level).

[0141] The system automatically generated a text message: "#2 Boiler Low-Pressure System Maintenance Ticket (No. X) expires in 210 minutes. The current comprehensive risk index is 7.77 (exceeding the threshold of 1.8). Please arrange to stop work or apply for an extension immediately!" Given input: A certain rule (Ground wire connection description specification), current threshold .

[0142] Total number of work tickets last month open.

[0143] Number of system alarms triggered .

[0144] The actual number of errors discovered during manual review by safety inspectors .

[0145] Learning rate factor .

[0146]

[0147]

[0148] Interpretation of the physical meaning of the correction results: The original threshold of 0.85 meant that an alarm would only be triggered if the text similarity was below 85%, which was too lenient, resulting in an alarm rate of 30%.

[0149] The actual error rate is only 4%, indicating that most alarms (26%) are false alarms or overly strict interventions.

[0150] The new threshold of 0.102 means that starting next month, an alert will be issued as long as the similarity is below 10.2% (a significant tightening, because descriptions that are not fully standardized but are substantially safe and correct need to be overlooked).

[0151] Example 2: A work order error prevention management system based on digital technology applies the method described in Example 1.

[0152] Furthermore, it should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0153] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0154] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory (ROM) / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0155] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

[0156] It is understood that the system provided in the embodiments of the present invention corresponds to the method provided in the embodiments of the present invention, and the explanation, examples and beneficial effects of the relevant content can be referred to the corresponding parts of the above methods.

Claims

1. A method for error-proofing management of work orders throughout the entire process based on digital technology, characterized in that, Includes: S1: Constructing a digital error prevention database based on safety production regulations, safety production processes, and problems identified during daily inspections; S2: Construct feature vectors based on the digital error prevention library. ; S3: Calculate the matching score between the input text and the standard device library in the SIS system. Based on the matching score Determine the similarity; S4: Based on the real-time data of the SIS system, calculate the logical conflict coefficient between the filling measures and the actual equipment status. Based on the aforementioned logical conflict coefficient Determine whether the filling measures are omitted or invalid; S5: Calculate the current task load factor Based on the aforementioned task load rate Personnel scheduling; S6: Based on the current queue length... Average processing speed Arrival rate Calculate the predicted waiting time ; S7: Risk function based on the remaining validity time of the work order Provide early warnings about the risk of expiration.

2. The method for error-proofing management of work orders based on digital technology as described in claim 1, characterized in that, The feature vector for: The It is an encoded vector; The This is the semantic determination threshold; The This is the time decay coefficient; The Weighting based on the credibility of the rule source; The This is the penalty coefficient.

3. The method for error-proofing management of work orders based on digital technology as described in claim 1, characterized in that, The matching score for: The A text sequence entered by the staff; The The name of the standard equipment library; The For dynamic time-normalized distance; The The length of the string.

4. The method for error-proofing management of work orders based on digital technology as described in claim 2, characterized in that, when When it is locked, it enters a locked state; when When S4 is executed, a yellow numerical indicator will be output. when When this happens, it enters a forced interception state and outputs a red correction command.

5. The method for error prevention management of work orders based on digital technology as described in claim 1, characterized in that, The logical conflict coefficient for: The This is the current operating state vector of the device; The The vector of measures to be taken for the work order; The For the first One associated device; The For the first The process correlation coefficient of each piece of equipment; The This is an XOR operation.

6. The method for error-proofing management of work orders based on digital technology as described in claim 5, characterized in that, when At that time, an "incomplete measures" alarm is triggered.

7. The method for error prevention management of work orders based on digital technology as described in claim 1, characterized in that, The current task load rate for: The Let k be the set of unfinished work tickets held by the work supervisor at the current time t; The Let m be the remaining safe time for the m-th ticket; The Let m be the risk level coefficient of the m-th ticket; The This is the decay constant due to time urgency; The It is a number decay function.

8. The method for error-proofing management of work orders based on digital technology as described in claim 7, characterized in that, when At that time, if Output an alarm to the issuer; The for: 。 9. The method for error-proofing management of work orders based on digital technology as described in claim 1, characterized in that, The predicted waiting time for: The The standard deviation is the random fluctuation. The This is a standard normally distributed random disturbance term.

10. A work order full-process error prevention management system based on digital technology, characterized in that, The method described in any one of claims 1 to 9 is applied.

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