Method, apparatus, device, and storage medium for AI intelligent inventory

Abstract

The present invention relates to a method, device, equipment and storage medium for AI intelligent inventory, including the following steps: determining whether the inventory asset data conforms to the inventory regulations; if it conforms, converting the format of the inventory asset data to obtain formal ledger information; conducting an information inventory on the formal ledger information to obtain an inventory result; determining whether the inventory equipment has a network, if there is no network, saving the inventory result in the inventory machine locally in the inventory equipment, and detecting and restoring the network of the inventory equipment; uploading the inventory result in the inventory machine locally to a preset server to generate an inventory report; if there is a network, uploading the inventory result to a preset server to generate an inventory report, solving the technical problem that the traditional inventory method is difficult to reflect the changes of assets in a timely manner, resulting in frequent occurrences of the problem of inconsistent accounts and actual situations, which affects the effectiveness and accuracy of asset management.

Description

Technical Field

[0001] The present invention relates to the field of information inventory technology, and in particular to an AI intelligent inventory method, device, equipment and storage medium. Background Art

[0002] With the development of information technology, the management of fixed assets has become increasingly complex in all kinds of organizations. The traditional manual inventory method is not only time-consuming and labor-intensive, but also prone to errors, especially in large enterprises or institutions, where assets are of various types and widely distributed, which makes it a difficult task to accurately track and record the location and status of each asset. In addition, due to the lack of real-time performance, traditional inventory methods are difficult to reflect asset changes in a timely manner, resulting in frequent discrepancies between accounts and actual assets, affecting the effectiveness and accuracy of asset management. Therefore, how to use modern technology to improve inventory efficiency, ensure data accuracy, and realize dynamic updates of asset information has become a problem that needs to be solved urgently.

[0003] A key issue in the research background is that the existing inventory methods are not adaptable enough to large-scale fixed assets. At present, most entities still rely on regular manual inspections, which is inefficient and cannot meet the rapidly changing market needs. At the same time, manual operations are affected by subjective factors, which can easily lead to omissions or erroneous records, thereby reducing the reliability of inventory results. In addition, even if some companies have introduced technologies such as electronic tags to assist inventory, these technologies often require special equipment support in specific environments, which increases implementation costs and technical barriers. Therefore, it is particularly urgent to develop an intelligent and more automated inventory method that can both improve work efficiency and reduce operating costs.

[0004] In response to the above challenges, the "AI Smart Inventory" method came into being. This method uses advanced AI algorithms and mobile Internet technology to achieve efficient and accurate inventory of fixed assets. Through the preset inventory equipment combined with the built-in AI algorithm, it can automatically identify and process physical asset information, convert it into structured ledger data in real time, and verify it according to national standards. More importantly, the design of this solution takes into account the network stability issues in actual application scenarios. It can save the inventory results in an offline state and automatically upload them to the server after the network is restored, ensuring the security and integrity of data transmission. In short, "AI Smart Inventory" provides a new solution that effectively solves many problems existing in the traditional inventory process. Summary of the invention

[0005] The main purpose of the present invention is to provide an AI intelligent inventory method, device, equipment and storage medium, which solves the technical problem that traditional inventory methods are difficult to reflect asset changes in a timely manner, resulting in frequent discrepancies between accounts and actual assets, affecting the effectiveness and accuracy of asset management.

[0006] To achieve the above object, the present invention provides an AI intelligent inventory method, comprising the following steps:

[0007] Conducting a real-time physical inventory of the target fixed assets through a preset inventory counting device to obtain inventory counted asset data, and judging whether the inventory counted asset data complies with inventory count regulations through a preset judgment mechanism; wherein the inventory count regulations include the use of asset information and national standard information;

[0008] If it is in compliance, the asset inventory data is formatted and formal ledger information is obtained;

[0009] The formal ledger information is counted by the AI ​​algorithm in the inventory APP downloaded by the inventory device to obtain the inventory result; wherein the inventory result includes normal inventory information and surplus inventory information;

[0010] Determine whether the inventory counting device has a network. If not, save the inventory counting result locally in the inventory counting device, and detect and restore the network of the inventory counting device; upload the inventory counting result locally in the inventory counting machine to a preset server by using the restored inventory counting device to generate an inventory counting report;

[0011] If there is a network, the inventory results are uploaded to a preset server to generate an inventory report.

[0012] Furthermore, the real-time physical inventory of the target fixed assets is performed by using a preset inventory counting device to obtain the inventory counted asset data, including:

[0013] Scanning the label of the target fixed asset using a scanner provided on the inventory counting device to obtain asset identification information;

[0014] The asset identification information is parsed to obtain asset basic data; wherein the asset basic data includes asset name, specification model and purchase date;

[0015] Taking an image of the target fixed asset by an image acquisition module provided on the inventory counting device to obtain asset image data;

[0016] Performing status recognition on the asset image data to obtain asset status information;

[0017] Through the data verification mechanism, the asset basic data and the asset status information are verified to obtain the asset verification result;

[0018] Determine whether there is information inconsistency in the asset verification result. If there is, record the inconsistent asset status information on the target fixed asset through the exception handling module on the inventory device, and replace the label on the target fixed asset correspondingly based on the inconsistent asset status information;

[0019] If not, sort out the asset verification result based on the information aggregation algorithm to obtain the inventory asset data; among them, the inventory asset data includes an asset detailed list and a status report.

[0020] Furthermore, the inventory device is equipped with a ledger format converter and an information integrator to convert the format of the inventory asset data to obtain the formal ledger information, including:

[0021] Remove redundancy from the inventory asset data through a data cleaning engine to obtain standardized data;

[0022] Regularize the format of the standardized data to obtain a normalized data structure;

[0023] Perform semantic annotation on the normalized data structure based on a semantic parser to obtain semantic annotation data; among them, the semantic annotation data includes asset category labels, attribute associations, and usage scenarios;

[0024] Use the ledger format converter to reorganize the structure of the semantic annotation data to obtain a fixed asset ledger; among them, the fixed asset ledger includes usage records and changes;

[0025] Based on the information integrator, perform a summary process on the fixed asset ledger to obtain the formal ledger information; among them, the formal ledger information includes an asset list and usage status.

[0026] Furthermore, the inventory result includes normal inventory information and inventory surplus information. Perform an information inventory on the formal ledger information through the AI algorithm in the preset inventory APP to obtain an inventory result, including:

[0027] Perform asset registration on the formal ledger information through the preset blockchain technology to obtain asset chain information;

[0028] Perform an information inventory on the historical target fixed asset based on the asset chain information to obtain a preliminary inventory result;

[0029] Perform a difference analysis on the asset status verification result through the AI algorithm in the preset inventory APP to obtain a difference analysis result;

[0030] If the difference analysis result shows that the historical target fixed asset is consistent with the formal ledger information, the preliminary inventory result is normal inventory information;

[0031] If the result of the difference analysis shows that there is a difference between the historical target fixed assets and the formal ledger information, the preliminary inventory result is overstock information.

[0032] Furthermore, determine whether the inventory device has a network. If there is no network, save the inventory result in the local inventory machine of the inventory device, and detect and restore the network of the inventory device; use the restored inventory device to upload the inventory result in the local inventory machine to a preset server to generate an inventory report, including:

[0033] Determine whether the inventory device has a network. If there is no network and the inventory result is normal inventory information, use the AI algorithm in the inventory APP to record information about the target fixed assets based on the normal inventory information to obtain normal inventory record information;

[0034] Store the normal inventory record information in the SQLite database of the inventory device to obtain the stored normal inventory record information;

[0035] Real-time automatically monitor and detect the network status in the inventory device through the inventory device to obtain the network status;

[0036] If the network status shows that the network has been fully restored, upload the stored normal inventory record information to the asset inventory system in the target PC through the inventory APP;

[0037] Through the asset inventory system, extract the data fields of the stored normal inventory record information and detect whether the data fields are correct. If correct, click the save button on the inventory device to add a timestamp to the stored normal inventory record information to obtain an inventory report;

[0038] If the inventory device has no network and the inventory result is overstock information, synchronously upload the overstock information to the asset inventory system in the target PC in real time;

[0039] Through the asset inventory system, extract the data fields of the stored normal inventory record information and detect whether the data fields are correct. If correct, click the save button on the inventory device to add a timestamp to the stored normal inventory record information to obtain an inventory report.

[0040] Furthermore, the storing the normal inventory record information in the SQLite database of the inventory device to obtain the stored normal inventory record information includes:

[0041] Analyze the data format of the normal inventory record information to obtain data structure information;

[0042] Select a preset compression algorithm in the database based on the data structure information;

[0043] Compress the normal inventory record information based on the preset compression algorithm to obtain the compressed normal inventory record information;

[0044] Perform block partitioning on the compressed normal inventory record information through a clustering algorithm to obtain block record data;

[0045] Store the block record data in partitions through an SQLite database to obtain the stored normal inventory record information.

[0046] Further, if there is a network, upload the inventory result to a preset server to generate an inventory report, including:

[0047] If the inventory device has a network and the inventory result is normal inventory information, automatically generate physical information for the normal inventory information through the asset inventory system to obtain detailed information on the target fixed assets for inventory;

[0048] Perform information verification on the detailed information of the target fixed assets based on a preset verification mechanism to verify whether the data fields in the detailed information of the fixed assets are correct;

[0049] If it is correct, save the detailed information of the target fixed assets through the asset inventory system to obtain the saved detailed information of the target fixed assets;

[0050] Modify the inventory result field in the formal ledger information corresponding to the saved detailed information of the target fixed assets to obtain the formal ledger information with the inventory result of "inventory normal";

[0051] Upload the formal ledger information with the inventory result of "inventory normal" to a preset server to generate an inventory report;

[0052] If the inventory device has a network and the inventory result is inventory surplus information, perform data verification on the inventory surplus information through the asset inventory system to verify whether the data fields in the detailed information of the fixed assets are correct;

[0053] If it is correct, upload the inventory surplus information to a preset server to generate an inventory report.

[0054] The present invention also provides an AI intelligent inventory device, including:

[0055] The first inventory module is used to perform real-time physical inventory on target fixed assets through a preset inventory device, obtain inventory asset data, and judge whether the inventory asset data complies with the inventory regulations through a preset judgment mechanism; wherein, the inventory regulations include usage asset information and national standard information;

[0056] The conversion module is used to, if it complies, convert the format of the inventory asset data to obtain formal ledger information;

[0057] The second inventory module is used to perform information inventory on the formal ledger information through the AI algorithm in the inventory APP downloaded by the inventory device to obtain an inventory result; wherein, the inventory result includes normal inventory information and inventory surplus information;

[0058] The detection module is used to judge whether the inventory device has a network. If there is no network, save the inventory result in the inventory machine local of the inventory device, and detect and restore the network of the inventory device; use the restored inventory device to upload the inventory result in the inventory machine local to a preset server to generate an inventory report;

[0059] The generation module is used to, if there is a network, upload the inventory result to a preset server to generate an inventory report.

[0060] The present invention also provides a computer device, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps of the method described in any one of the above are implemented.

[0061] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in any one of the above are implemented.

[0062] The AI intelligent inventory method provided by the present invention includes the following steps: Conduct real-time physical inventory of target fixed assets through a preset inventory device to obtain inventory asset data, and use a preset judgment mechanism to determine whether the inventory asset data meets the inventory regulations; wherein, the inventory regulations include information on the use of assets and national standard information; if it meets the requirements, convert the format of the inventory asset data to obtain official ledger information; conduct information inventory on the official ledger information through the AI algorithm in the inventory APP downloaded by the inventory device to obtain an inventory result; wherein, the inventory result includes normal inventory information and inventory surplus information; determine whether the inventory device has a network. If there is no network, save the inventory result in the inventory machine local of the inventory device, and detect and restore the network of the inventory device; use the restored inventory device to upload the inventory result in the inventory machine local to a preset server to generate an inventory report; if there is a network, upload the inventory result to a preset server to generate an inventory report. Through the above technical means, the technical problem that the traditional inventory method is difficult to reflect the changes of assets in a timely manner, resulting in frequent occurrences of discrepancies between accounts and actual situations, which affects the effectiveness and accuracy of asset management is solved. It realizes real-time physical inventory through a preset inventory device and combines the asset data automatically processed by the AI algorithm. This method greatly reduces the need for manual intervention, thereby improving the speed and accuracy of inventory. At the same time, the built-in judgment mechanism is used to ensure that the inventory asset data meets the specified standards, which helps to reduce the occurrence of human errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 is a schematic diagram of the steps of the AI intelligent inventory method in an embodiment of the present invention;

[0064] Figure 2 is a structural block diagram of the AI intelligent inventory device in an embodiment of the present invention;

[0065] Figure 3 is a schematic structural diagram of a computer device in an embodiment of the present invention.

[0066] The realization, functional characteristics and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0067] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0068] As Figure 1 shown, Figure 1Schematic diagram of the steps of an AI intelligent inventory method in an embodiment of the present invention;

[0069] An embodiment of the present invention provides an AI intelligent inventory method, including the following steps:

[0070] Step S1, perform real-time physical inventory on target fixed assets through a preset inventory device to obtain inventory asset data, and judge whether the inventory asset data meets the inventory regulations through a preset judgment mechanism; wherein, the inventory regulations include used asset information and national standard information.

[0071] Specifically, when implementing the steps described above, first, it is necessary to conduct a real-time physical inventory of the target fixed assets through the preset inventory equipment. Here, the "preset inventory equipment" refers to a hardware device specially designed for asset inventory. It can be a mobile terminal equipped with sensors such as a barcode scanner, RFID reader or camera, such as a tablet or a dedicated handheld device. These devices can identify and record the existence and characteristics of physical assets, thereby obtaining detailed inventory asset data. Next, in order to ensure that the collected information is accurate and consistent with the enterprise's asset use information and national standard information, a preset judgment mechanism is integrated into the system. This judgment mechanism is a built-in software logic or algorithm that evaluates the obtained inventory asset data based on pre-set standards and rules. For example, if a fixed asset should be accompanied by a specific safety mark or label in accordance with national standards, then the judgment mechanism will check whether the corresponding inventory data contains this information. Similarly, for asset use information, that is, the specific regulations defined within the enterprise on how to properly use, maintain and manage assets, the judgment mechanism will also verify whether the actual inventory data meets these requirements. For example, in a large manufacturing plant application scenario, suppose a batch of machines and equipment are included in the inventory as fixed assets. When the staff uses the preset inventory equipment to scan these machines one by one, the unique identification (which may be a QR code or an RFID tag) on ​​each machine will be read, and the generated inventory asset data includes but is not limited to information such as equipment number, model, location, and status. Subsequently, this information will be analyzed through a preset judgment mechanism to determine whether they comply with the established inventory regulations. For example, according to relevant national standards, certain types of machines must undergo regular safety inspections and be affixed with the latest inspection pass labels; and from the perspective of the company's own asset information, these machines may also be required to be located in a designated work area and maintained in good operating condition. Once any discrepancies are found, the system will immediately mark them for follow-up processing. In this process, the preset judgment mechanism plays a vital role. It not only ensures the efficiency and accuracy of the inventory work, but also promotes the standardization and standardization of asset management. In this way, whether it is for production equipment in the manufacturing industry or fixed assets in other industries, more scientific and reasonable management and supervision can be achieved.

[0072] Step S2: If it meets the requirements, the asset inventory data is formatted and formal ledger information is obtained.

[0073] Specifically, in the steps mentioned above, if it is confirmed through a preset judgment mechanism that the inventory asset data complies with the inventory regulations, the next step will be a crucial conversion process, that is, to convert the format of the inventory asset data to obtain the formal ledger information. This process is an important link to ensure that the inventory results can be effectively utilized and managed. Specifically, the format conversion involves reorganizing and standardizing the original data collected from the on-site physical inventory according to established standards and structures to ensure that this data can be seamlessly integrated into the enterprise's asset management information system. For example, in the application scenario of a large manufacturing factory, when the staff completes the physical inventory of a batch of machinery and equipment, the preset inventory equipment they use has recorded the relevant information of each machine, such as equipment number, model, location, and status. However, these original inventory asset data may not be directly applicable to the enterprise's internal management system because different systems often have their own data formats and input requirements. Therefore, in order to make this data part of the formal ledger information, format conversion is required. This conversion process is usually automatically executed by the software algorithm built into the inventory APP, which will adjust the inventory asset data to a format compatible with the enterprise's existing ledger system according to pre-set rules. This includes, but is not limited to, mapping data fields to the correct table columns, unifying the representation of dates and times, and ensuring that all necessary metadata is included, such as asset classification codes or department attribution information. Further, format conversion is not only to meet specific technical requirements but also to improve the quality and consistency of data. During this process, any inconsistent or missing information will be marked for subsequent manual review or supplementation. In addition, for some complex assets, additional processing steps may be required, such as merging multiple related records into a complete asset entry, or vice versa, splitting a comprehensive record into multiple independent components. Such processing ensures that the finally generated formal ledger information not only accurately reflects the true situation of physical assets but also facilitates subsequent querying, analysis, and report generation. In short, through this format conversion process, the real-time obtained inventory asset data can be effectively converted into valuable formal ledger information for the enterprise, thus supporting more efficient and accurate asset management practices.

[0074] Step S3, perform an information inventory on the formal ledger information through the AI algorithm in the inventory APP downloaded by the inventory device to obtain an inventory result; wherein, the inventory result includes normal inventory information and overage information.

[0075] Specifically, in the steps mentioned above, the process of information inventory of the official ledger information by the AI algorithm in the inventory APP downloaded through the inventory device is a key link in achieving efficient and accurate asset management. This process not only relies on advanced technical means but also combines the application of intelligent algorithms to ensure detailed and accurate inventory results, including normal inventory information and surplus inventory information. Specifically, in the application scenario of a large manufacturing factory, when the staff completes the physical inventory of fixed assets and converts the collected raw data into official ledger information, the next step is to use the inventory APP installed on the preset inventory device for further information processing. This inventory APP is embedded with specially designed AI algorithms that can automatically analyze and compare the official ledger information with the records in the enterprise's internal database to evaluate the actual status of the assets. For example, the AI algorithm can identify whether the assets that should be present according to the plan actually appear in the inventory results and whether their status meets the expectations, thus generating the so-called normal inventory information. At the same time, for those additional assets that unexpectedly appear in the inventory results, that is, assets not originally recorded in the ledger, the AI algorithm will also mark them, forming part of the surplus inventory information. For example, assume a manufacturing factory regularly conducts an inventory of the machinery and equipment on its production line. After completing the physical inventory and formatting it into official ledger information, the staff starts the inventory APP on the inventory device, and the APP immediately uses its built-in AI algorithm to process this official ledger information. During this process, the AI algorithm will check whether the latest inventory records of each piece of machinery and equipment match the previously stored data. If everything is normal, then this information will be classified as normal inventory information; while if there are new pieces of machinery and equipment that did not appear in the previous records, then the information of these newly added equipment will be identified as surplus inventory information. The AI algorithm can further analyze this surplus inventory information to try to find possible reasons, such as newly purchased equipment not yet being entered into the system or duplicate records caused by misoperations. In addition, the AI algorithm can not only simply compare numbers and entries but also predict potential problems based on historical data and trends. For example, it can predict the possible wear and tear or update requirements of certain types of equipment based on the inventory situation in the past few years. This forward-looking analysis helps the enterprise make preparations in advance and optimize the asset management strategy. Generally speaking, the information inventory of the official ledger information by the AI algorithm in the inventory APP downloaded through the inventory device not only improves the efficiency and accuracy of the inventory work but also provides the enterprise with deeper data insights to support more scientific and reasonable asset management decisions.

[0076] Step S4: Determine whether the inventory device has a network. If there is no network, save the inventory result in the local inventory machine of the inventory device, and detect and restore the network of the inventory device; use the restored inventory device to upload the inventory result in the local inventory machine to a preset server to generate an inventory report.

[0077] Specifically, in the steps described above, determining whether the inventory device has a network connection is a crucial step. This process ensures the continuity of the inventory work and the security of data even under poor network conditions. Specifically, when the inventory device completes the information inventory of the official ledger information and obtains an inventory result containing normal inventory information and inventory surplus information, the system will automatically check whether the inventory device currently has an effective network connection. If the inventory device is in a networkless state, then to avoid interrupting the entire inventory process, the system will immediately save the inventory result in the local storage space of the inventory machine in the inventory device. Taking a large manufacturing factory as an example, assume that during the physical inventory of machinery and equipment, the inventory personnel find that the wireless network signal in the area is unstable or completely missing. At this time, according to the preset logic, the inventory APP will not stop working or lose the collected data due to network problems. Instead, it will quickly respond and safely save all the generated inventory results to the local storage of the inventory device. This design not only protects the integrity of the data but also allows the inventory personnel to continue with subsequent work without being affected by the network status. Next, the system will continuously monitor and attempt to restore the network connection of the inventory device. This usually involves a series of automated detection and troubleshooting operations, such as reconnecting to the nearest wireless access point, switching to an alternative network connection (such as mobile data), or prompting the user to take necessary manual intervention measures to solve the problem. Once the network connection is successfully restored, the inventory device can immediately upload the previously saved inventory result to a preset server. During this process, the system will also verify the integrity and accuracy of the data transmission to ensure that no information is omitted or damaged. Finally, when all the inventory results are successfully uploaded to the server, the system will automatically generate a detailed inventory report. This report not only summarizes the status of all inventoried assets but also includes records of any abnormal situations, such as inventory surplus information, etc. In this way, even in a complex industrial environment, the enterprise can timely obtain the latest asset information, provide accurate data support for the management, and thus optimize the asset management strategy. In short, through the above mechanism, this solution effectively solves the risk of data loss that may be caused by unstable networks, while ensuring the efficiency and reliability of the inventory work, making the enterprise more proficient in asset management.

[0078] Step S5: If there is a network, upload the inventory result to a preset server to generate an inventory report.

[0079] Specifically, in the steps mentioned above, if the inventory device has a network connection, the inventory results will be uploaded to a preset server to generate an inventory report. This process is a key link to ensure that the inventory data can be integrated into the enterprise asset management system in a timely and accurate manner. Specifically, when the inventory device completes the information inventory of the official ledger information and obtains the inventory results including normal inventory information and surplus inventory information, the system will first confirm whether the inventory device has an effective network connection. If there is indeed an available network connection, the next step is to securely transmit these inventory results to the preset server. Taking a large manufacturing plant as an example, after the physical inventory is completed, the inventory device used by the staff has collected and processed all the relevant asset data to form the final inventory results. At this time, assuming that the inventory device is in an environment with a stable network connection, the inventory APP will automatically start the data upload process. To ensure the security and integrity of data transmission, an encrypted communication protocol, such as HTTPS or SSL / TLS, is usually adopted between the inventory device and the preset server to prevent sensitive information from being intercepted or tampered with during transmission. At the same time, data compression and format optimization may be performed before uploading to improve the transmission efficiency and reduce bandwidth occupancy. Once the inventory results are successfully uploaded to the preset server, the application program on the server side will immediately start processing the received data. This processing process includes, but is not limited to, verifying the integrity and consistency of the data, checking for duplicate records, and integrating the new inventory information into the existing asset database. At this stage, the server may also run a series of quality control algorithms, such as cross-checking the inventory records at different time periods to detect any potential anomalies. In this way, it can be ensured that the uploaded inventory results not only accurately reflect the actual status of the current assets, but also are consistent with the historical data. Finally, when all the inventory results have been strictly verified and integrated, the server will automatically generate a detailed inventory report. This report not only summarizes the status of all inventoried assets, but also includes records of any abnormal situations, such as surplus inventory information. For management, this inventory report provides valuable data support, helping them make more scientific and reasonable asset management decisions. For example, in the application scenario of a manufacturing plant, management can evaluate whether the equipment configuration on the production line is reasonable based on the latest inventory report, whether there are idle or redundant assets that need to be reallocated, or whether it is necessary to purchase new equipment to meet production requirements. In short, through the above mechanism, this solution not only realizes the efficient upload of inventory results and report generation, but also brings higher transparency and accuracy to the enterprise's asset management.

[0080] In a specific embodiment, the real-time physical inventory of the target fixed assets by the preset inventory device to obtain the inventory asset data includes:

[0081] Use a scanner set on the inventory device to scan the tags of the target fixed assets to obtain asset identification information;

[0082] Parse and process the asset identification information to obtain basic asset data; among them, the basic asset data includes asset name, specification model and purchase date;

[0083] Use an image acquisition module set on the inventory device to take images of the target fixed assets to obtain asset image data;

[0084] Perform status recognition on the asset image data to obtain asset status information;

[0085] Through a data verification mechanism, check the basic asset data and the asset status information to obtain an asset verification result;

[0086] Judge whether there is information inconsistency in the asset verification result. If there is, record the inconsistent asset status information on the target fixed assets through the exception handling module on the inventory device, and replace the tags on the target fixed assets corresponding to the inconsistent asset status information;

[0087] If not, organize the asset verification result based on an information aggregation algorithm to obtain the inventory asset data; among them, the inventory asset data includes an asset detailed list and a status report.

[0088] Specifically, in the steps mentioned above, the process of conducting real-time physical inventory of target fixed assets through a preset inventory device is a key link in achieving efficient and accurate asset management. This process not only relies on advanced hardware facilities but also combines the application of intelligent algorithms to ensure detailed and reliable inventory asset data. Specifically, the entire inventory process begins with using a scanner set on the inventory device to scan the labels of target fixed assets to obtain asset identification information. Here, the "labels" can be barcodes, QR codes, RFID tags, etc., which usually contain the basic identification features of the assets. Taking a large manufacturing factory as an example, assume that the staff is conducting a physical inventory of a batch of machinery and equipment. First, they use the inventory device equipped with a scanning function to scan the labels on these machines one by one. Each label stores specific information, such as equipment number, serial number, etc. When the scanner reads these labels, it can quickly and accurately obtain the corresponding asset identification information. Next, the system will analyze and process these asset identification information to extract the basic asset data. The basic asset data includes, but is not limited to, asset name, specification model, and purchase date, which are crucial for subsequent data verification and management. After obtaining the basic asset data, in order to further verify the status of the assets, the inventory device will use its built-in image acquisition module to take pictures of the target fixed assets. This step aims to capture the current physical state of the assets, such as whether there is damage, whether it is in the normal working position, etc. The asset image data obtained by taking pictures will be sent into the AI algorithm for status identification to generate asset status information. In this process, the AI algorithm may apply computer vision technology to automatically analyze the image content to determine whether there are abnormal conditions with the assets, such as wear, rust, or other forms of damage. With the completion of the collection of basic asset data and asset status information, the next step is to check and compare the two through a data verification mechanism to obtain the asset verification result. This verification process is to ensure that the collected data is accurate and matches the records within the enterprise. For example, in the context of a manufacturing factory, if the label of a machine shows that it was purchased three years ago, but the image recognition result shows that the machine looks very new, then such an inconsistency will be marked. If information inconsistencies are found in the asset verification result, the abnormal processing module on the inventory device needs to record the inconsistent asset status information on the target fixed assets. At the same time, based on these inconsistent information, the staff may need to replace the labels on the target fixed assets accordingly to ensure that all records are up-to-date and accurate. However, if no information inconsistencies are found after checking, then the system will sort out the asset verification result based on the information aggregation algorithm and finally obtain the inventory asset data including the asset inventory list and status report.The asset inventory list mentioned here details all the inventoried fixed assets and their related information, while the status report provides a specific description of the current condition of each asset. Such detailed asset inventory data not only provides an important basis for management decisions but also makes the enterprise's asset management more transparent and efficient. For example, in a manufacturing plant, managers can evaluate whether the equipment configuration on the production line is reasonable based on the latest asset inventory data, whether there are idle or redundant assets that need to be reallocated, or whether it is necessary to purchase new equipment to meet production needs. In addition, by regularly updating and maintaining this data, the enterprise can better track the life cycle of assets, plan maintenance schedules in advance, reduce unexpected downtime, and improve overall operational efficiency. In short, through the above mechanism, this solution realizes an integrated process from physical inventory to data collation, bringing significant advantages to the enterprise's asset management.

[0089] In a specific embodiment, the inventory device is equipped with a ledger format converter and an information integrator to convert the format of the inventory asset data to obtain formal ledger information, including:

[0090] Redundant data in the inventory asset data is removed through a data cleaning engine to obtain standardized data;

[0091] The format of the standardized data is regularized to obtain a normalized data structure;

[0092] Semantic annotation is performed on the normalized data structure based on a semantic parser to obtain semantic annotation data; among them, the semantic annotation data includes asset category labels, attribute associations, and usage scenarios;

[0093] The semantic annotation data is restructured using the ledger format converter to obtain a fixed asset ledger; among them, the fixed asset ledger includes usage records and changes;

[0094] Based on the information integrator, summary processing is performed on the fixed asset ledger to obtain the formal ledger information; among them, the formal ledger information includes an asset list and usage status.

[0095] Specifically, in the steps described above, the inventory device is equipped with an account format converter and an information integrator. Through a series of complex processing procedures, the format of the inventoried asset data is converted, and finally, the official account information is obtained. This process not only ensures the accuracy and consistency of the data but also improves the readability and usability of the data, providing a solid foundation for the enterprise's asset management. Specifically, in the application scenario of a large manufacturing factory, when the staff has completed the physical inventory of fixed assets and collected the inventoried asset data including the asset inventory list and status report, the next step is to convert its format. First, the redundant data in the inventoried asset data is removed through a data cleaning engine, which is a crucial preprocessing step. The data cleaning engine will automatically identify and remove duplicate or unnecessary data items. For example, if multiple pieces of machinery and equipment have the same serial number or certain asset records appear multiple times, these redundant information will be effectively cleaned, thus obtaining standardized data. This standardized data is the basis for all subsequent processing, ensuring the purity and accuracy of the data set. Then, in order to make these standardized data more widely applicable and understandable, the system will regularize their format to obtain a normalized data structure. This step involves adjusting the organization method of the data to conform to specific standards or protocols. For example, unifying the date format, numerical representation method, etc., to ensure seamless docking of data from different sources. For a manufacturing factory, this means that all machine equipment records will adopt a consistent format, facilitating subsequent analysis and management. The normalized data structure not only improves the quality of the data but also lays a good foundation for subsequent semantic parsing. Subsequently, semantic annotation is performed on the normalized data structure based on a semantic parser to obtain semantically annotated data. Here, semantic annotation refers to adding additional metadata to the data, such as asset category labels, attribute associations, and usage scenarios. For example, for a numerically controlled machine tool, the semantic parser may assign it a category label of "production equipment" and indicate its key attributes (such as accuracy level, working range) and main application scenarios (such as for machining specific types of parts). In this way, not only can the function and use of each asset be understood more intuitively, but also rich background information can be provided for advanced data analysis. Further, the structure of the semantically annotated data is reorganized using the account format converter to obtain a fixed asset account. The role of the account format converter is to rearrange and combine data elements to form a fixed asset account with clear logic and easy query. In this process, each asset entry will include detailed usage records and changes, such as the time point when a certain piece of equipment is transferred from one workshop to another or the specific content of the most recent maintenance. Such detailed account records provide a comprehensive historical view for the enterprise, helping to track the entire life cycle of the assets.Finally, based on the information integrator, the fixed asset ledger is summarized to obtain the formal ledger information. The task of the information integrator is to comprehensively analyze the data scattered in each ledger entry and generate a complete formal ledger information, which includes an asset list and usage status. For a manufacturing plant, this formal ledger information is not only the core document for enterprise asset management but also an important basis for management decision-making. For example, based on the latest formal ledger information, management can evaluate whether the equipment configuration on the production line is reasonable, whether there are idle or surplus assets that need to be reallocated, or whether it is necessary to purchase new equipment to meet production needs. For instance, in a manufacturing plant, managers can quickly find the detailed information of any piece of equipment through the formal ledger information, including its location, status, historical maintenance records, etc. In addition, by regularly updating and maintaining this information, the enterprise can better track the asset life cycle, plan maintenance schedules in advance, reduce unexpected downtime, and improve overall operational efficiency. In summary, through the above mechanism, this solution realizes a series of efficient conversions from the original inventory asset data to the formal ledger information, not only enhancing the value of the data but also promoting the scientific and refined management of assets.

[0096] In a specific embodiment, the inventory result includes normal inventory information and surplus inventory information. The formal ledger information is inventoried through the AI algorithm in the preset inventory APP to obtain the inventory result, including:

[0097] The formal ledger information is registered for assets through the preset blockchain technology to obtain asset chain information;

[0098] Based on the asset chain information and historical target fixed assets, an initial inventory result is obtained through information inventory;

[0099] The difference analysis is performed on the asset status verification result through the AI algorithm in the preset inventory APP to obtain the difference analysis result;

[0100] If the difference analysis result shows that the historical target fixed assets are consistent with the formal ledger information, the initial inventory result is normal inventory information;

[0101] If the difference analysis result shows that there are differences between the historical target fixed assets and the formal ledger information, the initial inventory result is surplus inventory information.

[0102] Specifically, in the steps described above, the process of information inventory of the official ledger information through the AI algorithm in the preset inventory APP combines blockchain technology and smart contracts to ensure a detailed and credible inventory result. This process not only improves the security and transparency of data but also enhances the accuracy and efficiency of the inventory work. Specifically, the entire process starts with asset registration of the official ledger information through the preset blockchain technology, and then generates asset chain information. This step lays a solid foundation for subsequent information inventory and verification. Taking a large manufacturing factory as an example, assuming that the staff has completed the physical inventory of fixed assets and collected the official ledger information including the asset inventory list and status report, the next step is to process this information in detail. First, the system uses the preset blockchain technology to conduct asset registration on the official ledger information to obtain asset chain information. Here, "blockchain technology" refers to a distributed ledger technology that can record the status changes of all assets and ensure that these records cannot be tampered with. For example, in a manufacturing factory, the history of each piece of machinery and equipment from purchase to each location change and maintenance will be detailedly recorded on the blockchain, forming the so-called asset chain information. This asset chain information not only includes the basic attributes of the assets, such as name, model, purchase date, etc., but also all the status change records, thus providing a complete view of the asset life cycle. Based on the asset chain information, an information inventory is conducted with the historical target fixed assets to obtain a preliminary inventory result. This step involves comparing the asset records in the current official ledger information with the historical records on the blockchain to determine the actual status of each asset. For example, in a manufacturing factory, the staff may find that the location of some equipment has changed, or new equipment has been added to the production line. By comparing the asset chain information, it is possible to accurately identify which assets exist as planned (i.e., normal inventory information), and which are unexpectedly present or missing (i.e., surplus or deficit inventory information). This blockchain-based inventory method greatly improves the consistency and accuracy of data, avoiding omissions or errors that may occur in traditional manual inventories. To further ensure the authenticity and reliability of the inventory result, the system conducts a difference analysis on the asset status verification result through the AI algorithm in the preset inventory APP to obtain a difference analysis result. Here, "difference analysis" refers to using the AI algorithm to automatically detect the inconsistencies between the official ledger information and the historical target fixed assets. For example, in a manufacturing factory, if a piece of equipment is shown to be located in a certain workshop in the official ledger information but appears in another workshop in the blockchain record, then this difference will be marked. In this way, the system can not only identify the actual differences but also provide a clear direction for subsequent processing. If the difference analysis result shows that the historical target fixed assets are consistent with the official ledger information, the preliminary inventory result is normal inventory information.This means that the status of all assets is consistent with the historical records, and no abnormal situations are found. For a manufacturing plant, this indicates that all machinery and equipment are in their expected positions and in good condition. Conversely, if the result of the difference analysis shows a difference between the historical target fixed assets and the official ledger information, the preliminary inventory result is overstock information. This situation usually involves assets that appear unexpectedly and may require management to be informed as soon as possible and take corresponding measures. For example, in a manufacturing plant, if new equipment that was not previously recorded is discovered, the system will quickly mark this information as overstock information so that managers can update the asset inventory in a timely manner to avoid resource waste or duplicate purchases. For instance, in the application scenario of a manufacturing plant, assume that during a regular inventory, the staff discovers several newly installed CNC machine tools. Through the above process, the system will first register the relevant information of these new devices on the blockchain, then compare it with the existing ledger, and finally confirm it as overstock information. Subsequently, the system will verify the status of these new devices and conduct a difference analysis through an AI algorithm to confirm that these devices are indeed not in the original historical records. Finally, based on the result of the difference analysis, the system will mark these new devices as overstock information and notify the relevant personnel for further processing. In summary, through the above mechanism, this solution achieves an efficient, secure, and trustworthy information inventory of the official ledger information. This method not only solves the problems existing in traditional inventory work, such as poor data consistency and many manual errors, but also provides an enterprise with a brand-new asset management tool, promoting the scientific and refined management of assets. By combining blockchain technology and AI algorithms, the system not only ensures the authenticity and integrity of data but also improves the efficiency and accuracy of the inventory work, making the enterprise's asset management more transparent and efficient.

[0103] In a specific embodiment, it is determined whether the inventory device has a network. If there is no network, the inventory result is saved locally in the inventory machine of the inventory device, and the network of the inventory device is detected and restored; the inventory result in the inventory machine locally is uploaded to a preset server by using the restored inventory device to generate an inventory report, including:

[0104] It is determined whether the inventory device has a network. If there is no network and the inventory result is normal inventory information, the AI algorithm in the inventory APP is used to record information about the target fixed assets based on the normal inventory information to obtain normal inventory record information;

[0105] The normal inventory record information is stored in the SQLite database of the inventory device to obtain the stored normal inventory record information;

[0106] The network status of the inventory device is obtained by the inventory device through real-time automatic monitoring and detection of the network situation inside the inventory device;

[0107] If the network status indicates that the network has fully recovered, upload the stored normal inventory record information to the asset inventory system in the target PC through the inventory APP;

[0108] Through the asset inventory system, extract the data fields of the stored normal inventory record information, and detect whether the data fields are correct. If correct, click the save button in the inventory device to add a timestamp to the stored normal inventory record information to obtain an inventory report;

[0109] If the inventory device has no network and the inventory result is overstock information, synchronize and upload the overstock information to the asset inventory system in the target PC in real time;

[0110] Through the asset inventory system, extract the data fields of the stored normal inventory record information, and detect whether the data fields are correct. If correct, click the save button in the inventory device to add a timestamp to the stored normal inventory record information to obtain an inventory report.

[0111] Specifically, in the steps described above, determining whether the inventory device has a network connection is a key link to ensure the smooth progress of the inventory work. This process not only involves the assessment of the current network status but also includes how to properly handle the inventory results in case of network unavailability and finally upload these results to a preset server to generate an inventory report. Specifically, the entire process starts with determining whether the inventory device has a network. If there is no network, a series of measures will be taken to ensure the continuity of the inventory work and the security of the data. Taking a large manufacturing factory as an example, assume that workers are using a mobile device equipped with an inventory APP to conduct a physical inventory of a batch of machinery and equipment. After completing the information inventory and obtaining the results including normal inventory information and inventory surplus information, the system will first automatically check whether there is an available network connection for the inventory device. If it is found that the network is unavailable, the system will not interrupt the inventory process but immediately activate the backup plan, that is, through the AI algorithm in the inventory APP, based on the normal inventory information, record the information of the target fixed assets to obtain the normal inventory record information. In this case, even without a network connection, the inventory data can still be recorded and saved, ensuring the continuity of the work. Next, to prevent data loss or damage, the system will store the normal inventory record information in the SQLite database in the inventory device to obtain the stored normal inventory record information. SQLite is a lightweight relational database management system, especially suitable for embedded applications, and it can efficiently manage and query data in a local environment. In the application scenario of a manufacturing factory, this means that all normal inventory record information can be safely stored inside the inventory device until the network is restored. This step is crucial for ensuring data integrity, especially in a working environment with unstable network conditions. At the same time, the inventory device will automatically monitor and detect the internal network situation in real time to obtain the network status. This continuous monitoring mechanism can timely capture changes in the network condition. Once the network is fully restored, the system will immediately respond and upload the stored normal inventory record information to the asset inventory system in the target PC through the inventory APP. The target PC refers to the computer terminal used for asset management within the enterprise, and the asset inventory system is an application program specifically designed to process and analyze inventory data. In this way, even in the case of a short network interruption, it can be ensured that all inventory data can finally be accurately transmitted to the enterprise's central management system. Further, through the asset inventory system, the system will extract the data fields of the stored normal inventory record information and detect whether the data fields are correct. For example, in a manufacturing factory, managers can view the detailed information of each piece of machinery and equipment, such as model, purchase date, current location, etc., through the asset inventory system.If all data fields are correct, the user only needs to click the save button in the inventory device to add a timestamp to the stored normal inventory record information and obtain a formal inventory report. The addition of the timestamp not only marks the specific time of data upload but also enhances the traceability and reliability of the data. However, if the inventory device has no network and the inventory result is an overstocked inventory, different handling methods need to be adopted. In this case, the system will immediately synchronously upload the overstocked inventory information to the asset inventory system in the target PC in real time. This is because overstocked inventory information usually involves assets that appear unexpectedly and may require management to be aware of and take corresponding measures as soon as possible. For example, in a manufacturing factory, if new equipment that was not previously recorded is found, the system will quickly send this information to the asset inventory system so that managers can update the asset list in a timely manner to avoid resource waste or duplicate purchases. Finally, whether it is for normal inventory information or overstocked inventory information, the system will extract the corresponding data fields through the asset inventory system and detect whether these data fields are correct. If it is confirmed that they are correct, the user also needs to click the save button in the inventory device to add a timestamp to the stored normal inventory record information and obtain a formal inventory report. In this process, the addition of the timestamp provides a clear time reference for each asset, which is helpful for subsequent audits and management decisions. In short, through the above mechanism, this solution not only solves the risk of data loss that may be caused by unstable network but also ensures the efficiency and reliability of the inventory work. Whether the network is normal or abnormal, it can accurately record, securely store, and timely upload the inventory results, providing strong support for enterprises and promoting the scientific and refined management of asset management.

[0112] In a specific embodiment, storing the normal inventory record information in the SQLite database of the inventory device to obtain the stored normal inventory record information includes:

[0113] Analyze the data format of the normal inventory record information to obtain data structure information;

[0114] Select a preset compression algorithm in the database based on the data structure information;

[0115] Perform compression processing on the normal inventory record information based on the preset compression algorithm to obtain the compressed normal inventory record information;

[0116] Perform block division on the compressed normal inventory record information through a clustering algorithm to obtain block record data;

[0117] Perform partition storage on the block record data through the SQLite database to obtain the stored normal inventory record information.

[0118] Specifically, in the steps described above, the process of storing the inventory normal record information in the SQLite database of the inventory device not only involves simple data saving operations, but is also a systematic process that includes complex steps such as data format analysis, compression processing, and partition storage. This process aims to ensure the security, integrity, and efficient access of data, thereby providing a solid foundation for subsequent data uploading and management. Specifically, in the application scenario of a large manufacturing factory, when the staff uses the inventory device to complete the information inventory of fixed assets and obtains the inventory normal record information, the system will first perform data format analysis on this information to obtain data structure information. Here, "data format analysis" refers to determining the internal data types, field lengths, and arrangement orders and other characteristics by parsing the content and organization method of the inventory normal record information. For example, in a manufacturing factory, the inventory record of each machine equipment may include multiple fields such as equipment number, model, purchase date, and location coordinates. By analyzing these fields, the specific attributes of each asset and its representation form in the record can be accurately understood. This detailed structure information is crucial for subsequent data processing. It not only helps to select a suitable storage scheme, but also provides a prerequisite for the effective utilization of data. Based on the obtained data structure information, the next step is to select a preset compression algorithm in the database. This is because directly storing the original inventory normal record information in the SQLite database may occupy a large amount of space, especially in the case of large-scale fixed asset inventory. Therefore, in order to optimize the storage efficiency and reduce resource consumption, the system will select the most suitable compression algorithm according to the characteristics of the data structure. For example, if there is a large amount of duplicate or redundant information in the records of a certain type of asset, then a compression algorithm that can effectively remove these redundancies can be selected; while for records containing a large amount of numerical data, a compression method specifically for this type of data can be selected. In this way, the data volume can be significantly reduced while maintaining the integrity and readability of the data. Once the appropriate compression algorithm is selected, the system will perform compression processing on the inventory normal record information to obtain the compressed inventory normal record information. This compression process is not just simply reducing the file size. More importantly, it can achieve efficient storage without affecting the data quality. For example, in a manufacturing factory, assume that the inventory records of a batch of machine equipment, after compression processing, the data that originally occupied a large amount of memory now only requires a very small space to be completely saved. This not only increases the storage capacity of the SQLite database, but also speeds up the data reading and writing speed, making subsequent operations smoother. To further improve the data management and access efficiency, the system will also perform block partitioning on the compressed inventory normal record information through a clustering algorithm to obtain block record data. The clustering algorithm is a data analysis technique that can group similar data items into one category to form independent data blocks.This method is particularly applicable to datasets with specific patterns or correlations. In the example of a manufacturing factory, assume that all the machinery and equipment located in the same workshop are grouped into a data block, while the equipment in different workshops forms separate sub-blocks. Through such block processing, the information within each data block can be made more concentrated and orderly, facilitating quick location and retrieval. Finally, the partitioned record data is stored in a SQLite database, and the normal inventory record information after storage is obtained. SQLite, as a lightweight relational database management system, supports multiple storage strategies, including partitioned storage. In this case, the system will allocate the partitioned record data to different database partitions according to pre-set rules. For example, different partitions can be created based on timestamps or geographical locations, which can not only improve query efficiency but also simplify backup and recovery operations. For a manufacturing factory, this means that managers can easily search for and manage the inventory records of relevant assets according to specific conditions (such as time periods or workshops), greatly enhancing the convenience and accuracy of asset management. In summary, through the above mechanism, this solution realizes a series of efficient conversions from the original normal inventory record information to the normal inventory record information after storage. This process not only ensures the security and integrity of the data but also significantly improves the data storage efficiency and access speed, providing strong support for enterprises and promoting the scientific and refined management of assets.

[0119] In a specific embodiment, if there is a network, the inventory result is uploaded to a preset server to generate an inventory report, including:

[0120] If the inventory device has a network and the inventory result is normal inventory information, the asset inventory system automatically generates physical information from the normal inventory information to obtain detailed information on the target fixed assets for inventory;

[0121] Based on a preset verification mechanism, the detailed information on the target fixed assets is verified to verify whether the data fields in the detailed information on the fixed assets are correct;

[0122] If it is correct, the asset inventory system saves the detailed information on the target fixed assets to obtain the detailed information on the target fixed assets after saving;

[0123] Modify the inventory result in the formal ledger information corresponding to the detailed information on the target fixed assets after saving to obtain the formal ledger information with the inventory result of "Inventory Normal";

[0124] Upload the formal ledger information with the inventory result of "Inventory Normal" to a preset server to generate an inventory report;

[0125] If the inventory device has a network and the inventory result is overstock information, the asset inventory system is used to perform data verification and information verification on the overstock information to verify whether the data fields in the detailed fixed asset information are correct;

[0126] If it is correct, the overstock information is uploaded to a preset server to generate an inventory report.

[0127] Specifically, in the steps described above, the process of uploading the inventory result to a preset server and generating an inventory report if there is a network connection is a key link to ensure the smooth completion of the inventory work and timely update of the enterprise asset management system. This process not only relies on an effective network connection but also incorporates automated information processing and verification mechanisms to ensure data accuracy and consistency. Specifically, the entire process starts with determining whether the inventory device has a network connection, and then corresponding processing measures are taken according to different types of inventory results (i.e., normal inventory information or surplus inventory information). First, in the application scenario of a large manufacturing factory, assume that the staff has completed the information inventory of a batch of machinery and equipment using a mobile device equipped with an inventory APP and obtained a result containing normal inventory information and surplus inventory information. If the inventory device confirms that there is an available network connection and the inventory result is normal inventory information, the system will automatically generate physical information for the normal inventory information through the asset inventory system to obtain detailed information on the target fixed assets. The "physical information" mentioned here refers to the specific description corresponding to the actually existing assets, including but not limited to equipment name, model, purchase date, location coordinates, etc. For example, in a manufacturing factory, when the inventory result shows that a certain CNC machine tool is in normal use, the asset inventory system will automatically generate detailed information about this machine tool, such as the workshop where it is currently located and the time of the last maintenance, thus forming a complete fixed asset record. Next, to ensure the accuracy of these automatically generated detailed information on fixed assets, the system will perform information verification on the detailed information of the target fixed assets based on a preset verification mechanism. This verification mechanism involves a series of verification rules to check whether each data field in the detailed information of the fixed assets is correct. For example, the system may check whether the equipment number conforms to a specific format, whether the purchase date is reasonable, and whether the location coordinates are within a legal range. Through this strict verification process, data entry errors or inconsistencies can be effectively avoided, ensuring the quality of the basic data for subsequent operations. Once it is confirmed that all data fields are correct after verification, the system will save the detailed information of the target fixed assets through the asset inventory system to obtain the saved detailed information of the target fixed assets. This step means that all normal inventory information has been officially recorded and has become part of the enterprise asset management system. For the manufacturing factory, this means that managers can now view the latest status of each piece of machinery and equipment through the central system, thus better planning production arrangements and maintenance plans. Subsequently, to reflect the latest inventory result, the system will modify the inventory result in the formal ledger information corresponding to the saved detailed information of the target fixed assets to obtain the formal ledger information with the inventory result of "inventory normal". This field modification is to clearly identify which assets have been inventoried and are in good condition, facilitating subsequent management and auditing.For example, in a manufacturing factory, the updated official ledger information will show that the latest inventory count result of a certain CNC machine tool is "inventory normal". This not only facilitates querying but also enhances the transparency and reliability of the data. Finally, the system will upload the official ledger information with the inventory count result of "inventory normal" to a preset server to generate an inventory report. This upload process ensures that all the latest inventory data can be promptly integrated into the enterprise's central management system, enabling the management to obtain the most accurate asset status at any time. Meanwhile, the generated inventory report provides important decision-making basis for the management, helping them evaluate whether the equipment configuration on the production line is reasonable, whether there are idle or surplus assets that need to be reallocated, or whether it is necessary to purchase new equipment to meet production requirements. On the other hand, if the inventory device has a network and the inventory count result is information on inventory surplus, the system will perform data verification on the inventory surplus information through the asset inventory system, also to verify whether the data fields in the detailed information of the fixed assets are correct. For inventory surplus information, that is, those assets that unexpectedly appear in the inventory count result, strict verification is particularly important because they may be newly purchased equipment that has not been timely entered into the system, or assets that cannot be found in the original ledger for other reasons. For example, in a manufacturing factory, if a batch of previously unrecorded new equipment is discovered, the system will strictly verify the detailed information of these equipment to ensure that each piece of data is accurate. If it is confirmed to be correct after verification, the system will directly upload the inventory surplus information to a preset server to generate an inventory report. This immediate upload method ensures that the management can quickly understand the situation of the newly added assets, update the asset management system in a timely manner, and take necessary management measures, such as registering new equipment and arranging installation and commissioning. In short, through the above mechanism, this solution not only solves the problem of efficient upload of inventory count results under stable network conditions but also ensures the accuracy and consistency of the data through automated information processing and strict verification mechanisms. Whether dealing with normal inventory count information or inventory surplus information, it can achieve secure storage and timely update of the data, providing strong support for the enterprise and promoting the scientific and refined management of assets.

[0128] The method of AI intelligent inventory count in the embodiment of the present invention has been described above. Next, the system of AI intelligent inventory count in the embodiment of the present invention will be described. Please refer to Figure 2 , an embodiment of the system of AI intelligent inventory count in the embodiment of the present invention includes:

[0129] The first inventory count module 21 is used to perform real-time physical inventory on the target fixed assets through a preset inventory device to obtain inventory asset data, and judge whether the inventory asset data meets the inventory regulations through a preset judgment mechanism; wherein, the inventory regulations include the information of using assets and the national standard information;

[0130] A conversion module 22, configured to convert the inventory asset data into formal ledger information if it meets the requirements;

[0131] A second inventory module 23, configured to perform information inventory on the formal ledger information through an AI algorithm in the inventory APP downloaded by the inventory device to obtain an inventory result; wherein, the inventory result includes normal inventory information and inventory surplus information;

[0132] A detection module 24, configured to determine whether the inventory device has a network. If there is no network, save the inventory result in the inventory machine local of the inventory device, and detect and restore the network of the inventory device; use the restored inventory device to upload the inventory result in the inventory machine local to a preset server to generate an inventory report;

[0133] A generation module 25, configured to upload the inventory result to a preset server to generate an inventory report if there is a network.

[0134] In this embodiment, for the specific implementation of each unit in the above system embodiment, please refer to that described in the above method embodiment, and details are not described herein again.

[0135] Refer to Figure 3 , and in an embodiment of the present invention, a computer device is further provided. The internal structure of the computer device may be as Figure 3 shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface, and a database connected through a system bus. Among them, the processor of the computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. The computer program, when executed by the processor, implements the above method.

[0136] Those skilled in the art can understand that Figure 3 the structure shown in

[0137] is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0138] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium provided by the present invention and used in the embodiments can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0139] It should be noted that in this article, the terms "include", "comprise", or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, apparatus, article, or method including a series of elements not only includes those elements but also includes other elements not explicitly listed, or further includes elements inherent to such a process, apparatus, article, or method. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, apparatus, article, or method including that element.

[0140] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structural or equivalent process transformation made by using the specification and drawings of the present invention, or directly or indirectly applied to other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. An AI intelligent inventory method, characterized in that: The following steps are involved: Conducting a real-time physical inventory of the target fixed assets through a preset inventory counting device to obtain inventory counted asset data, and judging whether the inventory counted asset data complies with inventory count regulations through a preset judgment mechanism; wherein the inventory count regulations include the use of asset information and national standard information; If it is in compliance, the asset inventory data is formatted and formal ledger information is obtained; The formal ledger information is counted by using an AI algorithm in the inventory APP downloaded by the inventory device to obtain an inventory result; Determine whether the inventory counting device has a network. If not, save the inventory counting result locally in the inventory counting device, and detect and restore the network of the inventory counting device; upload the inventory counting result locally in the inventory counting machine to a preset server by using the restored inventory counting device to generate an inventory counting report; If there is a network, the inventory result is uploaded to a preset server to generate an inventory report; The real-time physical inventory of the target fixed assets is performed by the preset inventory counting equipment to obtain the inventory counted asset data, including: Scanning the label of the target fixed asset using a scanner provided on the inventory counting device to obtain asset identification information; The asset identification information is parsed to obtain asset basic data; wherein the asset basic data includes asset name, specification model and purchase date; Taking an image of the target fixed asset by an image acquisition module provided on the inventory counting device to obtain asset image data; Performing status recognition on the asset image data to obtain asset status information; Through the data verification mechanism, the asset basic data and the asset status information are verified to obtain the asset verification result; Determine whether there is any information inconsistency in the asset verification result. If so, record the inconsistent asset status information on the target fixed asset through the exception handling module on the inventory counting device, and replace the label on the target fixed asset accordingly based on the inconsistent asset status information; If it does not exist, the asset verification results are sorted out based on an information aggregation algorithm to obtain the asset inventory data; wherein the asset inventory data includes an asset list and a status report.

2. The AI ​​intelligent inventory method according to claim 1, characterized in that: The inventory counting device is provided with a ledger format converter and an information integrator, which converts the format of the inventory counted asset data to obtain formal ledger information, including: Eliminate redundancy from the inventory asset data through a data cleaning engine to obtain standardized data; Regularizing the format of the standardized data to obtain a standardized data structure; Performing semantic annotation on the normalized data structure based on a semantic parser to obtain semantic annotation data; wherein the semantic annotation data includes asset category labels, attribute associations, and usage scenarios; The semantically annotated data is restructured using the ledger format converter to obtain a fixed asset ledger; wherein the fixed asset ledger includes usage records and changes; The fixed asset ledger is summarized and processed based on the information integrator to obtain the formal ledger information; wherein the formal ledger information includes an asset list and usage status.

3. The AI ​​intelligent inventory method according to claim 1, characterized in that: The inventory result includes normal inventory information and surplus inventory information. The formal ledger information is inventoryed by the AI ​​algorithm in the preset inventory APP to obtain the inventory result, including: The asset chain information is obtained by registering the formal ledger information through the preset blockchain technology; Conducting information inventory based on the asset chain information and historical target fixed assets to obtain preliminary inventory results; Performing difference analysis on the preliminary inventory results through an AI algorithm in a preset inventory APP to obtain a difference analysis result; If the difference analysis result shows that the historical target fixed assets are consistent with the formal ledger information, the preliminary inventory result is normal inventory information; If the result of the difference analysis is that there is a difference between the historical target fixed assets and the formal ledger information, the preliminary inventory result will be surplus information.

4. The AI ​​intelligent inventory method according to claim 1, characterized in that: Determine whether the inventory counting device has a network. If not, save the inventory counting result locally in the inventory counting machine in the inventory counting device, and detect and restore the network of the inventory counting device; The restored inventory counting device is used to upload the inventory counting results in the local inventory counting machine to a preset server to generate an inventory counting report, including: Determine whether the inventory counting device has a network. If there is no network and the inventory counting result is normal inventory counting information, then use the AI ​​algorithm in the inventory counting APP to record information of the target fixed asset based on the normal inventory counting information to obtain normal inventory counting record information; The normal inventory record information is stored in the SQLite database in the inventory device to obtain the normal inventory record information after storage; The network status in the inventory device is automatically monitored and detected in real time by the inventory device to obtain the network status; If the network status shows that the network is fully restored, the stored post-inventory normal record information is uploaded to the asset inventory system in the target PC through the inventory APP; Extracting the data fields of the stored post-inventory normal record information through the asset inventory system, and checking whether the data fields are correct. If correct, clicking a save button in the inventory device to add a timestamp to the stored post-inventory normal record information to obtain an inventory report; If the inventory device has no network, and the inventory result is surplus information, the surplus information is saved and uploaded to the asset inventory system in the target PC in real time after the network is restored; The data fields of the excess information are extracted through the asset inventory system, and the data fields are checked to see if they are correct. If they are correct, a save button in the inventory device is clicked to add a timestamp to the excess information to obtain an inventory report.

5. The AI ​​intelligent inventory method according to claim 4, characterized in that: The storing the normal inventory record information in the SQLite database in the inventory counting device to obtain the stored normal inventory record information includes: Performing data format analysis on the normal inventory record information to obtain data structure information; Selecting a compression algorithm preset in the database based on the data structure information; Compressing the normal inventory record information based on a preset compression algorithm to obtain compressed normal inventory record information; The compressed normal record information is divided into blocks by a clustering algorithm to obtain block record data; The block record data is partitioned and stored through the SQLite database to obtain normal record information after storage.

6. The AI ​​intelligent inventory method according to claim 4, characterized in that: If there is a network, the inventory result is uploaded to a preset server to generate an inventory report, including: If the inventory counting device has a network, and the inventory counting result is normal inventory counting information, then the normal inventory counting information is automatically generated into physical information by the asset inventory counting system to obtain detailed information of the inventory counting target fixed assets; Performing information verification on the target fixed asset detailed information based on a preset verification mechanism to verify whether the data fields in the fixed asset detailed information are correct; If correct, the detailed information of the target fixed asset is saved through the asset inventory system to obtain the detailed information of the saved target fixed asset; Modify the field of the inventory result in the formal ledger information corresponding to the detailed information of the saved target fixed asset to obtain the formal ledger information with the inventory result of "normal"; Upload the official account information with the inventory result of "normal" to the preset server to generate an inventory report; If the inventory counting device has a network, and the inventory counting result is excess information, the excess information is verified by the asset inventory counting system to verify whether the data fields in the fixed asset detailed information are correct; If correct, the inventory surplus information is uploaded to a preset server to generate an inventory report.

7. An AI intelligent inventory device, characterized in that: include: The first inventory module is used to perform a real-time physical inventory of the target fixed assets through a preset inventory device to obtain inventory asset data, and to determine whether the inventory asset data complies with the inventory regulations through a preset judgment mechanism; wherein the inventory regulations include the use of asset information and national standard information; A conversion module is used to convert the format of the inventory asset data to obtain formal ledger information if it meets the requirements; The second inventory module is used to perform an information inventory on the formal ledger information through an AI algorithm in the inventory APP downloaded by the inventory device to obtain an inventory result; wherein the inventory result includes normal inventory information and surplus inventory information; The detection module is used to determine whether the inventory counting device has a network. If there is no network, the inventory counting result is saved locally in the inventory counting machine in the inventory counting device, and the network of the inventory counting device is detected and restored; the inventory counting result in the local inventory counting machine is uploaded to a preset server by using the restored inventory counting device to generate an inventory counting report; A generation module, used for uploading the inventory result to a preset server to generate an inventory report if there is a network; The real-time physical inventory of the target fixed assets is performed by the preset inventory counting equipment to obtain the inventory counted asset data, including: Scanning the label of the target fixed asset using a scanner provided on the inventory counting device to obtain asset identification information; The asset identification information is parsed to obtain asset basic data; wherein the asset basic data includes asset name, specification model and purchase date; Taking an image of the target fixed asset by an image acquisition module provided on the inventory counting device to obtain asset image data; Performing status recognition on the asset image data to obtain asset status information; Through the data verification mechanism, the asset basic data and the asset status information are verified to obtain the asset verification result; Determine whether there is any information inconsistency in the asset verification result. If so, record the inconsistent asset status information on the target fixed asset through the exception handling module on the inventory counting device, and replace the label on the target fixed asset accordingly based on the inconsistent asset status information; If it does not exist, the asset verification results are sorted out based on an information aggregation algorithm to obtain the asset inventory data; wherein the asset inventory data includes an asset list and a status report.

8. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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