Bill of material generation method and device of intelligent lock, computer equipment and medium

Abstract

The invention relates to the technical field of intelligent locks, and discloses a bill of material generation method and device of an intelligent lock, computer equipment and a medium, the method comprises the following steps: obtaining service information of the intelligent lock in a preset range of a service system, the service information comprising repair data and installation data of a plurality of lock models; determining an initial bill of materials according to the repair data, and querying historical fault data of each lock model; on the basis of the installation data and the historical fault data, predicting accessory information required by each lock model in a future time period to obtain a predicted bill of materials; and constructing a target bill of material of the service system by using the initial bill of material and the predicted bill of material. According to the invention, the problems that the intelligent lock accessories are violently fluctuated due to the interference of multiple factors, the coordination of reasonable resource allocation and user experience guarantee is difficult to realize, and the requirements of maintenance accessory inventory management on accuracy and high-efficiency technical indexes cannot be met are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart locks, and in particular to a method, device, computer equipment and medium for generating a bill of materials for a smart lock. Background Art

[0002] With the booming development of the smart home market, smart locks are increasingly used in the field of home security, and their popularity continues to rise. In the use of smart locks, various faults such as battery exhaustion, circuit board damage, sensor failure, etc. are inevitable, which urgently requires efficient and timely maintenance services to ensure. In order to ensure the timeliness of maintenance, the parts required for maintenance are usually sent to the corresponding service providers in advance, and the inventory management of maintenance parts has become a key link.

[0003] The automatic replenishment algorithms for products currently on the market usually determine the safety stock based on circulation forecasts and perform manual forecasts to initialize the inventory. However, there are significant differences between the sales of smart lock repair parts and ordinary products. The calculation of their safety stock is affected by many factors, such as lock model accessories, lock failure rate, whether it is a batch problem, the model and quantity of locks in the responsible area, the installation age of the locks, etc., and the process is more complicated. If the traditional commodity automatic replenishment algorithm is used, the inventory level will fluctuate violently. When there is too much inventory, it will occupy a lot of funds and increase warehousing costs; when there is insufficient inventory, it will extend customer waiting time and reduce customer satisfaction. It is difficult to meet the requirements of accuracy and efficiency for smart lock repair parts inventory management, and it is impossible to ensure a good user experience while making reasonable use of funds. Summary of the invention

[0004] In view of this, an embodiment of the present invention provides a method, device, computer equipment and medium for generating a bill of materials for a smart lock, so as to solve the problem that the inventory level of smart lock accessories fluctuates violently due to interference from multiple factors, making it difficult to achieve the coordination of reasonable resource allocation and user experience assurance, and failing to meet the requirements of technical indicators of accuracy and efficiency for maintenance parts inventory management.

[0005] In a first aspect, an embodiment of the present invention provides a method for generating a bill of materials for a smart lock, the method comprising:

[0006] Obtain service information of smart locks within a preset range of the service system, wherein the service information includes repair data and installation data of multiple lock models;

[0007] Determine an initial bill of materials based on the repair data, and query historical failure data for each of the lock models;

[0008] Based on the installation data and historical failure data, the accessory information required for each lock model in the future time period is predicted to obtain a predicted bill of materials;

[0009] The target bill of materials of the service system is constructed using the initial bill of materials and the predicted bill of materials.

[0010] Further, determining an initial bill of materials according to the repair data includes:

[0011] Extracting the failure events of each lock model in the repair report data;

[0012] Determine the type of first accessories to be replenished and the corresponding quantity of first accessories according to the fault event;

[0013] An initial bill of materials is constructed based on the first accessory type and the corresponding first accessory quantity.

[0014] Furthermore, the querying of the historical fault data of each of the lock models includes:

[0015] Query the historical failure events of the lock model in the historical database;

[0016] Calculate the historical failure rate of the lock model in each time period according to the historical failure events;

[0017] Based on the historical failure rate of the lock model in each time period, historical failure data of the lock model is constructed.

[0018] Furthermore, the accessory information required for each lock model in a future time period is predicted based on the installation data and historical failure data to obtain a predicted bill of materials, including:

[0019] Analyze the historical fault data to obtain the fault probability distribution of each lock model;

[0020] Predicting potential failure events of the lock model in a future time period based on the failure probability distribution and the installation data;

[0021] A predicted bill of materials for the service system is generated using the potential failure events.

[0022] Further, the generating a predicted bill of materials for the service system using the potential failure event includes:

[0023] Obtaining the association between the preset fault event and the preset accessory type;

[0024] Determining a second accessory type associated with the potential fault event according to the association relationship;

[0025] Traversing the preset configuration list of the lock model to determine the number of second accessories associated with the second accessory type;

[0026] A forecast bill of materials is constructed based on the second accessory type and the corresponding second accessory quantity.

[0027] Furthermore, the step of constructing a target bill of materials for the service system using the initial bill of materials and the predicted bill of materials includes:

[0028] Merging the initial bill of materials and the predicted bill of materials to obtain a merged bill of materials;

[0029] Sort the various accessory types in the fusion material list according to a priority allocation rule to obtain an accessory supplement sequence;

[0030] The current resource status of the service system is obtained, and the types of accessories to be replenished and the corresponding replenishment quantities are determined in the accessory replenishment sequence according to the resource status to obtain a target bill of materials.

[0031] Furthermore, after constructing the target bill of materials of the service system using the initial bill of materials and the predicted bill of materials, the method further includes:

[0032] Check whether there is an updated model in the lock model list;

[0033] If there is an updated model in the lock model list, query the update failure rate of the historical model;

[0034] The target bill of materials is revised based on the preset configuration list of the updated model and the updated failure rate to obtain a revised target bill of materials.

[0035] In a second aspect, an embodiment of the present invention provides a device for generating a bill of materials for a smart lock, the device comprising:

[0036] An acquisition module, used to acquire service information of smart locks within a preset range of the service system, wherein the service information includes repair data and installation data of multiple lock models;

[0037] A determination module, used to determine an initial bill of materials based on the repair data, and query the historical failure data of each of the lock models;

[0038] A prediction module, used to predict the accessory information required for each lock model in a future time period based on the installation data and historical failure data, and obtain a predicted bill of materials;

[0039] A construction module is used to construct a target bill of materials for the service system using the initial bill of materials and the predicted bill of materials.

[0040] In a third aspect, an embodiment of the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0041] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method of the first aspect or any corresponding embodiment thereof.

[0042] The method provided in the embodiment of the present application has the following beneficial effects:

[0043] The method provided in the embodiment of the present application builds a comprehensive and accurate information architecture by collecting the repair data and installation data of the lock. According to the repair data, the types and quantities of required accessories can be accurately located, the maintenance response time can be shortened, and the maintenance process can be more efficient and smooth. The installation data provides key data for analyzing the use history of the lock. In-depth analysis combined with historical fault data can obtain the fault tendency and rules of different lock models at different use stages. This allows us to fully consider various factors when predicting future accessory needs, so as to formulate a targeted and forward-looking forecast bill of materials. In terms of inventory management, we can prepare sufficient accessories in advance to avoid maintenance delays due to insufficient inventory, and reasonably control the inventory scale according to the forecast, so as to achieve the dual goals of accurate inventory control and efficient resource utilization. Finally, by integrating the initial bill of materials and the forecast bill of materials, the order of accessory replenishment is reasonably planned based on the priority allocation rule, and the replenishment details are determined with reference to the real-time resource status of the service system, so as to achieve the optimal allocation of resources. Under the condition of limited resources, the supply of accessories for core and emergency maintenance tasks is prioritized, and the balance between inventory cost and service quality is successfully built, which is conducive to the stability of the intelligent lock maintenance service system and comprehensively improves the overall operational efficiency and customer satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1 is a flow chart of a method for generating a bill of materials for a smart lock according to an embodiment of the present invention;

[0046] Figure 2is a schematic diagram of the installation data content of a smart lock according to an embodiment of the present invention;

[0047] Figure 3 is a schematic diagram of the priority of replenishing lock accessories according to an embodiment of the present invention;

[0048] Figure 4 is a schematic diagram of a flow chart of calculating an update failure rate according to an embodiment of the present invention;

[0049] Figure 5 is a schematic diagram of a process for correcting a target bill of materials according to an embodiment of the present invention;

[0050] Figure 6 is a schematic diagram of a flow chart for calculating the replenishment quantity of smart lock repair parts according to an embodiment of the present invention;

[0051] Figure 7 is a flow chart of a method for replenishing smart lock repair accessories according to an embodiment of the present invention;

[0052] Figure 8 is a structural block diagram of a device for generating a bill of materials for a smart lock according to an embodiment of the present invention;

[0053] Fig. 9 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0055] According to an embodiment of the present invention, a method, apparatus, computer device and medium for generating a bill of materials for a smart lock are provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that shown here.

[0056] In this embodiment, a method for generating a bill of materials for a smart lock is provided. Figure 1 is a flow chart of a method for generating a bill of materials for a smart lock according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0057] Step S11, obtaining service information of smart locks within a preset range of the service system, wherein the service information includes repair data and installation data of multiple lock models.

[0058] In the embodiment of the present application, the repair data records the actual faults of the smart lock, including the faulty lock model, occurrence time, etc., reflecting the fault manifestations of different models, such as battery exhaustion, circuit board damage, sensor failure, etc. The installation data focuses on recording installation details, such as Figure 2 As shown, including the installation address, lock model and installation time, the distribution of lock models and usage time within the preset range can be analyzed. After obtaining the repair data, data retrieval and classification technology is used to extract fault events by model group from the database records (including model, repair time, fault description), such as the "fingerprint recognition module is unresponsive" of the smart lock, in preparation for determining the repair parts.

[0059] Step S12, determining an initial bill of materials based on the repair report data, and querying historical failure data of each lock model.

[0060] In the embodiment of the present application, determining the initial bill of materials according to the repair data includes the following steps A1-A3:

[0061] Step A1, extracting the fault events of each lock model in the repair report data.

[0062] Specifically, from the repair data of smart locks, the fault conditions that occurred in each lock model are extracted separately. For example, among the numerous repair records, the faults that occurred in the model V smart lock are screened out, such as battery exhaustion fault events, circuit board damage fault events, etc. These fault events are the key basis for determining the subsequent parts requirements, because different faults must correspond to different repair parts requirements. By extracting fault events, it is possible to clarify the types of problems that each lock model may encounter in actual use, and prepare for further parts analysis.

[0063] Step A2: determining the type of first accessories to be replenished and the corresponding quantity of first accessories according to the fault event.

[0064] Specifically, after determining the fault event, the first accessory type is determined based on the fault type and accessory association relationship constructed based on the smart lock product structure and maintenance experience, for example, "no response to fingerprint recognition module" corresponds to the fingerprint recognition module. When determining the number of first accessories, multiple factors are considered: on the one hand, the frequency of recent repair reports of the fault event is counted. For example, a certain model of smart lock has received 20 repair reports of "no response to fingerprint recognition module" in the past month and the number is increasing. In this case, the number of accessories required is usually large; on the other hand, the average number of accessories required for each repair of this fault is combined with the historical repair, such as an average of 1.5 each time. Through a comprehensive evaluation of these two aspects, a more reasonable number of first accessories is determined.

[0065] In addition, when a new type of fault occurs, the lock accessories involved can be preliminarily determined by consulting the product documentation, manuals, maintenance guides and technical bulletins of the smart lock; then the fault can be reproduced in a test environment, and the identified lock accessories can be checked and tested to narrow the scope; at the same time, prior knowledge can be used to establish the association between the new fault type and the accessories.

[0066] Step A3: construct an initial bill of materials based on the first accessory type and the corresponding first accessory quantity.

[0067] Specifically, after determining the first accessory type and quantity corresponding to the fault events of different lock models, this information is integrated into the initial bill of materials. The format of the initial bill of materials can be a two-dimensional table, with rows representing different lock models and columns representing fault events, first accessory types, first accessory quantity and other information. For example, for lock model V, if there is a "battery exhaustion" fault, the corresponding first accessory type is battery, and the quantity is 5; for a "circuit board damage" fault, the corresponding first accessory type is circuit board, and the quantity is 2, etc. The initial bill of materials presents the current preliminary accessories demand determined based on the repair report data, and provides a basis for further optimization in combination with historical fault data.

[0068] The method provided in the embodiment of the present application extracts fault events of each lock model in the repair data, which helps to accurately locate the specific circumstances of the problems that occur during actual use, and provides basic support for the subsequent accurate analysis of parts needs. Determining the type of first accessories to be replenished and the corresponding quantity of first accessories based on the fault event can clarify the specific accessories and corresponding quantities that are urgently needed for the current maintenance, making the parts replenishment more targeted. Constructing the initial bill of materials based on this information can clearly and intuitively present the full picture of the current parts needs determined based on the actual repair situation, facilitate subsequent operations such as integration with the predicted bill of materials, and ensure that the maintenance work can be carried out smoothly according to the actual fault situation.

[0069] In the embodiment of the present application, querying the historical fault data of each lock model includes the following steps B1-B3:

[0070] Step B1, query the historical failure events of the lock model in the historical database.

[0071] Specifically, the historical database stores a large number of fault-related records accumulated during the use of smart locks in the past. First, use the specific lock model as the search identifier. For example, if you want to query the historical fault events of a certain brand model V smart lock, use "V" as the keyword to filter and search in the database. Each record in the database usually covers many aspects of information such as the time when the fault occurred, the specific description of the fault, the number of the lock involved, etc. Through data retrieval tools or writing corresponding query statements, all records that match the lock model are extracted, and then the specific description of the fault is focused on from these records, thereby obtaining various historical fault events that have occurred in this lock model.

[0072] Step B2, calculating the historical failure rate of the lock model in each time period based on the historical failure events.

[0073] Specifically, after obtaining the historical failure events of a specific lock model, the failure rate is analyzed and calculated according to different time periods, such as the four time periods after the lock model is updated (within 3 months, more than 3 months and less than 12 months, more than 12 months and less than 36 months, and more than 36 months). Take the model V smart lock in a certain area as an example. It has 20 failure events within 3 months after it was updated. The total number of this lock model used during the same period is 500. Using 20÷500, the failure rate is 0.04 (ie 4%). The same method is used to calculate the historical failure rates corresponding to other time periods.

[0074] Step B3: construct historical failure data of the lock model based on the historical failure rate of the lock model in each time period.

[0075] Specifically, after calculating the historical failure rate of the lock model in each time period, use a data table or a specific data structure to store the information, such as constructing a two-dimensional table, with rows set to different time periods (such as the above four time periods), and columns set to lock models, historical failure rates, etc. Taking the model V smart lock as an example, fill in the historical failure rate value of the corresponding period in the corresponding cell to form records such as "Model V smart lock-3 months-4%", organize the data of each period, and construct the historical failure data of the lock model, which can intuitively present the failure probability, facilitate subsequent call analysis, and help accurately predict accessory information.

[0076] The method provided in the embodiment of the present application queries the historical failure events of the lock model in the historical database, and can mine various problems that have occurred in past use, so as to accumulate materials for unlocking the long-term failure performance of the lock. The historical failure rate of the lock model in each time period is calculated based on the historical failure events, and the probability of lock failure in different stages can be reflected with quantitative data, which is convenient for grasping the failure law. The historical failure data of the lock model is constructed based on its historical failure rate in each time period, so that these valuable data can be systematically integrated, providing reliable data reference for the subsequent prediction of accessory demand in the future time period, thereby improving the accuracy and scientificity of accessory prediction.

[0077] Step S13, predicting the accessory information required for each lock model in the future time period based on the installation data and the historical failure data, and obtaining a predicted bill of materials.

[0078] In the embodiment of the present application, step S13 includes the following steps C1-C3:

[0079] Step C1, analyzing historical fault data to obtain the fault probability distribution of each lock model.

[0080] Specifically, the historical failure data contains the failure rate information of each lock model in different time periods. By collating and analyzing these data, such as using statistical analysis methods or data visualization tools, the failure rate of each lock model in different time periods (such as within 3 months, more than 3 months and less than 12 months, etc.) is presented in the form of a chart or data distribution. Taking the model V smart lock as an example, it can be concluded that the probability of a certain type of failure within 3 months is 10%, and the probability of the failure drops to 5% when it is more than 3 months and less than 12 months, etc., thereby obtaining the failure probability distribution of different fault types of each lock model in different periods, providing a basis for the subsequent prediction of potential failure events.

[0081] Step C2, predicting potential failure events of the lock model in a future time period based on the failure probability distribution and the installation data.

[0082] Specifically, after mastering the probability distribution of failures, we combine the installation data, that is, the number of lock models and installation time in the area that each service provider is responsible for, to make predictions. For example, it is known that there are 1,000 model V smart locks in a certain area, and the installation time is about 6 months. According to the probability distribution of failures, the probability of a certain failure of this model in a period of more than 3 months and less than 12 months is 5%. It can be predicted that the number of model V smart locks that may have such failures in this area in the next month is about 1000×5%÷12≈4.17 (rounded to about 4), that is, the number of potential failure events and the models of locks involved are predicted, and it is clear which models of locks may have what kind of failures in the future.

[0083] Step C3, using potential failure events to generate a predicted bill of materials for the service system.

[0084] The method provided in the embodiment of the present application analyzes historical fault data to obtain the fault probability distribution of each lock model, which can clearly present the possibility trend of faults of different lock models from a macro level, and assist in grasping the overall situation of the fault. Based on the fault probability distribution and installation data, the potential fault events of the lock model in the future time period are predicted, so that the possible fault conditions in the future can be known in advance, so as to prepare for the response in advance. Using potential fault events to generate a predicted bill of materials for the service system helps to plan the accessories needed in the future in advance, avoid the shortage of repair parts due to sudden failures, and ensure the timeliness and consistency of maintenance services.

[0085] In the embodiment of the present application, step C3 includes the following steps C31-C34:

[0086] Step C31, obtaining the association relationship between the preset fault event and the preset accessory type.

[0087] Specifically, in the smart lock repair parts management system, a database is pre-built to record the association between various possible fault events and the corresponding types of repair parts. For example, when the smart lock has a preset fault event of "fingerprint recognition sensor failure", by querying the database, it can be clearly determined that the corresponding preset accessory type is the fingerprint recognition sensor of this model of smart lock; for another example, the "battery power is exhausted quickly" fault event, the corresponding preset accessory type is a battery of the corresponding specification. Through such an association relationship, when the potential fault event is determined in the subsequent steps, the type of accessories that need to be prepared can be quickly and accurately found, providing a basic basis for accurate replenishment.

[0088] Step C32: determining the second accessory type associated with the potential fault event according to the association relationship.

[0089] Specifically, based on the obtained association relationship, the second accessory type associated with the potential fault event is determined. For example, after analysis and prediction, it is found that a certain number of model V smart locks in a certain area may have a potential fault event of "circuit board short circuit" in the next month. Based on the association relationship established previously, it can be determined that the corresponding second accessory type is the specific circuit board model of the model V smart lock. For another example, if a potential fault event of "abnormal operation of the door lock motor" is predicted, the associated second accessory type can be determined to be the door lock motor suitable for the smart lock.

[0090] Step C33, traverse the preset configuration list of the lock model to determine the number of second accessories associated with the second accessory type.

[0091] Specifically, each specific lock model has its preset configuration list, which lists in detail the standard quantity, specifications and other information of each accessory of the lock under normal assembly. After determining the type of the second accessory, the specific number of second accessories can be determined by combining the list with relevant factors. For example, for a certain model of smart lock, the normal circuit board configuration is 1 piece. Due to maintenance inspection, replacement and past experience (each "circuit board short circuit" fault repair requires an average of 1.5 spare pieces), if it is predicted that the fault may occur 5 times in the next month, the number of second accessories can be calculated to be 7.5 pieces (actual rounding). Similarly, the number of door lock motors is also determined in this comprehensive manner, so that the number corresponding to each type of second accessories can be reasonably determined by comprehensively considering multiple factors to meet maintenance needs.

[0092] Step C34, constructing a forecast bill of materials based on the second accessory type and the corresponding second accessory quantity.

[0093] Specifically, after determining the type and quantity of the second accessory associated with each potential failure event, integrate the information to construct a predicted bill of materials. For example, if the predicted bill of materials is presented in a table, the table header includes columns such as lock model, second accessory type, and second accessory quantity. One row records "Model V-circuit board abc-7 pieces", and another row records "Model V-door lock motor bcd-3 pieces", etc., listing the accessories required for different lock models. This list can clearly show the future preparation of accessories based on potential failure predictions, provide an intuitive and important reference for accessories procurement and replenishment arrangements, and ensure the rationality of maintenance services and inventory management.

[0094] The method provided in the embodiment of the present application obtains the association relationship between the preset fault event and the preset accessory type, ensuring that the accessory type that should be prepared for each type of fault can be determined. The second accessory type associated with the potential fault event is determined based on the association relationship, and the corresponding accessories can be accurately locked for the predicted potential fault, thereby improving the accuracy of accessory preparation. The preset configuration list of the lock model is traversed to determine the number of second accessories associated with the second accessory type. The configuration of the lock itself and various demand factors in actual maintenance are comprehensively considered to reasonably determine the number of accessories, avoid excessive or insufficient inventory of accessories, and make the predicted bill of materials more in line with the actual maintenance scenario, which is conducive to the rational allocation of resources and the efficient implementation of maintenance work.

[0095] Step S14, constructing a target bill of materials for the service system using the initial bill of materials and the predicted bill of materials.

[0096] In the embodiment of the present application, step S14 includes the following steps D1-D3:

[0097] Step D1, merging the initial bill of materials and the predicted bill of materials to obtain a fused bill of materials.

[0098] Specifically, the initial bill of materials is based on the current actual repair parts required for repairs determined by the repair data, while the forecast bill of materials is an estimate of the parts required for future potential faults based on historical fault data and installation data. When merging these two lists, it is necessary to integrate the items with the same lock model and the same accessory type. For example, the initial bill of materials contains "Model V Smart Lock-Batteries-5" (indicating that the current repair report determines that 5 batteries need to be replenished), and the forecast bill of materials contains "Model V-Batteries-3" (indicating that 3 batteries are predicted to be needed in the future), then the corresponding entry in the fused bill of materials is "Model V-Batteries-8". In this way, the current actual demand is combined with the future forecast demand to form a fused bill of materials that integrates immediate and future accessory needs, avoiding the one-sidedness of making replenishment decisions based on a single list.

[0099] Step D2, sorting each accessory type in the fusion bill of materials according to the priority allocation rule to obtain an accessory supplement sequence.

[0100] Specifically, determining the priority allocation rules requires a comprehensive consideration of many factors. First, consider the impact of accessory failures on the normal use of smart locks by users. For example, accessories that affect the opening and closing functions of door locks (such as lock cylinders, circuit boards, etc.) have a higher priority, while some accessories that only affect auxiliary functions (such as doorbell function-related accessories) have a relatively low priority. Secondly, it is determined based on the out-of-stock cost of the accessories. If the out-of-stock of an accessory will cause a long delay in repairs and may cause serious customer dissatisfaction or high compensation, its priority will also increase. Based on these rules, sort the accessory types in the fused bill of materials. For example, if Figure 3 As shown, for the smart lock of model V, if the current integrated material list contains requirements for accessories such as lock cylinders, batteries, and doorbell buttons, the accessory types corresponding to the first priority (main board, lock body, and fingerprint head, etc.) can be arranged first according to priority, followed by the accessory types corresponding to the second priority (panel, etc.), and finally the accessory types corresponding to the third priority, thereby forming an accessory supplement sequence to ensure that, when resources are limited, accessories that have a greater impact on user experience and business operations are given priority.

[0101] Step D3, obtaining the current resource status of the service system, determining the type of accessories to be replenished and the corresponding replenishment quantity in the accessories replenishment sequence according to the resource status, and obtaining the target material list.

[0102] Specifically, the resource status of the service system includes information such as the current inventory quantity, available funds, and supplier delivery cycle. First check the current inventory quantity and compare it with the accessories in the accessories replenishment sequence. For example, for lock cylinders with higher priority, if there are only 2 in the current inventory, and the replenishment sequence requires 5, considering the funds and supply cycle, if the existing resources are sufficient and the supply cycle is short, it can be determined that the number of lock cylinders to be replenished this time is 3; if resources are tight or the supply cycle is long, the replenishment quantity can be reduced or the replenishment plan can be adjusted according to specific rules. For other accessories, the same method is used to determine the number of each accessory type to be replenished in the accessories replenishment sequence one by one according to the resource status, and finally form the target bill of materials.

[0103] The method provided in the embodiment of the present application obtains a fused bill of materials by fusing the initial bill of materials and the predicted bill of materials, combining the current actual maintenance needs with the potential future maintenance needs, so that the parts list can comprehensively cover various immediate and subsequent demand situations, avoiding the unreasonable replenishment problem caused by one-sided considerations. The various types of accessories in the fused bill of materials are sorted according to the priority allocation rules to obtain a parts replenishment sequence, which can reasonably arrange the replenishment order based on factors such as the importance of the accessories to the maintenance work and user experience, ensure that key accessories are replenished first, and improve resource utilization efficiency and maintenance service quality. Obtain the current resource status of the service system, determine the type of accessories to be replenished and the corresponding replenishment quantity in the accessories replenishment sequence according to the resource status, and obtain the target bill of materials. It can fully combine the actual resource situation, while meeting the maintenance needs, to achieve optimal resource allocation, and avoid problems such as maintenance delays or cost waste caused by resource limitations or unreasonable allocation.

[0104] In the embodiment of the present application, after step S14, the method further includes steps E1-E3:

[0105] Step E1, checking whether there is an updated model in the lock model list.

[0106] Specifically, in the market environment of smart locks, products will be constantly updated, and new lock models may be launched and put into use. Therefore, it is necessary to check the lock model list regularly or at specific nodes to see if there are any new models. This detection can be achieved in a variety of ways, such as communicating with the production system or product management system to obtain information about new product launches; or connecting to the installation system, maintenance registration system, etc. to see if there are any lock models that have not been recorded. For example, when a smart lock with a new function and a new appearance design is put into use and enters the maintenance service scope, its corresponding new lock model needs to be detected in time to determine whether the target bill of materials needs to be adjusted accordingly.

[0107] Step E2: If there is an updated model in the lock model list, query the update failure rate of the historical model.

[0108] Specifically, if an updated model is detected, its historical failure rate information needs to be queried. Since it is a new model, the failure rate for the corresponding period can be calculated by collecting fault feedback records during the new product trial, small-scale launch or internal testing phase, and counting the fault frequency and usage time range. For example, within one month of the first trial of a new smart lock model, a specific fault was reported 10 times, with a total of 100 locks on trial. The update failure rate for this fault this month is 10%. On the other hand, by referring to the failure rate of mature lock models of the same series, similar technical architecture or functional modules, the update failure rate can be grasped by comprehensive data. For example, Figure 4 As shown, big data analyzes the historical repair records of historical models to obtain the updated failure rate of historical models within a preset time period after the new models are updated, providing a basis for revising the bill of materials.

[0109] In addition, when making analogical inferences, the standards for selecting mature lock models of the same series and similar technical architecture or functional modules include: from the perspective of product series, priority is given to models of the same brand, target market and positioning; in terms of technical architecture, the core components and principles must be the same or similar, and the communication and connection technologies must be similar; in terms of functional modules, the main functions must have a high degree of overlap and the additional functions must have a strong correlation, so that the failure rate of mature lock models can provide a more accurate reference for new lock models.

[0110] Step E3, modifying the target material list based on the preset configuration list of the updated model and the updated failure rate to obtain a modified target material list.

[0111] Specifically, Figure 5 As shown in the figure, after clarifying the update failure rate of the historical model within the preset time period, the preset configuration list of the updated model (i.e., the standard quantity, specifications, and other information of each accessory when the new lock model is normally assembled) and the priority allocation rules, the original target material list can be modified based on this information to obtain a revised target material list. First, the various accessories involved in the new lock model are clarified according to the preset configuration list, such as the new lock model contains a new type of fingerprint recognition module, special specifications of batteries and other accessory information. Then, the demand quantity of these accessories is estimated in combination with the update failure rate. Assuming that according to the update failure rate calculated above, it is expected that the new lock model may involve 5 replacements of the fingerprint recognition module due to fault repair needs in the next month. Considering possible spare parts, it is determined according to experience that 8 fingerprint recognition modules need to be prepared as a supplementary quantity, and the same applies to other accessories. These new accessory demand information are integrated into the original target material list, and the corresponding items are replaced or added to form a revised target material list.

[0112] In the embodiments of the present application, Figure 6 As shown, the calculation process of the replenishment quantity of smart lock repair parts includes: determining the predicted bill of materials based on potential failure events through three input data, namely the lock models within the preset range of the service system and the corresponding installation data, repair data and failure rate of each model. Based on these data, calculate the target bill of materials required for the future time period, that is, the safety stock of each lock model, and then adjust the target bill of materials according to the current resource status of the service system to obtain the revised target bill of materials, such as using the safety stock minus the current inventory to obtain the replenishment quantity. The entire process realizes the prediction of safety stock in the future time period by collecting and analyzing the smart lock installation data, failure rate data and repair parts demand data, and determines the replenishment quantity based on the current resource status, which helps to optimize inventory management and improve replenishment efficiency and accuracy.

[0113] In this embodiment, a method for replenishing smart lock repair parts is provided. Figure 7 is a flow chart of a method for replenishing smart lock repair accessories according to an embodiment of the present invention. Figure 7 As shown, the process includes the following steps:

[0114] First, obtain the service information of the smart lock within the preset range of the service system, including repair data and installation data. Next, determine the initial bill of materials based on the repair data, query the historical fault data of the lock model, and then predict the accessories information required in the future time period based on the installation data and historical fault data to obtain the predicted bill of materials, and then use the initial bill of materials and the predicted bill of materials to build the target bill of materials. After that, query the existing inventory of accessories in the service system to obtain the target bill of materials for the accessories to be replenished. Determine whether the current resource status of the service system meets the target bill of materials. If it does, the process ends. If not, the early warning system generates reminder information for replenishing accessories, generates a supplementary bill of materials based on the target bill of materials, and then generates a list to be settled. After the settlement is successful, the accessories are replenished. In addition, if an updated model appears, the target bill of materials needs to be revised.

[0115] The method provided in the embodiment of the present application detects whether there are updated models in the lock model list, which helps to promptly discover new lock products on the market and ensure that the bill of materials management can keep up with the pace of product updates. If there are updated models in the lock model list, the update failure rate of the historical model is queried to understand the failure conditions of the new lock model in the initial use stage, providing a key basis for the subsequent reasonable estimation of its accessories demand. The target bill of materials is revised based on the preset configuration list of the updated model and the updated failure rate to obtain the revised target bill of materials, which can make the entire bill of materials adapt to the addition of new products, ensure that the maintenance parts management for all lock models in use is scientific and reasonable, ensure that the maintenance service will not be out of touch with the supply of accessories due to the emergence of new products, and maintain the stability and integrity of the service.

[0116] In this embodiment, a bill of materials generation device for a smart lock is also provided, which is used to implement the above embodiments and preferred implementations, and will not be repeated here. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0117] This embodiment provides a device for generating a bill of materials for a smart lock, such as Figure 8 As shown, including:

[0118] An acquisition module 81 is used to acquire service information of smart locks within a preset range of the service system, wherein the service information includes repair data and installation data of multiple lock models;

[0119] A determination module 82 is used to determine an initial bill of materials based on the repair report data and query the historical failure data of each lock model;

[0120] A prediction module 83, for predicting the accessory information required for each lock model in a future time period based on the installation data and historical failure data, and obtaining a predicted bill of materials;

[0121] The construction module 84 is used to construct a target bill of materials of the service system using the initial bill of materials and the predicted bill of materials.

[0122] In an optional embodiment of the present application, the determination module 84 includes an extraction submodule and a query submodule;

[0123] The extraction submodule is used to extract the fault events of each lock model in the repair data; determine the first accessory type to be replenished and the corresponding first accessory quantity according to the fault event; and construct an initial material list based on the first accessory type and the corresponding first accessory quantity.

[0124] The query submodule is used to query the historical failure events of the lock model in the historical database; calculate the historical failure rate of the lock model in each time period based on the historical failure events; and construct the historical failure data of the lock model based on the historical failure rate of the lock model in each time period.

[0125] In an optional embodiment of the present application, the prediction module 83 is used to analyze historical failure data to obtain the failure probability distribution of each lock model; predict potential failure events of the lock model in a future time period based on the failure probability distribution and installation data; and use potential failure events to generate a predicted bill of materials for the service system.

[0126] In an optional embodiment of the present application, the prediction module 83 is used to obtain the association relationship between the preset fault event and the preset accessory type; determine the second accessory type associated with the potential fault event based on the association relationship; traverse the preset configuration list of the lock model to determine the number of second accessories associated with the second accessory type; and construct a predicted material list based on the second accessory type and the corresponding second accessory quantity.

[0127] In an optional embodiment of the present application, a construction module 84 is used to fuse the initial bill of materials and the predicted bill of materials to obtain a fused bill of materials; sort the various accessory types in the fused bill of materials according to the priority allocation rule to obtain an accessory replenishment sequence; obtain the current resource status of the service system, and determine the type of accessories to be replenished and the corresponding replenishment quantity in the accessory replenishment sequence according to the resource status to obtain a target bill of materials.

[0128] In an optional embodiment of the present application, the device also includes: a correction module, used to detect whether there is an updated model in the lock model list; if there is an updated model in the lock model list, query the update failure rate of the historical model; based on the preset configuration list of the updated model and the update failure rate, correct the target material list to obtain a corrected target material list.

[0129] See also Fig. 9 , Fig. 9 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Fig. 9As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system).

[0130] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0131] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0132] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created by the use of a computer device based on the presentation of a small program landing page, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0133] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0134] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0135] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0136] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for generating a bill of materials for a smart lock, characterized in that: The method comprises: Obtain service information of smart locks within a preset range of the service system, wherein the service information includes repair data and installation data of multiple lock models; Determine an initial bill of materials based on the repair data, and query historical failure data for each of the lock models; Based on the installation data and historical failure data, the accessory information required for each lock model in the future time period is predicted to obtain a predicted bill of materials; The target bill of materials of the service system is constructed using the initial bill of materials and the predicted bill of materials.

2. The method according to claim 1, characterized in that Determining the initial bill of materials according to the repair data includes: Extracting the failure events of each lock model in the repair report data; Determine the type of first accessories to be replenished and the corresponding quantity of first accessories according to the fault event; An initial bill of materials is constructed based on the first accessory type and the corresponding first accessory quantity.

3. The method according to claim 1, characterized in that The querying of the historical fault data of each lock model includes: Query the historical failure events of the lock model in the historical database; Calculate the historical failure rate of the lock model in each time period according to the historical failure events; Based on the historical failure rate of the lock model in each time period, historical failure data of the lock model is constructed.

4. The method according to claim 1, characterized in that: The method of predicting the accessory information required for each lock model in a future time period based on the installation data and the historical failure data to obtain a predicted bill of materials includes: Analyze the historical fault data to obtain the fault probability distribution of each lock model; Predicting potential failure events of the lock model in a future time period based on the failure probability distribution and the installation data; A predicted bill of materials for the service system is generated using the potential failure events.

5. The method according to claim 4, characterized in that The step of generating a predicted bill of materials for the service system using the potential failure event includes: Obtaining the association between the preset fault event and the preset accessory type; Determining a second accessory type associated with the potential fault event according to the association relationship; Traversing the preset configuration list of the lock model to determine the number of second accessories associated with the second accessory type; A forecast bill of materials is constructed based on the second accessory type and the corresponding second accessory quantity.

6. The method according to claim 1, characterized in that The step of constructing a target bill of materials for the service system by using the initial bill of materials and the predicted bill of materials includes: Merging the initial bill of materials and the predicted bill of materials to obtain a merged bill of materials; Sort the various accessory types in the fusion material list according to a priority allocation rule to obtain an accessory supplement sequence; The current resource status of the service system is obtained, and the types of accessories to be replenished and the corresponding replenishment quantities are determined in the accessory replenishment sequence according to the resource status to obtain a target bill of materials.

7. The method according to claim 1, characterized in that After constructing a target bill of materials for the service system using the initial bill of materials and the predicted bill of materials, the method further includes: Check whether there is an updated model in the lock model list; If there is an updated model in the lock model list, query the update failure rate of the historical model; The target bill of materials is revised based on the preset configuration list of the updated model and the updated failure rate to obtain a revised target bill of materials.

8. A device for generating a bill of materials for a smart lock, characterized in that: The device comprises: An acquisition module, used to acquire service information of smart locks within a preset range of the service system, wherein the service information includes repair data and installation data of multiple lock models; A determination module, used to determine an initial bill of materials based on the repair data, and query the historical failure data of each of the lock models; A prediction module, used to predict the accessory information required for each lock model in a future time period based on the installation data and historical failure data, and obtain a predicted bill of materials; A construction module is used to construct a target bill of materials for the service system using the initial bill of materials and the predicted bill of materials.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method according to any one of claims 1 to 7.

Images