Intelligent management method and system for ancient building restoration consumables based on multi-source data fusion

Abstract

The invention belongs to the technical field of ancient building restoration management and control, and particularly relates to an ancient building restoration consumable intelligent management method and system based on multi-source data fusion, and the system specifically comprises a consumable storage monitoring module, a storage environment analysis module, an inventory loss analysis module, an inventory early warning response analysis module, a consumable storage condition evaluation module and an intelligent management end. Real-time complete data support is provided for whole-process analysis through the consumable storage monitoring module, the storage environment analysis module early warns environment abnormity in time, the inventory loss analysis module clearly presents the loss condition, the inventory early warning response analysis module accurately recognizes the inventory insufficiency condition, and the inventory early warning response analysis module accurately recognizes the inventory insufficiency condition. And the consumable storage condition evaluation module integrates multi-dimensional indexes to comprehensively judge the control performance, reasonably grades suppliers and prompts the suppliers to adjust demands in time, so that the targets of consumable storage safety, accurate inventory control and stable supply guarantee are integrally realized.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of ancient building repair management and control, and in particular to an ancient building repair consumable intelligent management method and system based on multi-source data fusion. BACKGROUND

[0002] Ancient buildings carry rich historical and cultural information, and technical repair and protection is a complex and demanding system engineering. In recent years, with the introduction of digital and intelligent technologies, ancient building repair technology is gradually transforming from relying on manual experience to data-driven and intelligent decision-making.

[0003] For example, the Chinese invention patent with the publication number CN117196224A relates to an ancient building repair intelligent management method and system. The method obtains point cloud data by laser scanning, constructs a digital twin model, identifies damaged features, and generates a repair decision using a repair optimization function, significantly improving the comprehensiveness of information acquisition, objectivity of judgment, and work efficiency in the repair process. It focuses on the state diagnosis and repair scheme generation of the building itself, representing an important direction for the intelligent development of ancient building repair.

[0004] However, ancient building repair not only involves the diagnosis and optimization of building structures and repair processes, but also highly depends on the continuous, stable, and safe supply of various special repair consumables. In actual engineering management, there is a general focus on repair processes and structural analysis, and there is a lack of intelligent management means for the repair process, such as consumable supply chain, storage environment, inventory status, and supplier performance. It is difficult to ensure the stability and reliability of the supply of ancient building repair consumables, and it is impossible to ensure the smooth development of ancient building repair work. Therefore, a solution is proposed. SUMMARY

[0005] The purpose of the present application is to provide an ancient building repair consumable intelligent management method and system based on multi-source data fusion to solve the technical defects proposed in the background art.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical solution: an ancient building repair consumable intelligent management system based on multi-source data fusion, comprising a consumable storage monitoring module, a storage environment analysis module, a inventory loss analysis module, a inventory early warning response analysis module, a consumable storage management evaluation module, and an intelligent management terminal. The consumable storage monitoring module comprehensively monitors the storage warehouse of ancient building repair consumables and outputs real-time storage monitoring data sets. The storage environment analysis module analyzes the storage environment of the consumable storage warehouse, and sends corresponding warning information to the intelligent management terminal when it is determined to be in a storage environment warning state. The inventory loss analysis module analyzes the inventory loss status of the consumable storage warehouse and synchronously outputs the inventory loss information; the inventory early warning response analysis module analyzes the inventory performance of all consumables and sends corresponding early warning information to the intelligent management terminal when it is determined that there is a consumable in an inventory shortage state; the consumable storage management evaluation module analyzes the consumable storage management performance and sends the consumable storage management early warning signal to the intelligent management terminal when it is generated.

[0007] Further, the specific analysis process of the storage environment analysis module is as follows: Obtain the environment parameters that need to be monitored in the storage warehouse, collect the monitoring data of each environment parameter in real time, compare the monitoring data of each environment parameter with the corresponding preset data requirements, and if there is an environment parameter whose monitoring data does not meet the corresponding preset data requirements, it is determined to be in a storage environment early warning state.

[0008] Further, the specific analysis process of the inventory early warning response analysis module is as follows: Obtain the inventory of all consumables, compare the inventory of all consumables with the corresponding preset inventory lower limit threshold value respectively, and if there is a consumable whose inventory is lower than the corresponding preset inventory lower limit threshold value, it is determined that the corresponding consumable is in an inventory shortage state.

[0009] Further, the specific analysis process of the consumable storage management evaluation module is as follows: Obtain all loss information in the evaluation period, and accordingly obtain the amount involved in the consumable loss and discard during storage and mark it as a storage loss condition value; obtain the total duration of the consumable storage warehouse in the storage environment early warning state in the evaluation period and mark it as a storage environment warning time value, and obtain an inventory response decision value by analysis and calculation; Calculate the storage loss condition value, the storage environment warning time value and the inventory response decision value by weighted summation to obtain a storage management matching coefficient, compare the storage management matching coefficient with the preset storage management matching coefficient threshold value, and if the storage management matching coefficient exceeds the preset storage management matching coefficient threshold value, generate a consumable storage management early warning signal.

[0010] Further, the analysis and calculation method of the inventory response decision value is as follows: Obtain the time when the corresponding consumable is in an inventory shortage state and mark it as a first time, and mark the time when the corresponding consumable ends the inventory shortage state as a second time, and calculate the time difference between the first time and the second time to obtain a time supplement coefficient; All time-interval coefficients for the corresponding consumables within the evaluation period are obtained and summed to obtain the total time-interval measurement value. The time-interval coefficients are compared with the preset time-interval coefficient threshold. If the time-interval coefficient exceeds the preset time-interval coefficient threshold, the corresponding time-interval coefficient is marked as a time-isolation coefficient. The number of time-isolation coefficients corresponding to the corresponding consumables within the evaluation period is obtained and marked as time-isolation detection values. The consumable replenishment value is obtained by weighted summation of the total measured value and the time difference detection value. Each consumable is pre-set to correspond to a set of preset weight values. The consumable replenishment value of the corresponding consumable is multiplied by the corresponding preset weight value to obtain the consumable replenishment risk value. The inventory response decision value is obtained by summing the consumable replenishment risk values ​​of all consumables.

[0011] Furthermore, the intelligent management terminal communicates with the supplier performance capability dynamic assessment module. Based on the supplier's historical performance data, real-time supply status, and product quality feedback, the supplier performance capability dynamic assessment module dynamically assesses and classifies the supplier's performance capability, and sends the assessment results to the intelligent management terminal.

[0012] Furthermore, the specific analysis process of the supplier performance capability dynamic assessment module is as follows: Obtain all suppliers of consumables, and label the corresponding supplier as j, where j is a natural number greater than 1; collect the number of on-time delivery orders of supplier j during the monitoring period and calculate the ratio with the total number of orders, and label the ratio result as the on-time fulfillment rate Bj; and collect the number of qualified products supplied by supplier j during the monitoring period and calculate the ratio with the total supply quantity, and label the ratio result as the quality pass rate Zj; The emergency order processing cycle tj1 of supplier j during the monitoring period and the corresponding industry average emergency order processing cycle tj2 are collected, and the emergency response capability coefficient Yj of supplier j is calculated by Yj=1-tj1 / tj2; the product price fluctuation range ts1 of supplier j during the monitoring period and the corresponding industry average price fluctuation range ts2 are collected, and the emergency response capability coefficient Yj of supplier j is calculated by Gj=1-ts1 / ts2. The supplier's overall performance capability score Pj is calculated by weighted summation: Pj = β1 × Zj + β2 × Qj + β3 × Yj + β4 × Gj; where β1, β2, β3, and β4 are preset indicator weights, and β1 + β2 + β3 + β4 = 1; the preset upper limit threshold Pmax and the preset lower limit threshold Pmin of the performance capability are obtained, and the supplier's overall performance capability score Pj is numerically compared with the preset upper limit threshold Pmax and the preset lower limit threshold Pmin respectively; If Pj≥Pmax, then supplier j is marked as a Grade A supplier and given the "Preferred Cooperation" label; if Pmax>Pj>Pmin, then supplier j is marked as a Grade B supplier and given the "Normal Cooperation" label; if Pj≤Pmin, then supplier j is marked as a Grade C supplier and given the "Restricted Cooperation or Elimination" label.

[0013] Furthermore, the supplier performance capability dynamic assessment module is connected to the supply intervention decision and early warning module. The supplier performance capability dynamic assessment module sends the assessment results of all suppliers to the supply intervention decision and early warning module. The supply intervention decision and early warning module analyzes the urgency of supply intervention and determines whether to generate a supply intervention early warning signal. When a supply intervention early warning signal is generated, it is sent to the intelligent management terminal. When the intelligent management terminal receives the supply intervention early warning signal, it issues a corresponding early warning.

[0014] Furthermore, the specific analysis process of the supply intervention decision-making early warning module is as follows: The number of C-level suppliers is obtained and the ratio is calculated with the total number of suppliers to obtain the restricted elimination status value. The restricted elimination status value is compared with the preset restricted elimination status threshold. If the restricted elimination status value exceeds the preset restricted elimination status threshold, a supply intervention warning signal is generated. If the elimination occupancy value does not exceed the preset elimination occupancy threshold, the reciprocal of the supplier's comprehensive performance capability score is marked as the performance poorness value, and the average of all suppliers' performance poorness values ​​is calculated to obtain the supplier evaluation value, and the performance poorness value with the largest value is marked as the supplier's poorness value. The supply intervention warning value is calculated by weighting and summing the restriction elimination status value, supplier evaluation value, and supplier inferiority value. The supply intervention warning value is then compared with the preset supply intervention warning threshold. If the supply intervention warning value exceeds the preset supply intervention warning threshold, a supply intervention warning signal is generated.

[0015] This invention also proposes an intelligent management method for consumable materials for the restoration of ancient buildings based on multi-source data fusion, including the following steps: Step 1: Conduct comprehensive monitoring of the storage warehouse for materials used in the restoration of ancient buildings; Step 2: Analyze the storage environment of the consumables storage warehouse; Step 3: Analyze the inventory loss status of the consumables storage warehouse; Step 4: Analyze the inventory performance of all consumables; Step 5: Analyze the performance of consumable storage management and control, and issue corresponding warnings from the intelligent management terminal when a consumable storage management warning signal is generated.

[0016] Compared with the prior art, the beneficial effects of the present invention are: 1. In this invention, by comprehensively monitoring the storage warehouse of consumables for the restoration of ancient buildings to output storage monitoring datasets in real time, the storage environment warning status can be judged in a timely manner based on the monitoring data, so as to avoid damage to consumables due to unsuitable environment. It can accurately analyze the inventory loss status of the warehouse and identify consumables with insufficient inventory in a timely manner, which is conducive to timely optimization of storage methods and avoids the restoration progress being affected by material shortages. Furthermore, by integrating multiple factors to comprehensively evaluate the overall performance of consumable storage management, the smooth progress of the restoration work of ancient buildings can be ensured.

[0017] 2. In this invention, suppliers are graded through a dynamic evaluation module of their performance capabilities, and high-quality partners are selected to avoid performance risks. Furthermore, a supply intervention decision-making early warning module promptly prompts suppliers to adjust their needs, reducing supply risks and significantly improving the level of intelligence in the management of consumables for ancient building restoration, thereby further ensuring the smooth progress of ancient building restoration work. Attached Figure Description

[0018] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings; Figure 1 This is a system block diagram of Embodiment 1 of the present invention; Figure 2 This is a system block diagram of Embodiments 2 and 3 of the present invention; Figure 3 This is a flowchart of the method in Embodiment 4 of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1: As Figure 1 As shown, the intelligent management system for ancient building restoration consumables based on multi-source data fusion proposed in this invention includes a consumables storage monitoring module, a storage environment analysis module, an inventory loss analysis module, an inventory early warning response analysis module, a consumables storage condition assessment module, and an intelligent management terminal. The consumables storage monitoring module comprehensively monitors the storage warehouse for consumables used in the restoration of ancient buildings and outputs real-time storage monitoring datasets, providing accurate and complete data support for subsequent analysis modules. The storage environment analysis module analyzes the storage environment conditions of the consumables warehouse. When it determines that the storage environment is in an early warning state, it sends corresponding warning information to the intelligent management terminal, effectively preventing consumables damage due to unsuitable environments and ensuring the quality of consumable storage. The specific analysis process of the storage environment analysis module is as follows: The system acquires the environmental parameters that need to be monitored within the storage warehouse, collects monitoring data for each environmental parameter in real time, and compares the monitoring data of each environmental parameter with the corresponding preset data requirements. If there are environmental parameters whose monitoring data does not meet the corresponding preset data requirements, it indicates that the corresponding environmental parameters need to be adjusted in a timely manner, and the system is judged to be in a storage environment early warning state.

[0021] The inventory loss analysis module analyzes the inventory loss status of consumable storage warehouses and outputs inventory loss information synchronously, allowing managers to clearly understand the consumable loss situation and providing data basis for optimizing storage methods and reducing losses. The inventory early warning response analysis module analyzes the inventory performance of all consumables. When it determines that a consumable is in a state of insufficient inventory, it sends corresponding early warning information to the intelligent management terminal, which helps ensure an uninterrupted supply of consumables needed for ancient building restoration work and avoids delays in restoration progress due to material shortages. The specific analysis process of the inventory early warning response analysis module is as follows: Once the inventory of all consumables is obtained, the inventory of each consumable is compared with the corresponding preset lower limit threshold. If there are consumables with an inventory lower than the corresponding preset lower limit threshold, it indicates that the consumables need to be replenished in time, and the consumables are judged to be in a state of insufficient inventory.

[0022] The consumables storage management assessment module analyzes the performance of consumables storage management and sends early warning signals to the intelligent management terminal when they are generated. Upon receiving these early warning signals, the intelligent management terminal issues corresponding alerts, comprehensively assessing the overall performance of consumables storage management and reminding managers to optimize management strategies in a timely manner, thereby significantly improving the effectiveness of consumables management. The specific analysis process of the consumables storage management assessment module is as follows: Obtain all loss information during the assessment period, thereby obtaining the amount involved in the loss and disposal of consumables during storage and marking it as the storage loss value; obtain the total duration during the assessment period when the consumable storage warehouse is in a storage environment warning state and mark it as the storage environment warning time value. The moment when the corresponding consumable is in a state of insufficient inventory is obtained and marked as the first moment, and the moment when the corresponding consumable ends the state of insufficient inventory is marked as the second moment. The time difference between the first moment and the second moment is calculated to obtain the time compensation coefficient. All time-interval coefficients for the corresponding consumables within the evaluation period are obtained and summed to obtain the total time-interval measurement value. The time-interval coefficients are compared with the preset time-interval coefficient threshold. If the time-interval coefficient exceeds the preset time-interval coefficient threshold, the corresponding time-interval coefficient is marked as a time-isolation coefficient. The number of time-isolation coefficients corresponding to the corresponding consumables within the evaluation period is obtained and marked as time-isolation detection values. The consumable replenishment value is obtained by weighted summation of the total measured value and the time-anomaly detection value. Specifically, the total measured value and the time-anomaly detection value are assigned corresponding preset weight coefficients, and the total measured value and the time-anomaly detection value are multiplied by the corresponding preset weight coefficients. The sum of the two sets of products is marked as the consumable replenishment value. It should be noted that the larger the value of the consumable replenishment value, the worse the timeliness of replenishment of the corresponding consumables during the evaluation period. Each type of consumable is pre-set with a set of preset weight values ​​greater than zero. The more important the consumable, the larger the preset weight value it corresponds to. The consumable replenishment status value of the consumable is multiplied by the corresponding preset weight value to obtain the consumable replenishment risk value. The consumable replenishment risk values ​​of all consumables are summed to obtain the inventory response decision value. The storage matching coefficient is obtained by weighted summation of storage loss condition value, storage environment alarm time value and inventory response decision value. Specifically, the storage loss condition value, storage environment alarm time value and inventory response decision value are assigned corresponding preset weight coefficients, and the storage loss condition value, storage environment alarm time value and inventory response decision value are multiplied by the corresponding preset weight coefficients, and the sum of the three sets of product results is marked as the storage matching coefficient. It should be noted that the higher the value of the storage matching coefficient, the worse the overall performance of consumable management during the evaluation period. The storage matching coefficient is compared with the preset storage matching coefficient threshold. If the storage matching coefficient exceeds the preset storage matching coefficient threshold, it indicates that the overall performance of consumable management during the evaluation period is poor, and a consumable storage management early warning signal is generated.

[0023] Example 2: Figure 2 As shown, the difference between this embodiment and Embodiment 1 lies in the communication connection between the intelligent management terminal and the supplier performance capability dynamic evaluation module. This module dynamically evaluates and classifies supplier performance capabilities based on historical performance data, real-time supply status, and product quality feedback, and sends the evaluation results to the intelligent management terminal. This allows managers to have a detailed understanding of each supplier's performance capabilities and make appropriate adjustments to related suppliers, helping them select high-quality partners, mitigate performance risks, and ensure a stable supply of consumables. The specific analysis process is as follows: Obtain all suppliers of consumables, and label the corresponding supplier as j, where j is a natural number greater than 1; collect the number of on-time delivery orders of supplier j during the monitoring period and calculate the ratio with the total number of orders, and label the ratio result as the on-time fulfillment rate Bj; and collect the number of qualified products supplied by supplier j during the monitoring period and calculate the ratio with the total supply quantity, and label the ratio result as the quality pass rate Zj; The emergency order processing cycle tj1 of supplier j during the monitoring period and the corresponding industry average emergency order processing cycle tj2 are collected, and the emergency response capability coefficient Yj of supplier j is calculated by Yj=1-tj1 / tj2; the product price fluctuation range ts1 of supplier j during the monitoring period and the corresponding industry average price fluctuation range ts2 are collected, and the emergency response capability coefficient Yj of supplier j is calculated by Gj=1-ts1 / ts2. The supplier's overall performance capability score Pj is calculated by weighted summation: Pj = β1 × Zj + β2 × Qj + β3 × Yj + β4 × Gj; where β1, β2, β3, and β4 are preset indicator weights, and β1 + β2 + β3 + β4 = 1; the preset upper limit threshold Pmax and the preset lower limit threshold Pmin of the performance capability are obtained, and the supplier's overall performance capability score Pj is numerically compared with the preset upper limit threshold Pmax and the preset lower limit threshold Pmin respectively; If Pj≥Pmax, then supplier j is marked as a Grade A supplier and given the "Preferred Cooperation" label; if Pmax>Pj>Pmin, then supplier j is marked as a Grade B supplier and given the "Normal Cooperation" label; if Pj≤Pmin, then supplier j is marked as a Grade C supplier and given the "Restricted Cooperation or Elimination" label.

[0024] Example 3: Figure 2 As shown, the difference between this embodiment and Embodiment 1 and Embodiment 2 is that the supplier performance capability dynamic assessment module is connected to the supply intervention decision and early warning module. The supplier performance capability dynamic assessment module sends the assessment results of all suppliers to the supply intervention decision and early warning module. The supply intervention decision and early warning module analyzes the urgency of supply intervention and determines whether to generate a supply intervention early warning signal. When a supply intervention early warning signal is generated, it is sent to the intelligent management terminal. When the intelligent management terminal receives a supply intervention early warning signal, it issues a corresponding warning to remind managers to intervene in a timely manner to adjust suppliers, reduce the degree of potential supply risks for consumables, and further ensure the stability of subsequent consumable supply. The specific analysis process of the supply intervention decision-making early warning module is as follows: The number of C-level suppliers is obtained and the ratio is calculated to the total number of suppliers to obtain the restricted elimination status value. The restricted elimination status value is compared with the preset restricted elimination status threshold. If the restricted elimination status value exceeds the preset restricted elimination status threshold, it indicates that the overall risk of consumable supply during the monitoring period is relatively high, and timely manual intervention to adjust suppliers is required, thus generating a supply intervention warning signal.

[0025] Furthermore, if the restricted elimination status value does not exceed the preset restricted elimination status threshold, the reciprocal of the supplier's comprehensive performance capability score is marked as the performance poorness value, and the average of all suppliers' performance poorness values ​​is calculated to obtain the supplier evaluation value, and the performance poorness value with the largest value is marked as the supplier's poorness value. The supply intervention warning value is calculated by weighting and summing the restriction and elimination status value, the supplier evaluation value, and the supplier inferiority value. Specifically, the restriction and elimination status value, the supplier evaluation value, and the supplier inferiority value are each assigned a corresponding preset weight coefficient, and the restriction and elimination status value, the supplier evaluation value, and the supplier inferiority value are each multiplied by the corresponding preset weight coefficient. The sum of the three sets of products is marked as the supply intervention warning value. It should be noted that the higher the value of the supply intervention warning value, the higher the overall risk of consumable supply during the monitoring period, and the more necessary it is for timely human intervention to adjust the supplier. The supply intervention warning value is compared with the preset supply intervention warning threshold. If the supply intervention warning value exceeds the preset supply intervention warning threshold, it indicates that the overall risk of consumable supply during the monitoring period is relatively high, and timely human intervention to adjust the supplier is necessary. In this case, a supply intervention warning signal is generated.

[0026] Example 4: Figure 3 As shown, the difference between this embodiment and Embodiments 1, 2, and 3 is that the intelligent management method for ancient building restoration consumables based on multi-source data fusion proposed in this invention includes the following steps: Step 1: Conduct comprehensive monitoring of the storage warehouse for materials used in the restoration of ancient buildings; Step 2: Analyze the storage environment of the consumables storage warehouse; Step 3: Analyze the inventory loss status of the consumables storage warehouse; Step 4: Analyze the inventory performance of all consumables; Step 5: Analyze the performance of consumable storage management and control, and issue corresponding warnings from the intelligent management terminal when a consumable storage management warning signal is generated.

[0027] The working principle of this invention is as follows: During use, the consumables storage monitoring module provides real-time and complete data support for the entire process analysis; the storage environment analysis module provides timely warnings of environmental anomalies to prevent consumables from being damaged due to unsuitable environments; the inventory loss analysis module clearly presents the loss status, providing a basis for loss reduction and optimization; the inventory early warning response analysis module accurately identifies insufficient inventory to ensure the continuous progress of repair work; the consumables storage management assessment module integrates multi-dimensional indicators to comprehensively evaluate management performance and generate early warning signals to assist in management optimization; the supplier performance capability dynamic assessment module classifies suppliers, selects high-quality partners to avoid performance risks; and the supply intervention decision early warning module promptly prompts suppliers to adjust their needs to reduce supply risks. Overall, it achieves the goals of safe consumables storage, accurate inventory control, and stable supply guarantee, significantly improving the intelligent level of consumables management for ancient building restoration and facilitating the smooth progress of restoration work.

[0028] In this invention, the threshold, preset value, or preset range settings are for result comparison and analysis to determine whether the result is good or bad. The magnitude of these values ​​is determined by a combination of large-scale model analysis of sample data and human experience, and can also be appropriately adjusted based on seasonal or common-sense influence conditions. Similarly, the preset weight coefficients and influence factors are assigned specific values ​​based on the magnitude of each parameter's influence on the result, ultimately reflecting the impact on the result. These settings are also determined by a combination of large-scale model analysis of sample data and human experience, and can also be appropriately adjusted based on seasonal or common-sense influence conditions.

[0029] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, enabling those skilled in the art to better understand and utilize it. The invention is limited only by the claims and their full scope and equivalents.

[0030] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. An intelligent management system for consumable materials for the restoration of ancient buildings based on multi-source data fusion, characterized in that, It includes a consumables storage monitoring module, a storage environment analysis module, an inventory loss analysis module, an inventory early warning response analysis module, a consumables storage management status assessment module, and an intelligent management terminal; The consumables storage monitoring module comprehensively monitors the storage warehouse of consumables for the restoration of ancient buildings and outputs storage monitoring datasets in real time. The storage environment analysis module analyzes the storage environment status of the consumables storage warehouse and sends the corresponding warning information to the intelligent management terminal when it determines that the storage environment is in a warning state. The inventory loss analysis module analyzes the inventory loss status of the consumables storage warehouse and outputs inventory loss information synchronously. The inventory early warning response analysis module analyzes the inventory performance of all consumables and sends the corresponding early warning information to the intelligent management terminal when it determines that there are consumables in a state of insufficient inventory. The consumable storage management assessment module analyzes the performance of consumable storage management and sends the consumable storage management early warning signal to the intelligent management terminal when it generates a consumable storage management early warning signal.

2. The intelligent management system for ancient building restoration consumables based on multi-source data fusion as described in claim 1, characterized in that, The specific analysis process of the storage environment analysis module is as follows: The system acquires the environmental parameters that need to be monitored within the storage warehouse, collects monitoring data for each environmental parameter in real time, compares the monitoring data of each environmental parameter with the corresponding preset data requirements, and determines that the storage environment is in an early warning state if there are any environmental parameters whose monitoring data do not meet the corresponding preset data requirements.

3. The intelligent management system for ancient building restoration consumables based on multi-source data fusion as described in claim 1, characterized in that, The specific analysis process of the inventory early warning response analysis module is as follows: Obtain the inventory of all consumables, and compare the inventory of each consumable with the corresponding preset lower limit threshold. If there are consumables with an inventory lower than the corresponding preset lower limit threshold, then the consumables are determined to be in a state of insufficient inventory.

4. The intelligent management system for ancient building restoration consumables based on multi-source data fusion as described in claim 1, characterized in that, The specific analysis process of the consumables storage condition assessment module is as follows: Obtain all loss information during the assessment period, thereby obtaining the amount involved in the loss and disposal of consumables during storage and marking it as the storage loss value; obtain the total duration of the consumable storage warehouse in the storage environment early warning state during the assessment period and mark it as the storage environment early warning value, and obtain the inventory response decision value through analysis and calculation. The storage matching coefficient is calculated by weighting and summing the storage loss status value, storage environment alarm value, and inventory response decision value. If the storage matching coefficient exceeds the preset storage matching coefficient threshold, a consumable storage management early warning signal is generated.

5. The intelligent management system for ancient building restoration consumables based on multi-source data fusion according to claim 4, characterized in that, The specific methods for analyzing and calculating inventory response decision values ​​are as follows: The consumable replenishment value is obtained by weighted summation of the total measured value and the time difference detection value. The consumable replenishment value of the corresponding consumable is multiplied by the corresponding preset weight value to obtain the consumable replenishment risk value. The inventory response decision value is obtained by summing the consumable replenishment risk values ​​of all consumables.

6. The intelligent management system for ancient building restoration consumables based on multi-source data fusion as described in claim 1, characterized in that, The intelligent management terminal communicates with the supplier performance capability dynamic assessment module. Based on the supplier's historical performance data, real-time supply status and product quality feedback, the supplier performance capability dynamic assessment module dynamically assesses and classifies the supplier's performance capability, and sends the assessment results to the intelligent management terminal.

7. The intelligent management system for ancient building restoration consumables based on multi-source data fusion as described in claim 6, characterized in that, The specific analysis process of the supplier performance capability dynamic assessment module is as follows: Obtain all suppliers of consumables, mark the corresponding suppliers as j, where j is a natural number greater than 1; calculate the comprehensive performance capability score Pj of the suppliers by weighted summation, and obtain the preset upper limit threshold Pmax and the preset lower limit threshold Pmin of the performance capability. If Pj≥Pmax, then supplier j is marked as a Grade A supplier and given the "Preferred Cooperation" label; if Pmax>Pj>Pmin, then supplier j is marked as a Grade B supplier and given the "Normal Cooperation" label; if Pj≤Pmin, then supplier j is marked as a Grade C supplier and given the "Restricted Cooperation or Elimination" label.

8. The intelligent management system for ancient building restoration consumables based on multi-source data fusion according to claim 7, characterized in that, The supplier performance capability dynamic assessment module communicates with the supply intervention decision and early warning module. The supply intervention decision and early warning module analyzes the urgency of supply intervention and sends it to the intelligent management terminal when it generates a supply intervention early warning signal.

9. The intelligent management system for ancient building restoration consumables based on multi-source data fusion as described in claim 8, characterized in that, The specific analysis process of the supply intervention decision early warning module is as follows: the supply intervention early warning value is calculated by weighting and summing the restriction elimination status value, the supplier evaluation value, and the supplier inferiority value. If the supply intervention early warning value exceeds the preset supply intervention early warning threshold, a supply intervention early warning signal is generated.

10. A method for intelligent management of consumable materials for the restoration of ancient buildings based on multi-source data fusion, characterized in that, Includes the following steps: Step 1: Conduct comprehensive monitoring of the storage warehouse for consumables for ancient building restoration; Step 2: Analyze the storage environment of the consumables storage warehouse; Step 3: Analyze the inventory loss status of the consumables storage warehouse; Step 4: Analyze the inventory performance of all consumables; Step 5: Analyze the performance of consumables storage management and control, and issue corresponding warnings to the intelligent management terminal when a consumables storage management warning signal is generated.

Images