Intelligent printing system for business hall invoices

Abstract

The invention discloses a business hall invoice intelligent printing system, which comprises a multi-source data acquisition module used for acquiring equipment state data, user queue data and business attribute data of each printing terminal in a business hall in real time; the dynamic priority evaluation module is used for calculating an invoice issuing emergency degree quantized value E based on the business attribute data; the self-adaptive scheduling engine is used for generating a printing task distribution instruction according to the equipment state data, the user queue data and the emergency degree quantized value E; the predictive maintenance module is used for constructing a consumable consumption prediction model according to the consumable allowance historical data; the execution control module is used for driving the target printing terminal to execute the printing task; wherein the equipment state data comprises the busy and idle states of the printing terminal, the printing rate and the consumable allowance. According to the method, tasks can be intelligently scheduled, the efficiency is improved, the stability is guaranteed, the experience is optimized, and the cost is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of printing systems, in particular to a business hall invoice intelligent printing system. BACKGROUND

[0002] In the daily operation of the business hall, invoice printing is a high-frequency business, which is directly related to user experience and operational efficiency. The traditional invoice printing system has many pain points in actual application:

[0003] Lack of intelligent task scheduling: unable to dynamically distinguish task priority according to customer level, business deadline, etc., resulting in high-value customers or time-sensitive businesses being delayed due to queue congestion, low task allocation efficiency;

[0004] Passive equipment maintenance: consumable level monitoring is lagging, and the terminal may suddenly stop when the consumables are exhausted, affecting task continuity, and the manual inspection of consumables is inefficient and costly;

[0005] Poor environmental adaptability: environmental factors such as temperature and humidity can affect the efficiency of printing equipment, but existing systems do not consider environmental factors in equipment evaluation, which can lead to scheduling errors;

[0006] Weak disaster recovery capability: when a printing terminal fails, the pending tasks may be interrupted, and there is no priority mechanism for task migration, so high-priority tasks may be delayed due to disordered migration, affecting business continuity;

[0007] Poor user experience: users lack precise path guidance when searching for printing terminals in the business hall, and the queue length and terminal load are not optimized, resulting in excessive user movement and waiting time.

[0008] These problems result in the traditional system's inadequate performance in efficiency, stability, and user experience, making it difficult to meet the needs of large-scale and sophisticated operation of the business hall. Therefore, a business hall invoice intelligent printing system is proposed. SUMMARY

[0009] The present application solves the problems of the prior art by the following technical solutions, and the present application comprises:

[0010] A multi-source data acquisition module for real-time acquisition of device status data, user queue data, and business attribute data of each printing terminal in the business hall;

[0011] A dynamic priority evaluation module for calculating an invoice issuance emergency quantification value E based on business attribute data;

[0012] An adaptive scheduling engine for generating printing task allocation instructions based on device status data, user queue data, and emergency quantification value E;

[0013] A predictive maintenance module configured to construct a consumable consumption prediction model according to consumable remaining amount historical data;

[0014] An execution control module configured to drive the target printing terminal to execute a printing task;

[0015] The device state data includes printing terminal busy state, printing rate and consumable remaining amount; the service attribute data includes customer level identifier and service deadline; and the user queue data includes queue length and estimated processing time.

[0016] Further, the calculation formula of the emergency quantitative value E is:

[0017]

[0018] V is a weight coefficient based on the customer level identifier, V>1 when the customer level identifier is a large customer identifier, and V=1 when the customer level identifier is a common customer; Tq is the current queue estimated processing time in the user queue data; Dt is the difference between the current time and the service deadline; and a, β and γ are dynamic adjustment coefficients.

[0019] Further, the adjustment strategy of the dynamic adjustment coefficient is:

[0020]

[0021] is the average processing time in the user queue data, Nbusy is the number of busy terminals in the device state data, Ntotal is the total number of printing terminals, Npending is the number of pending tasks in the user queue data, Nterminal is the available terminal base, and k is a load sensitivity coefficient obtained by training historical operation data.

[0022] Further, the adaptive scheduling engine performs the following operations:

[0023] A printing terminal performance score function is constructed:

[0024]

[0025] Rs(i) is the printing rate in the device state data, Cr(i) and Ct(i) are the consumable remaining amount ratios in the device state data, Ql(i) is the associated queue length in the user queue data, and ω1, ω2 and ω3 are corresponding item weight systems.

[0026] The weight coefficient ωk is dynamically adjusted according to the emergency quantitative value E, specifically:

[0027]

[0028] ​The printing task is assigned to the printing terminal with the highest Si value calculated by the performance score function.

[0029] Further, when the predictive maintenance module constructs the consumable consumption prediction model, a time series decomposition method is adopted, and the specific process is as follows:

[0030] The consumable remaining amount historical data is decomposed into three components, and the decomposition formula is:

[0031] Cp(t)=T(t)+S(t)+R(t);

[0032] Wherein, T(t) is the trend component, S(t) is the periodic component, and R(t) is the random component;

[0033] Based on the decomposed components, consumable consumption prediction is performed, and when the predicted value Cp(t) is lower than the preset threshold value, the system automatically triggers two operations:

[0034] Starting the consumable replacement warning to prompt the staff to replenish the consumables in time;

[0035] Adjusting the task allocation strategy of the adaptive scheduling engine to reduce the allocation of new tasks to the terminal.

[0036] Further, the predictive maintenance module and the adaptive scheduling engine work cooperatively, and the specific interaction process is as follows:

[0037] The predictive maintenance module monitors the consumable warning level Lw in real time, and when Lw reaches the preset critical value, the terminal priority adjustment mechanism is triggered;

[0038] The system reduces the priority weight of the terminal in the scheduling allocation through a weight decay function, and the weight decay function formula is:

[0039]

[0040] Wherein, Wi is the adjusted terminal priority weight, W0 is the initial priority weight, μ is the decay coefficient, Lw is the current consumable warning level, and L0 is the warning level critical value;

[0041] The adaptive scheduling engine takes the adjusted Wi into the terminal performance evaluation when generating the printing task allocation instruction, and reduces the allocation of new tasks to the terminal.

[0042] Further, the system further comprises an environment monitoring module for acquiring the temperature and humidity data of the environment where each printing terminal is located in real time.

[0043] The environment monitoring module and the adaptive scheduling engine work cooperatively, specifically, an environment correction factor is introduced into the printing terminal performance score function, and the corrected performance score function is:

[0044] S i ′=S i ·(1+η·ΔT·ΔH);

[0045] wherein ΔT is the difference of the ambient temperature deviating from the standard value, ΔH is the difference of the ambient humidity deviating from the standard value, Si is the terminal performance score before correction, and η is the environmental influence coefficient.

[0046] Further, the system further comprises a path optimization module, and the specific working process of the module is as follows:

[0047] Real-time data acquisition is performed, including:

[0048] User position data, which is used to acquire the specific position information of the user in the business hall in real time;

[0049] Print terminal distribution topology information, which contains the physical layout of each print terminal, the network of the passing path in the business hall, and the relative position relationship between the terminals;

[0050] User queue data and device state data, wherein the user queue data contains the queue number of each print terminal, and the device state data contains the load factor of each print terminal.

[0051] A path optimization objective function is constructed based on the real-time data, and the specific process is as follows:

[0052]

[0053] wherein dk is the actual reachable distance calculated in combination with the print terminal distribution topology information, that is, the length of the shortest feasible path from the user position to the kth terminal based on the network of the passing path in the topology; Wk is the load factor of the kth print terminal, which is obtained from the device state data; qk is the queue number of the kth print terminal, which is obtained from the user queue data; λ is the queue number weight coefficient, which is used to balance the influence of the distance, the load, and the queue length on the path selection; and n is the total number of print terminals.

[0054] According to the calculation result of the objective function, in combination with the physical layout in the print terminal distribution topology, a specific ticket taking path containing the travel direction and the key nodes to pass through is generated, and guidance is provided to the user through screen display, voice broadcast, and the like, so as to guide the user to efficiently reach the target print terminal.

[0055] Further, the system further comprises a disaster recovery processing module, and the specific working process of the module is as follows:

[0056] The disaster recovery processing module monitors the running state of each print terminal in real time, and when a print terminal failure is detected, a task migration mechanism is triggered immediately.

[0057] A task migration priority function is constructed to determine the migration order of the tasks to be processed on the fault terminal, and the formula of the task migration priority function is:

[0058]

[0059] Pm is the task migration priority, E is the quantitative value of the urgency of the task to be migrated, Si is the performance score of the fault terminal before the fault, Sj is the performance score of the candidate migration terminal, max(Sj) is the highest performance score among all candidate migration terminals, and θ1 and θ2 are weight coefficients for balancing the influence of task urgency and candidate terminal adaptability on migration priority.

[0060] According to the calculation result of the migration priority function, the tasks to be processed on the fault terminal are migrated to the corresponding candidate terminal in the order of Pm value from high to low, and high-priority tasks are preferentially assigned to the candidate terminal with the highest Pm value, so that continuous task execution is ensured.

[0061] Compared with the prior art, the business hall invoice intelligent printing system has the following advantages: the dynamic priority evaluation module calculates the quantitative value of the urgency E, comprehensively considers the customer level, queue estimated processing time and business deadline, and combines the performance score function of the adaptive scheduling engine to realize intelligent allocation of printing tasks, preferentially process urgent tasks, balance the load of each terminal, reduce the user waiting time, and improve the overall processing efficiency; the predictive maintenance module constructs a prediction model based on historical data of consumable reserves, early warns of insufficient consumables and adjusts the task allocation strategy to reduce terminal downtime caused by consumable shortage; the disaster recovery processing module triggers the task migration mechanism when the terminal fails, preferentially migrates high-urgency tasks based on the priority function, ensures business continuity, and improves system reliability; the path optimization module combines user location, terminal distribution, queue and load data to plan the shortest feasible path for the user and provide guidance, reducing the user's moving time in the business hall; at the same time, the dynamic priority mechanism and the balanced scheduling reduce the overall user waiting time, improve the convenience and satisfaction of the user in taking the ticket; the environmental monitoring module introduces a temperature and humidity correction factor to adjust the terminal performance score, so that the system adapts to the influence of environmental changes on the equipment; each module adjusts the load, queue and other dynamic changes in real time through dynamic adjustment coefficients to realize intelligent management, and is more flexible than the traditional system; the predictive maintenance reduces the blindness of consumable replacement and reduces the labor maintenance cost; the load-balanced scheduling strategy reduces the wear and tear caused by excessive use of the terminal, prolongs the service life of the equipment; the disaster recovery mechanism reduces the business loss caused by the fault, and optimizes the overall operating cost. BRIEF DESCRIPTION OF DRAWINGS

[0062] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings. Among them:

[0063] Figure 1 is a system block diagram of the present application. DETAILED DESCRIPTION

[0064] The embodiments of the present application will be described in detail below, and the embodiments are implemented on the premise of the technical solutions of the present application, and detailed implementation manners and specific operation processes are given, but the protection scope of the present application is not limited to the following embodiments.

[0065] As shown in Figure 1 , the present embodiment provides a technical solution: an intelligent invoice printing system for a business hall, comprising:

[0066] a multi-source data acquisition module, configured to acquire device state data, user queue data and service attribute data of each printing terminal in the business hall in real time;

[0067] a dynamic priority evaluation module, configured to calculate an emergency quantization value E of invoice issuing based on the service attribute data;

[0068] an adaptive scheduling engine, configured to generate a printing task allocation instruction according to the device state data, the user queue data and the emergency quantization value E;

[0069] a predictive maintenance module, configured to construct a consumable consumption prediction model according to consumable remaining amount historical data;

[0070] an execution control module, configured to drive a target printing terminal to execute a printing task;

[0071] Among them, the device state data includes printing terminal busy state, printing rate and consumable remaining amount; the service attribute data includes customer level identifier and service deadline; and the user queue data includes queue length and estimated processing time.

[0072] The calculation formula of the emergency quantization value E is:

[0073]

[0074] Among them: V is a weight coefficient based on the customer level identifier, V>1 when the customer level identifier is a large customer identifier, and V=1 when the customer level identifier is a general customer; Tq is the current queue estimated processing time in the user queue data; Dt is the difference between the current time and the service deadline; and a, β and γ are dynamic adjustment coefficients.

[0075] By identifying the corresponding weight coefficient V through the customer level, the difference between large customers (V>1) and ordinary customers (V=1) is distinguished, ensuring that the task of high-value customers obtains higher priority, improving the service experience of large customers, combining the current queue processing time Tq, avoiding the concealment of the urgency of the task itself due to long queues, and simultaneously adapting to the changes in the overall queue processing efficiency through dynamic adjustment of the coefficient a, introducing the difference Dt between the current time and the business deadline, and strengthening the urgency of the deadline task through the exponential function, avoiding business failure due to overtime, a, β, γ are dynamic adjustment coefficients, which can be adjusted according to the real-time state of the system, so that the urgency calculation is more in line with the actual operation scene, and the scheduling flexibility is improved.

[0076] Suppose there are two invoice printing tasks to be processed in the business hall:

[0077] Task 1: The customer level is large customer (V=1.2), the current queue processing time Tq=15 minutes, and the business deadline is 40 minutes away from the current time (Dt=40);

[0078] Task 2: The customer level is ordinary customer (V=1), the current queue processing time Tq=5 minutes, and the business deadline is 10 minutes away from the current time (Dt=10).

[0079] Suppose the dynamic adjustment coefficient is a=0.5, β=0.5, and γ=0.05 (calculated based on the real-time state of the system).

[0080] Calculate the urgency quantization value E:

[0081] E1=0.5×(1.2 / 15)+0.5×e^(-0.05×40)≈0.5×0.08+0.5×e(-2)≈0.04+0.5×0.135≈0.04+0.0675=0.1075;

[0082] E2=0.5×(1 / 5)+0.5×e^(-0.05×10)≈0.5×0.2+0.5×e(-0.5)≈0.1+0.5×0.606≈0.1+0.303=0.403.

[0083] Wherein, E1 is task 1, and E2 is task 2; because E2>E1, the system determines that task 2 is more urgent, and it is preferentially allocated printing resources, which not only guarantees the timeliness of the deadline business, but also reflects the differentiated weight of large customers and ordinary customers through the V value, and task 1 is a large customer, but because the deadline is sufficient, the urgency is lower than that of the ordinary customer task near the deadline.

[0084] The adjustment strategy of the dynamic adjustment coefficient is:

[0085]

[0086] wherein is the average processing time in user queue data, Nbusy is the number of busy terminals in device status data, Ntotal is the total number of printing terminals, Npending is the number of pending tasks in user queue data, Nterminal is the base number of available terminals, k is a load sensitivity coefficient trained from historical operation data;

[0087] α is related to the average queue processing time T, when the overall queue processing time is long, α increases, the weight of the queue factor in the urgency E is strengthened, and the influence of the long queue on user experience is preferentially relieved; when the queue processing efficiency is improved, α decreases, and the queue factor is prevented from interfering with the urgency evaluation too much; β is related to the proportion of busy terminals, when the number of busy terminals in the system is small, β increases, so that the influence of the business deadline (Dt) on E is more significant, and time-sensitive business is preferentially guaranteed; when the number of busy terminals is large, β decreases, so as to avoid overemphasizing the time and ignoring the resource limitation, and prevent the system load from being unbalanced; γ is related to the ratio of pending tasks and available terminals, when the pending tasks are intensive, γ increases, so that the urgency of tasks near the deadline (Dt is small) is more obviously improved, and time-sensitive tasks are ensured not to be delayed in a high-pressure scenario; when the task pressure is small, γ decreases, so as to avoid over-emergency of ordinary time-sensitive tasks.

[0088] The adaptive scheduling engine performs the following operations:

[0089] A printing terminal performance score function is constructed:

[0090]

[0091] wherein Rs(i) is the printing speed in device status data, Cr(i) and Ct(i) are consumable remaining ratios in device status data, Ql(i) is the associated queue length in user queue data, ω1, ω2 and ω3 are corresponding item weights in the system;

[0092] The weight coefficient ωk is dynamically adjusted according to the urgency quantization value E, specifically:

[0093]

[0094] The printing task is assigned to the printing terminal with the highest value of the performance score function calculation result Si;

[0095] The performance score function Si comprehensively considers the printing speed (Rs), the consumable ratio (Cr / Ct), and the associated queue length (Ql), avoids one-sided scheduling caused by a single index, ensures the selection of a terminal with fast speed, sufficient consumables, and short queue, improves the task processing efficiency, the weight coefficient ωk is adjusted in real time according to the urgency E, the weight of the key index is strengthened for an urgent task (high E) to prioritize fast processing; for a common task (low E), each factor is more balanced to avoid resource waste, so that the scheduling strategy and the task priority are accurately matched, the task is allocated to the terminal with the highest Si, the resource occupation of the inefficient terminal is reduced, the load of each terminal is balanced, the overall waiting time is reduced, and the overall operation efficiency of the system is improved.

[0096] Suppose the urgency E of task 2 is 0.581 (high, needs to be processed in priority), and there are currently two selectable printing terminals:

[0097] Terminal A: the printing speed is 3 sheets / minute, the consumable ratio is 0.8 (sufficient), and the associated queue length is 2 people;

[0098] Terminal B: the printing speed is 2 sheets / minute, the consumable ratio is 0.6 (medium), and the associated queue length is 1 person.

[0099] Step 1: Calculate the dynamic weight coefficient ωk;

[0100] Let σ1 (printing speed reference weight) = 0.4, σ2 (consumable ratio reference weight) = 0.3, and σ4 (queue length reference weight) = 0.3, according to the formula:

[0101] ωk = (E x σk) ÷ (σ1 + σ2 + σ3)

[0102] Since σ1 + σ2 + σ3 = 1, we have:

[0103] ω1 = 0.581 x 0.4 = 0.2324 (printing speed weight, because the task is urgent, the weight is strengthened);

[0104] ω2 = 0.581 x 0.3 = 0.1743 (consumable ratio weight);

[0105] ω3 = 0.581 x 0.3 = 0.1743 (queue length weight).

[0106] Step 2: Calculate the terminal performance score Si;

[0107] Si of terminal A:

[0108] Si = ω1 x printing speed + ω2 x consumable ratio - ω3 x queue length

[0109] = 0.2324 x 3 + 0.1743 x 0.8 - 0.1743 x 2 = 0.697 + 0.139 - 0.349 = 0.487

[0110] Si of terminal B:

[0111] Si = 0.2324 x 2 + 0.1743 x 0.6 - 0.1743 x 1 = 0.465 + 0.105 - 0.174 = 0.396

[0112] Si of terminal A (0.487) is higher than that of terminal B (0.396), so task 2 is assigned to terminal A. This assignment not only takes advantage of A's faster printing rate (meeting the speed requirement of high urgency tasks), but also takes into account its sufficient consumables and acceptable queue length, achieving efficient scheduling.

[0113] When the predictive maintenance module constructs the consumable consumption prediction model, it uses the time series decomposition method, and the specific process is as follows:

[0114] The consumable remaining amount historical data is decomposed into three components, and the decomposition formula is:

[0115] Cp(t) = T(t) + S(t) + R(t);

[0116] Wherein, T(t) is the trend component, S(t) is the periodic component, and R(t) is the random component;

[0117] Based on the decomposed components, consumable consumption prediction is carried out, and when the predicted value Cp(t) is lower than the preset threshold, the system automatically triggers two operations:

[0118] Start consumable replacement warning to prompt staff to replenish consumables in time;

[0119] Adjust the task allocation strategy of the adaptive scheduling engine to reduce the allocation of new tasks to the terminal;

[0120] Decomposing the consumable remaining amount historical data into trend component (long-term consumption trend), periodic component (such as daily / weekly consumption pattern) and random component (sudden fluctuation) can more accurately predict future consumable remaining amount, and give early warning when consumables are about to run out (below the preset threshold), avoiding terminal downtime due to sudden depletion of consumables and reducing the risk of task interruption. When the consumables are predicted to be insufficient, the task allocation strategy of the adaptive scheduling engine is adjusted in time to reduce the allocation of new tasks to the terminal, avoiding the failure of new tasks due to lack of consumables, ensuring the continuity of task processing, and at the same time, gaining time for consumable replenishment. Compared with the traditional passive maintenance mode of dealing with consumable depletion, this method changes from passive to active through predictive maintenance, reduces the loss of operation interruption caused by consumable problems, reduces the blindness of manual inspection, and improves the maintenance efficiency;

[0121] Suppose there is a printing terminal in the service hall, and its consumable remaining amount historical data is decomposed by time series:

[0122] The trend component T(t) shows that the consumables are linearly decreasing in the long term (the daily consumption is stable);

[0123] The periodic component S(t) shows that the consumption peak is from 10:00 to 16:00 every day (corresponding to the peak of customer flow in the service hall);

[0124] The random component R(t) has little effect (no significant sudden consumption).

[0125] The system predicts based on the decomposed components: within the next 1 hour, the predicted value Cp(t) of the consumable remaining amount of the terminal will be lower than the preset threshold (such as only 10% left).

[0126] At this time, the system triggers two operations:

[0127] Start the consumable replacement warning, and notify the staff through the background that the consumables of terminal X will be insufficient within 1 hour, please replenish in time;

[0128] The adaptive scheduling engine adjusts the task allocation strategy to reduce the allocation of new tasks to the terminal. For example, the urgent task 2 mentioned in the foregoing (which needs to be processed first) may be allocated to the terminal (if its performance score is higher), but due to the consumable warning, the scheduling engine will allocate it to other terminals with sufficient consumables (such as terminal A), to avoid task interruption due to insufficient consumables.

[0129] Through this process, not only is the task failure caused by consumable shortage avoided in advance, but also the staff is given buffer time to replenish consumables, ensuring the continuity of system operation.

[0130] The predictive maintenance module works with the adaptive scheduling engine, and the specific interaction process is as follows:

[0131] The predictive maintenance module monitors the consumable warning level Lw in real time, and when Lw reaches the preset critical value, the terminal priority adjustment mechanism is triggered;

[0132] The system reduces the priority weight of the terminal in scheduling allocation through a weight decay function, and the weight decay function formula is:

[0133]

[0134] Where Wi is the adjusted terminal priority weight, W0 is the initial priority weight, μ is the decay coefficient, Lw is the current consumable warning level, and L0 is the warning level critical value;

[0135] The adaptive scheduling engine takes the adjusted Wi into account in terminal performance evaluation when generating printing task allocation instructions, reducing the allocation of new tasks to the terminal;

[0136] The above process realizes that when the consumable early warning level Lw reaches the preset critical value, the priority weight of the terminal is dynamically reduced by the weight attenuation function, and the attenuation amplitude is positively correlated with the degree of exceeding the critical value of the early warning level, so that the priority adjustment is more suitable for the actual consumable tension state, and the one-size-fits-all rigid regulation is avoided.

[0137] Strengthen the closed-loop management of early warning to scheduling: The early warning of the predictive maintenance module is not only a simple prompt, but also directly triggers the adjustment of the scheduling strategy, so that the early warning information of the consumable shortage is converted into a constraint condition for task allocation in real time, forming a closed loop of early warning, regulation and obstacle avoidance, and avoiding task failure or interruption caused by the terminal with consumables running out of stock being assigned a large number of tasks.

[0138] Guarantee the continuity of task processing: By reducing the allocation of new tasks to the terminal with consumables, a buffer time is reserved for consumable replenishment, and tasks are directed to other terminals with sufficient consumables, ensuring that the overall task processing is not severely affected by the consumable problem of a single terminal, maintaining stable operation of the system, and reducing the risk of operation interruption caused by consumable shortage.

[0139] Optimize resource coordination efficiency: Realize the deep coordination between predictive maintenance and scheduling engine, avoid the contradiction that a large number of orders are still dispatched after early warning when the two modules work independently, improve the intelligent level of system module linkage, and make the resource allocation more accurately adapt to the actual state of the equipment.

[0140] Further, the system further comprises an environment monitoring module for acquiring real-time temperature and humidity data of the environment where each printing terminal is located;

[0141] The environment monitoring module and the adaptive scheduling engine work cooperatively, specifically, an environment correction factor is introduced into the printing terminal efficiency score function, and the corrected efficiency score function is:

[0142] S i ′=S i ·(1+η·ΔT·ΔH);

[0143] Wherein, ΔT is the difference between the environmental temperature and the standard value, ΔH is the difference between the environmental humidity and the standard value, Si is the terminal efficiency score before correction, and η is the environmental influence coefficient.

[0144] Temperature and humidity are key factors affecting the stability of printing equipment. For example, high temperature and high humidity may cause paper jam and printing speed reduction. By using the environment correction factor, the terminal efficiency score Si can be dynamically adjusted according to the degree of deviation of the actual temperature and humidity from the standard value, so as to avoid misjudgment of the actual capacity of the terminal due to abnormal environment, such as a terminal with high speed but low actual efficiency due to high temperature and humidity. The corrected score is more consistent with the real state.

[0145] Enhance the environmental adaptability of the scheduling strategy: The environmental monitoring module collects data in real time, enabling the adaptive scheduling engine to flexibly adjust task allocation according to environmental changes. For example, when the temperature and humidity of the environment where a terminal is located deviate significantly from the standard, its revised performance score will decrease, and the scheduling engine will reduce the allocation of tasks to that terminal, avoiding task processing delays or failures due to environmental factors, and ensuring the stability of task processing.

[0146] Guarantee print quality and equipment life: By preferentially allocating tasks to terminals in a suitable environment, high-load operation of equipment in harsh environments is reduced, the risk of equipment failure due to environmental problems is reduced, indirectly extending the life of the equipment, and avoiding print quality problems caused by environmental factors, improving user experience.

[0147] Suppose there are two terminals in the business hall, continuing the previous task 2 scenario, task 2 has an urgency E = 0.581, which needs to be allocated preferentially:

[0148] Terminal A: Before correction, performance score Si = 0.487, print rate 3 sheets per minute, sufficient supplies, 2 people in the queue, but the temperature at its location is 3°C higher than the standard value (ΔT = 3), and the humidity is 20% higher than the standard value (ΔH = 0.2);

[0149] Terminal B: Before correction, performance score Si = 0.396, print rate 2 sheets per minute, supplies are moderate, 1 person in the queue, the temperature and humidity of the environment meet the standard, ΔT = 0, ΔH = 0.

[0150] The environmental influence coefficient η is set to -0.01, the greater the deviation of temperature and humidity, the more significant the negative impact on performance.

[0151] According to the correction formula Si' = Si · (1 + η · ΔT · ΔH), we have:

[0152] Terminal A's revised score:

[0153] Si' = 0.487 × [1 + (-0.01) × 3 × 0.2] = 0.487 × (1 - 0.006) = 0.487 × 0.994 ≈ 0.484;

[0154] Terminal B's revised score: Si' = 0.396 × [1 + (-0.01) × 0 × 0] = 0.396 × 1 = 0.396.

[0155] At this time, the terminal A still has a higher score than terminal B after the correction, and task 2 is still assigned to A, but the score is slightly reduced due to environmental influence; if the temperature and humidity of terminal A deviate more, such as ΔT = 10 and ΔH = 0.5, then the corrected Si' = 0.487 * [1 + (-0.01) * 10 * 0.5] = 0.487 * 0.95 ≈ 0.463, and if there is another environmentally suitable terminal C (corrected Si' = 0.47) at this time, task 2 will be assigned to C, avoiding task delay due to environmental influence on terminal A, and reflecting the accuracy of environmental adaptation.

[0156] Further, the system further comprises a path optimization module, and the specific workflow of the module is as follows:

[0157] Real-time data acquisition is performed, including:

[0158] User position data, real-time acquisition of specific position information of the user in the business hall;

[0159] Print terminal distribution topology information, including the physical layout of each print terminal, the passageway network (such as the distribution of passageways, the location of obstacles) in the business hall, and the relative position relationship between terminals;

[0160] User queue data and device state data, wherein the user queue data includes the number of queues of each print terminal, and the device state data includes the load factor of each print terminal.

[0161] Based on the real-time data, a path optimization objective function is constructed, specifically:

[0162]

[0163] Wherein, dk is the actual reachable distance calculated in combination with the print terminal distribution topology information (that is, the shortest feasible path length from the user's position to the kth terminal based on the passageway network in the topology); Wk is the load factor of the kth print terminal (obtained from the device state data); qk is the number of queues of the kth print terminal (obtained from the user queue data); λ is the queue number weight coefficient (used to balance the influence of distance, load and queue length on path selection); n is the total number of print terminals;

[0164] According to the calculation result of the objective function, in combination with the physical layout in the print terminal distribution topology, a specific ticket taking path including the direction of travel and the key nodes to be passed through is generated, and guidance is provided to the user through screen display, voice broadcast and the like, guiding the user to efficiently reach the target print terminal;

[0165] By collecting real-time user location, terminal physical layout, terminal queue number and load factor, etc. dynamic data, it ensures that the path planning does not rely on static map, but combines the actual traffic conditions and terminal state (such as low load, short queue) in the business hall to generate the truly shortest and efficient feasible path, avoiding users walking the wrong way.

[0166] Balancing distance and waiting, reducing overall time consumption: the path optimization objective function comprehensively considers the actual reachable distance of users to terminals, terminal load factor and queue number, and balances the influence of the three through the weight coefficient λ. For example, although a certain terminal is close, but the queue is extremely long, the function will tend to select a terminal that is slightly far away but has a short queue and low load, avoiding the situation that users wait for a long time in a short distance, and reducing the total time consumption of users from departure to invoice taking.

[0167] The generated path contains the direction of travel, the key nodes passed through, and guides the user through screen display, voice broadcast and other ways, especially for users who are not familiar with the layout of the business hall, which can significantly reduce the difficulty of finding the way, reduce the time of users in the business hall, and improve the smoothness and satisfaction of the ticket taking process.

[0168] By guiding users to terminals with low load and short queue, the pressure of people flow of each terminal is indirectly balanced, avoiding the situation that some terminals are overcrowded due to superior location, and some terminals are idle due to relatively poor location, improving the utilization efficiency of the overall space and equipment of the business hall.

[0169] The system also includes a disaster recovery processing module, and the specific workflow of the module is as follows:

[0170] The disaster recovery processing module monitors the running state of each printing terminal in real time, and when it detects that a printing terminal fails, it immediately triggers the task migration mechanism;

[0171] A task migration priority function is constructed to determine the migration order of the tasks to be processed on the failed terminal, and the formula of the task migration priority function is:

[0172]

[0173] Pm is the task migration priority; E is the quantitative value of the urgency of the task to be migrated; Si is the performance score of the failed terminal before failure; Sj is the performance score of the candidate migration terminal; max(Sj) is the highest performance score among all candidate migration terminals; θ1 and θ2 are weight coefficients, used to balance the influence of task urgency and candidate terminal adaptability on migration priority;

[0174] According to the calculation result of the migration priority function, the tasks to be processed on the fault terminal are migrated to the corresponding candidate terminal in the order of Pm value from high to low, the high priority task is preferentially assigned to the candidate terminal with the highest Pm value, and the continuous execution of the task is ensured; the task migration mechanism is triggered immediately when the terminal fails, avoiding the interruption of the tasks to be processed on the fault terminal, ensuring the business does not terminate, especially for urgent tasks (such as near deadline business) that can be preferentially guaranteed to be processed, reducing the risk of business failure caused by failure. Precise migration according to priority: the task migration priority function Pm considers the task urgency (E) and the adaptability of the candidate terminal (the ratio of the efficiency of the fault terminal to the highest efficiency of the candidate terminal), so that the task with high urgency and strong adaptability is preferentially migrated, avoiding the waste of resources or delay of high priority task caused by disordered migration, and improving the migration efficiency; through real-time monitoring and rapid migration, the influence of single terminal failure on the overall system is reduced, the system risk resistance is improved, the user waiting time extension or business loss caused by failure is reduced, and the stable operation of the system is ensured.

[0175] Suppose that terminal A in the business hall suddenly fails, and there are two tasks to be processed on the terminal:

[0176] Task 2: Urgency E = 0.581, higher, near deadline;

[0177] Task 3: ordinary task, urgency E = 0.2, non-urgent.

[0178] The efficiency score Si of terminal A before failure is 0.487; the candidate migration terminals are B and C, and the highest efficiency score max(Sj) of the candidate terminal is 0.5 (the efficiency score of terminal C);

[0179] Let the weight coefficient θ1 = 0.6 (task urgency weight) and θ2 = 0.4 (adaptability weight), according to the migration priority function Pm = θ1·E + θ2·(Si / max(Sj)):

[0180] The Pm of task 2 is 0.6×0.581 + 0.4×(0.487 / 0.5) ≈ 0.349 + 0.4×0.974 ≈ 0.349 + 0.390 = 0.739;

[0181] The Pm of task 3 is 0.6×0.2 + 0.4×(0.487 / 0.5) ≈ 0.12 + 0.390 = 0.51.

[0182] Because the Pm of task 2 (0.739) is higher than that of task 3 (0.51), the system preferentially migrates task 2 to the terminal C with the highest efficiency, and task 3 is subsequently migrated to terminal B.

[0183] The task 2 with high emergency after failure is not interrupted and is processed quickly on the adaptive terminal, ensuring the service timeliness; the ordinary task is orderly migrated, avoiding resource confusion, and reflecting the accuracy and efficiency of the disaster recovery mechanism.

[0184] Finally, it should be pointed out that the above detailed description of the method and device is only an embodiment, and those skilled in the art can modify the embodiment in different ways without departing from the scope of the present application.

Claims

1. A smart invoice printing system for business halls, characterized in that, include: The multi-source data acquisition module is used to acquire real-time device status data, user queue data, and business attribute data of each printing terminal in the business hall; The dynamic priority assessment module is used to calculate the urgency metric E for invoice issuance based on business attribute data; An adaptive scheduling engine is used to generate print task allocation instructions based on device status data, user queue data, and emergency metric E. The predictive maintenance module is used to build a consumable consumption prediction model based on historical data of consumable reserves. The execution control module is used to drive the target printing terminal to perform printing tasks; The device status data includes the printer terminal's busy / idle status, printing rate, and remaining consumables. The business attribute data includes customer level identifier and business deadline; the user queue data includes queue length and estimated processing time.

2. The intelligent invoice printing system for business halls as described in claim 1, characterized in that: The formula for calculating the emergency quantification value E is as follows: Where: V is the weighting coefficient based on the customer level identifier. When the customer level identifier is a large customer, V>1, and when the customer level identifier is a regular customer, V=1; Tq is the estimated processing time of the current queue in the user queue data; Dt is the difference between the current time and the business deadline; α, β and γ are dynamic adjustment coefficients.

3. The intelligent invoice printing system for business halls as described in claim 2, characterized in that... The adjustment strategy for the dynamic adjustment coefficient is as follows: in Nbusy is the average processing time in the user queue data, Nbusy is the number of busy terminals in the device status data, Ntotal is the total number of printing terminals, Npending is the number of pending tasks in the user queue data, Nterminal is the base number of available terminals, and k is the load sensitivity coefficient, which is obtained by training from historical operational data.

4. The intelligent invoice printing system for business halls as described in claim 3, characterized in that: The adaptive scheduling engine performs the following operations: Construct a printer terminal performance scoring function: Where Rs(i) is the printing rate in the device status data, Cr(i) and Ct(i) are the consumable balance ratio in the device status data, Ql(i) is the associated queue length in the user queue data, and ω1, ω2 and ω3 are the corresponding item weight system; The weighting coefficient ωk is dynamically adjusted based on the emergency quantification value E, specifically as follows: The print job is assigned to the print terminal with the highest Si value calculated by the performance rating function.

5. The intelligent invoice printing system for business halls as described in claim 4, characterized in that: When constructing the consumable consumption prediction model, the predictive maintenance module uses the time series decomposition method, and the specific process is as follows: The historical data on consumable inventory is decomposed into three components, and the decomposition formula is as follows: Cp(t) = T(t) + S(t) + R(t); Where T(t) is the trend component, S(t) is the periodic component, and R(t) is the random component; Based on the decomposed components, consumable consumption is predicted. When the predicted value Cp(t) is lower than a preset threshold, the system automatically triggers two operations: Activate the consumables replacement alert to remind staff to replenish consumables in a timely manner; Adjust the task allocation strategy of the adaptive scheduling engine to reduce the number of new tasks assigned to this terminal.

6. The intelligent invoice printing system for business halls as described in claim 5, characterized in that: The predictive maintenance module works in conjunction with the adaptive scheduling engine, and the specific interaction process is as follows: The predictive maintenance module monitors the consumable warning level Lw in real time. When Lw reaches the preset threshold, it triggers the terminal priority adjustment mechanism. The system reduces the terminal's priority weight in scheduling and allocation through a weight decay function. The formula for the weight decay function is: Where Wi is the adjusted terminal priority weight, W0 is the initial priority weight, μ is the attenuation coefficient, Lw is the current consumable warning level, and L0 is the warning level threshold. When generating print task assignment instructions, the adaptive scheduling engine incorporates the adjusted Wi into the terminal performance evaluation, reducing the number of new tasks assigned to that terminal.

7. The intelligent invoice printing system for business halls as described in claim 6, characterized in that: The system also includes an environmental monitoring module, which is used to acquire real-time temperature and humidity data of the environment in which each printing terminal is located; The environmental monitoring module works in conjunction with the adaptive scheduling engine, specifically by introducing an environmental correction factor into the printing terminal performance scoring function. The corrected performance scoring function is as follows: S i ′=S i ·(1+η·ΔT·ΔH); Where ΔT is the difference between the ambient temperature and the standard value, ΔH is the difference between the ambient humidity and the standard value, Si is the terminal performance score before correction, and η is the environmental impact coefficient.

8. The intelligent invoice printing system for business halls as described in claim 7, characterized in that: The system also includes a path optimization module, the specific workflow of which is as follows: Real-time data acquisition includes: User location data, to obtain the user's specific location information in the business hall in real time; Printing terminal distribution topology information, including the physical layout of each printing terminal, the network of access paths within the business hall, and the relative positional relationships between terminals; User queue data and device status data, where user queue data includes the number of users in the queue for each printing terminal, and device status data includes the load factor for each printing terminal. The path optimization objective function is constructed based on real-time data, specifically as follows: Where dk is the actual reachable distance calculated based on the topology information of the printing terminal distribution; Wk is the load factor of the k-th printing terminal; qk is the number of people in the queue of the k-th printing terminal; λ is the weighting coefficient of the number of people in the queue; and n is the total number of printing terminals. Based on the calculation results of the objective function and the physical layout in the topology of the printing terminal distribution, a specific ticket collection path is generated, including the direction of travel and key nodes along the way. The path is then guided to the user through screen display, voice broadcast, and other means to efficiently reach the target printing terminal.

9. The intelligent invoice printing system for business halls as described in claim 8, characterized in that: The system also includes a disaster recovery module, the specific workflow of which is as follows: The disaster recovery module monitors the operating status of each printing terminal in real time. When a printing terminal failure is detected, the task migration mechanism is immediately triggered. A task migration priority function is constructed to determine the migration order of tasks to be processed on the faulty terminal. The formula for the task migration priority function is as follows: Pm is the task migration priority; E is the urgency metric of the task to be migrated; Si is the performance score of the faulty terminal before the failure; Sj is the performance score of the candidate migration terminal; max(Sj) is the highest performance score among all candidate migration terminals; θ1 and θ2 are weighting coefficients.

10. The intelligent invoice printing system for business halls as described in claim 9, characterized in that: Based on the migration priority function calculation results, tasks to be processed on the faulty terminal are migrated to the corresponding candidate terminals in descending order of Pm value. High-priority tasks are assigned to the candidate terminal with the highest Pm value to ensure continuous task execution.

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