Environment context processing and model optimization method and system based on edge computing

Abstract

The invention relates to the technical field of data processing, in particular to an environment context processing and model optimization method and system based on edge computing, initial data and historical data in each time period are stored in a target memory together, so that the continuity and integrity of the data in the time dimension are ensured, and the data processing efficiency is improved. The association between the current data and the past environment state is mined by fusing the data of different sources and time, so that the states of the target physical place and each target object at the end of each time period are more comprehensively grasped, and the target memory stores continuous historical data along with the passage of time; the dynamic change of the target physical place and the target object can be known by combining the context data, so that the edge world model can better adapt to the characteristics and change of the target physical place, the adaptation degree between the edge world model and the target physical place is improved, and the completion efficiency and the completion effect of the target application task are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to an environment context processing and model optimization method and system based on edge computing. BACKGROUND

[0002] With the rapid development of Internet of Things and artificial intelligence technology, environment context processing and model application are widely used in intelligent buildings, industrial manufacturing, intelligent transportation and many other fields. For AI application and multi-modal model, context environment information is the key to improve model effect. In the prior art, environment context data extraction and model optimization usually rely on cloud processing and common sense modeling, which is difficult to efficiently and timely perceive the local environment context and understand the unique rules of the local physical place, and difficult to efficiently integrate the independent data of each target object and the overall context data of the target physical place, resulting in poor quality of the obtained context data, making the trained model insufficiently adaptable to the dynamic changes of the target physical place, and unable to accurately reflect the real state and change trend of the physical place, which makes it difficult to provide reliable decision support in industrial production environment monitoring, intelligent traffic flow prediction and many other practical application scenarios.

[0003] Therefore, how to improve the adaptability between the edge world model and the target physical place, so as to improve the completion efficiency and completion effect of the target application task has become a problem to be solved. SUMMARY

[0004] In view of the above technical problems, the technical scheme adopted by the present application is an environment context processing and model optimization method based on edge computing, comprising the following steps: S1, generating the ID corresponding to each target object in the target physical place and the target physical place, and allocating the corresponding target storage location for each ID in the target storage.

[0005] S2, storing the initial data corresponding to each target object in the target physical place in the current time period into the corresponding target storage location, wherein the target storage also stores the first historical context data corresponding to the target physical place and the second historical context data corresponding to each target object before the start of the current time period, and the current time period refers to a continuous time period with the current time as the end point and the time length T.

[0006] S3, according to the initial data corresponding to each target object in the current time period, the first historical context data corresponding to the target physical place and the second historical context data corresponding to each target object before the start of the current time period, obtaining the first reference context data corresponding to the target physical place and the second reference context data corresponding to each target object at the end of the current time period.

[0007] S4, the first reference context data corresponding to the target physical site and the second reference context data corresponding to each target object at the end of the current time period are stored as the first historical context data corresponding to the target physical site and the second historical context data corresponding to each target object before the start of the next time period, and are stored in the corresponding target storage location.

[0008] S5, according to the first historical context data and the second historical context data stored in the target storage before the preset model training time, the preset world model of the cloud is model distilled at the preset model training time, and an edge world model corresponding to the target physical site is obtained.

[0009] S6, according to the first historical context data and the second historical context data stored in the target storage and the edge world model, the target application interface is called to execute the target application task corresponding to the target application interface.

[0010] The application also provides an environment context processing and model optimization system based on edge computing, comprising: An ID generation module is configured to generate IDs corresponding to the target physical site and each target object in the target physical site, and allocate a corresponding target storage location for each ID in the target storage.

[0011] A first data storage module is configured to store initial data corresponding to each target object in the target physical site in the current time period in the corresponding target storage location, wherein the target storage also stores first historical context data corresponding to the target physical site and second historical context data corresponding to each target object before the start of the current time period, and the current time period refers to a continuous time period with the current time as the end point and a time length of T.

[0012] A data processing module is configured to obtain first reference context data corresponding to the target physical site and second reference context data corresponding to each target object at the end of the current time period according to the initial data corresponding to each target object in the current time period, the first historical context data corresponding to the target physical site and the second historical context data corresponding to each target object before the start of the current time period.

[0013] A second data storage module is configured to store the first reference context data corresponding to the target physical site and the second reference context data corresponding to each target object at the end of the current time period as the first historical context data corresponding to the target physical site and the second historical context data corresponding to each target object before the start of the next time period, and store them in the corresponding target storage location.

[0014] The model distillation module is configured to perform model distillation on the preset world model of the cloud at a preset model training time according to the first historical context data and the second historical context data stored in the target memory before the preset model training time, and obtain the edge world model corresponding to the target physical site.

[0015] The task execution module is configured to invoke the target application interface to execute a target application task corresponding to the target application interface according to the first historical context data and the second historical context data stored in the target memory and the edge world model.

[0016] The present application has at least the following advantages: by assigning a unique ID to each target physical site and target object and assigning a corresponding storage location, the data can be organized in order in the target memory, improving the efficiency and accuracy of subsequent data storage, query and update, by storing the initial data in the current time period together with the second historical context data in the target memory, the continuity and integrity of the data in the time dimension are ensured, which helps to establish the association between the current data and the past environment state, and to fuse data from different sources and different times, which can fully exploit the advantages of various types of data, and help to mine potential rules and trends in the data, so as to more comprehensively and accurately grasp the state of the target physical site and each target object at the end of the current time period, further enabling the target memory to store continuous historical data over time, facilitating understanding of the dynamic changes of the target physical site and the target object in combination with the context data, improving the characterization degree of the target physical site, enabling subsequent model training to be based on more comprehensive and longer-term data samples, enabling the edge world model trained based on the historical data of the target physical site to better adapt to the characteristics and changes of the target physical site, thereby improving the adaptability between the edge world model and the target physical site, and further improving the completion efficiency and completion effect of the target application task. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0018] Figure 1 A flowchart of an environment context processing and model optimization method based on edge computing provided for the first embodiment of the present application; Figure 2 A flowchart of obtaining initial data in an environment context processing and model optimization method based on edge computing provided for the first embodiment of the present application; Figure 3 A flowchart for acquiring initial collection data in the environment context processing and model optimization method based on edge computing provided for the first embodiment of the present application is provided. Figure 4 A flowchart for acquiring second reference context data in the environment context processing and model optimization method based on edge computing provided for the first embodiment of the present application is provided. Figure 5 A flowchart for acquiring first reference context data in the environment context processing and model optimization method based on edge computing provided for the first embodiment of the present application is provided. Figure 6 A flowchart for acquiring edge world model in the environment context processing and model optimization method based on edge computing provided for the first embodiment of the present application is provided. Figure 7 A flowchart for acquiring first target context data, second target context data and target scene features in the environment context processing and model optimization method based on edge computing provided for the first embodiment of the present application is provided. Figure 8 A flowchart for executing prediction control task in the environment context processing and model optimization method based on edge computing provided for the first embodiment of the present application is provided. Figure 9 A flowchart for executing target query task in the environment context processing and model optimization method based on edge computing provided for the first embodiment of the present application is provided. Figure 10 A flowchart for executing natural language interaction task in the environment context processing and model optimization method based on edge computing provided for the first embodiment of the present application is provided. Figure 11 A module schematic diagram of the environment context processing and model optimization system based on edge computing provided for the second embodiment of the present application is provided. DETAILED DESCRIPTION

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

[0020] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and in the above-described drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It can be understood that the above-described terms for distinguishing similar objects can be interchanged under appropriate circumstances, so that the present application can also be implemented in other embodiments in addition to the above-described illustrated embodiments or described embodiments. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or server including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0021] Embodiment one The embodiment one provides an edge computing-based environment context processing and model optimization method, which includes the following steps, as shown in Figure 1 S1, generating the ID corresponding to the target physical site and each target object in the target physical site, and allocating the corresponding target storage location for each ID in the target storage.

[0022] Among them, the target physical site refers to a specific physical space or system that needs to be processed and analyzed, such as a smart factory, a smart building or a specific natural ecological area, which is the object range of data research and processing.

[0023] The target object refers to an individual or component with independent characteristics and behavior in the target physical site, which can be a sensor, a device, a person, an object, a robot, etc. For example, in a smart factory, the target object can be a robot on the production line, various sensors, transport vehicles, etc.

[0024] The target storage refers to a storage device or system for storing the data corresponding to the target physical site and each target object, which can be a database, a hard disk array, etc., which provides a centralized management and storage space for data, facilitating subsequent data query, reading and processing.

[0025] ​In order to ensure independent data storage and tracking of the target physical site and each physical object, a unique corresponding ID is generated for the target physical site and each target object, and a corresponding target storage location is allocated for each ID in the target storage, that is, a special area is demarcated for each ID in the target storage to store the relevant data of the target physical site or target object corresponding to each ID. In addition, the target storage usually adopts a specific data structure to manage the storage location, such as a hash table, an index tree, etc. Through the specific data structure, the corresponding storage location can be quickly found according to the ID, and the data storage, query and update can be more efficient and accurate.

[0026] In the embodiment, the ID can be a string of numbers, letters or a combination of numbers and letters, and can be generated by a Universally Unique Identifier (UUID) or Re-Identification (ReID) method.

[0027] By allocating a unique ID to each target physical site and target object and allocating a corresponding storage location, the data can be organized in order in the target storage, and the efficiency and accuracy of data storage, query and update can be improved.

[0028] S2, store the initial data of each target object in the target physical site in the corresponding target storage location within the current time period, wherein the target storage also stores the first historical context data corresponding to the target physical site before the start of the current time period and the second historical context data corresponding to each target object, and the current time period refers to a continuous time period with the current time as the end point and the time length T.

[0029] The current time period is a continuous time period with the current time as the end point and the time length T, and the specific value of T is determined according to the actual application scenario and demand, representing a specific time window for data collection and processing, for analyzing the changes and status of the physical site within a period of time. Through the change of the current time period, the target physical site and the target object can be regularly collected, processed, stored, updated, summarized and applied.

[0030] The initial data refers to the data collected or generated by each target object within the current time period, which is the basis for subsequent processing and analysis, and contains the original information of the target object within the time period, such as temperature, humidity, pressure, image data collected by the camera, etc.

[0031] The first historical context data refers to a series of background data and environmental information related to the target physical site before the start of the current time period, which describes the overall state and related conditions of the physical site in the past, such as the trend of environmental temperature change in the past period, the historical record of equipment operation state, etc., which helps to understand the evolution process of the current physical site.

[0032] The second historical context data corresponding to each target object refers to the data records generated by the target object in the past period before the start of the current time period, reflecting the historical behavior and characteristic changes of the target object, such as the numerical sequence obtained by a certain sensor through multiple measurements in the past. It can be used to analyze the performance changes and trends of the target object. The target memory has saved the first historical context data corresponding to the target physical site and the second historical context data corresponding to each target object before the start of the current time period.

[0033] As mentioned above, the target object data in the physical site changes over time, and by storing the initial data in the current time period together with the historical data in the target memory, the continuity and integrity of the data in the time dimension are ensured, and it is helpful to establish the association between the current data and the past environmental state, facilitate the mining of the association and change trend between the target objects and the physical site, at the same time, the continuous time series data can more accurately reflect the dynamic change process of the target object and the physical site, providing comprehensive data support for subsequent analysis and processing.

[0034] In a specific embodiment, a plurality of data acquisition devices are arranged in the target physical site, each data acquisition device corresponding to a plurality of target objects in the target physical site, such as Figure 2 As shown in FIG. 2, S2 includes the following steps: S21, obtaining the initial acquisition data of each target object corresponding to each data acquisition device in the current time period.

[0035] S22, performing vectorization processing on the initial acquisition data of each target object in the target physical site corresponding to the current time period, to obtain the initial vector features of each target object corresponding to the current time period.

[0036] S23, performing semantic summarization on the initial acquisition data of each target object in the target physical site corresponding to the current time period, to obtain the initial semantic description of each target object corresponding to the current time period.

[0037] S24, performing rule summarization on the initial acquisition data, initial vector features and initial semantic description of each target object corresponding to the current time period, to obtain the local rule data of each target object corresponding to the current time period.

[0038] S25, taking the initial collection data, initial vector features, initial semantic description, local regularity data and the current time period of each target object as the initial data of each target object in the current time period.

[0039] Among them, the data collection device can be various sensors such as temperature sensors, humidity sensors, light sensors, pressure sensors, video collection devices such as cameras, audio collection devices such as microphones, and detection devices such as radars. The data collection device is deployed in the target physical site for real-time monitoring and collection of relevant data of the target object corresponding thereto. In the current time period, the data collection device continuously collects data according to a certain sampling frequency, which is used to reflect the state and change of the target object in the time period.

[0040] In machine learning and data analysis, vectors are a data representation method convenient for computer processing and analysis. For the initial collection data of each target object in the current time period, the initial collection data is converted into initial vector features through a specific vector conversion method, which facilitates the computer to process data in a unified and standardized form, thereby improving the efficiency and accuracy of data processing.

[0041] The embodiment converts the initial collection data of each modality through a text embedding model, an image embedding model, and a multi-modal embedding model. The text embedding model is used to convert the initial collection data of the text modality into a low-dimensional vector space representation, extract the semantic and syntactic features of the initial collection data, and facilitate the computer to process and analyze the text. The image embedding model is used to convert the initial collection data of the image modality into a low-dimensional vector representation, extract image features, and facilitate the computer to process and analyze images. The multi-modal embedding model is used to fuse the embedding models of text, image, audio and other multi-modal data, capture the correlation and complementary information between different modal data, and facilitate the computer to better understand and process multi-modal data.

[0042] Semantic summarization involves natural language processing and semantic understanding. Specifically, the initial collection data is converted into natural language text description through semantic analysis and conversion of the collected data, which facilitates the combination of data and domain knowledge and provides more rich environmental data. Semantic summarization can be realized by using a large language model to generate appropriate text description according to the characteristics of the data. For example, in the intelligent factory scene, the temperature sensor collects temperature data corresponding to the warehouse shelf as 22℃, and the large language model can generate the corresponding initial semantic description as "The temperature of the shelf is kept at 22℃, which is conducive to the storage of goods and avoids damage to goods due to excessive or low temperature."

[0043] The local rule refers to rule information with specificity and locality mined by analyzing and processing the initial collection data, the initial vector feature and the initial semantic description of each target object in the current time period, and used to reflect the characteristics and behavior model of the corresponding target object. The local rule is comprehensively analyzed and summarized from multiple dimensions to fully depict the local rule of the target object.

[0044] The above, the initial data is obtained by comprehensively analyzing the initial collection data, the initial vector feature, the initial semantic description, the local rule data and the current time period, so that the initial data has a more comprehensive and multi-dimensional representation, and the state of the physical place and the target object can be more comprehensively understood, thereby providing a rich data basis for deep mining and comprehensive utilization of data.

[0045] In a specific embodiment, as shown in Figure 3 S21 includes the following steps: S211, according to the business scene type corresponding to the target physical place, obtaining the preset data extraction rule corresponding to the target physical place.

[0046] S212, according to the preset data extraction rule, filtering the original data collected by each data collection device in the current time period, and obtaining the initial collection data of each target object corresponding to each data collection device in the current time period.

[0047] Among them, people, things and robots under different business scene types have different business characteristics and needs, pay attention to different dimensions of business data, and the corresponding data extraction rules are different.

[0048] Therefore, the implementer can preform the preset data extraction rule matched with each business scene type according to the monitoring target, data characteristics and analysis requirements of each business scene type, and store it in the rule library or configuration file of the target storage, so as to obtain the preset data extraction rule corresponding to the target physical place from the target storage according to the business scene type corresponding to the target physical place, and use it to filter the original data collected by each data collection device in the current time period, so as to ensure that the extracted data matches the characteristics and needs of the target physical place, thereby improving the pertinence and effectiveness of the data.

[0049] For example, in a shopping mall retail scenario, consumers are more concerned with product prices, promotions, and store locations, allowing them to efficiently select desired products. Merchants are more concerned with product sales, inventory, and customer purchasing preferences, allowing them to optimize product displays and marketing strategies. Mall managers are more concerned with customer flow, the length of time consumers spend in different areas, and store performance, allowing them to assess mall operational efficiency and plan commercial layouts. Accordingly, the data extraction rules are as follows: For consumers, data on their movements within the mall, store visits, and product browsing is extracted. For merchants, data on product sales records, inventory changes, customer purchase times and frequency is extracted. For mall managers, data on customer flow at each entrance and exit, real-time foot traffic by floor and store, and average customer dwell time is extracted.

[0050] In the hospital inpatient setting, patients are more concerned about changes in their symptoms, treatment plans, and recovery progress. Medical staff are more concerned about patients' vital signs, test results, and treatment responses in order to adjust treatment measures. Hospital management is more concerned about bed usage, medical resource allocation, department operational efficiency, and optimizing management decisions. Correspondingly, the data extraction rules are as follows: for patients, extract their symptom descriptions, rehabilitation self-assessment data, feedback on treatment, and other data. For medical staff, extract patients' vital signs data such as heart rate, blood pressure, and body temperature, as well as various examination reports, medication records, and other data. For hospital management, extract data such as bed occupancy rates, medical equipment usage time, and drug consumption in each department.

[0051] As described above, preset data extraction rules are obtained according to the business scenario type, and initial collected data are filtered out from the original collected data according to the preset data extraction rules to ensure that the extracted initial collected data is more consistent with the type and needs of the target physical place, avoiding the extraction of irrelevant data, and when the business scenario type changes or the needs are adjusted, the extracted initial collected data can be optimized by modifying and updating the corresponding data extraction rules, without the need to modify the collection processing of each data collection device separately, thereby improving the efficiency and accuracy of subsequent data analysis and processing.

[0052] S3, based on the initial data corresponding to each target object in the current time period, the first historical context data corresponding to the target physical place before the start of the current time period and the second historical context data corresponding to each target object, obtain the first reference context data corresponding to the target physical place at the end of the current time period and the second reference context data corresponding to each target object.

[0053] Among them, the initial data of each target object in the current time period reflects the real-time status of the target object in this time period, the first historical context data reflects the overall condition of the target physical place in the past, and the second historical context data of each target object summarizes and records the past status of the target object itself.

[0054] By integrating the initial data of each target object in the current time period, the first historical context data and the second historical context data of each target object, the first reference context data of the target physical site at the end of the current time period and the second reference context data of each target object are obtained. Specifically, the first reference context data is a description of the overall state of the target physical site at the end of the current time period after considering the current data and the second historical context data, and the second reference context data is the state data of each target object at the end of the current time period, which comprehensively considers the current performance and historical development of the target object.

[0055] By fusing data from different sources and at different times, the advantages of various types of data can be fully utilized, which helps to mine potential rules and trends in the data, so as to more comprehensively and accurately grasp the state of the target physical site and each target object at the end of the current time period.

[0056] In a specific embodiment, as shown in Figure 4 S3 includes the following steps: S31, for any target object in the target physical site, feature extraction is performed on the initial data corresponding to the current target object in the current time period to obtain the first initial feature corresponding to the current target object in the current time period.

[0057] S32, feature extraction is performed on the second historical context data corresponding to the current target object before the start of the current time period to obtain the first historical feature corresponding to the current target object before the start of the current time period.

[0058] S33, feature fusion is performed on the first initial feature corresponding to the current target object in the current time period and the first historical feature corresponding to the current target object before the start of the current time period to obtain the second reference context data corresponding to the current target object at the end of the current time period.

[0059] The initial data may contain a large amount of redundant information, and feature extraction can convert high-dimensional original data into low-dimensional feature vectors, reduce the amount of data while retaining key information, and improve the efficiency of subsequent processing.

[0060] The first initial feature reflects the current state of the target object, and the first historical feature reflects the long-term trend of the target object. By fusing the two, the complementarity of the data can be fully utilized, and the current state and historical development of the target object can be considered comprehensively to obtain the second reference context data corresponding to each target object, which is used to represent more comprehensive and accurate context information of each target object in the front and rear time periods.

[0061] In a specific embodiment, as shown in Figure 5As shown, S3 includes the following steps: S34, feature extraction is performed on the initial data of all target objects in the target physical site corresponding to the current time period, to obtain the second initial feature corresponding to the target physical site in the current time period.

[0062] S35, feature fusion is performed on the second initial feature corresponding to the target physical site in the current time period and the first historical context data corresponding to the beginning of the current time period, to obtain the first reference context data corresponding to the target physical site at the end of the current time period.

[0063] Among them, the initial data of all target objects in the environment in the current time period is taken as a whole for feature extraction, so as to extract the key features that can represent the state of the entire target physical site in the current time period, i.e. the second initial feature.

[0064] The first historical context data contains the past state, trend and background information of the target physical site, and through the operation of feature fusion, the state features of the environment in the current time period are combined with the past historical information, so as to generate a more comprehensive and more capable of reflecting the overall state of the target physical site at the end of the current time period. The first reference context data.

[0065] S4, the first reference context data corresponding to the target physical site at the end of the current time period and the second reference context data corresponding to each target object are stored as the first historical context data corresponding to the target physical site and the second historical context data corresponding to each target object before the start of the next time period, and are stored in the corresponding target storage location.

[0066] Among them, the state of the target physical site and the target objects therein changes continuously over time, and by converting the first reference context data and the second reference context data obtained at the end of the current time period into the second historical context data before the start of the next time period, and storing them in the corresponding target storage location of the target storage, a historical basis is provided for data processing in the subsequent time period. The process of cyclic updating and continuous accumulation of data can be formed, so as to better track and analyze the dynamic changes of the target physical site and each target object over time, to discover the trend, periodicity and abnormal situation of the target physical site based on the context data changing over time. Dynamic information such as.

[0067] S5, according to the first historical context data and the second historical context data stored by the target storage before the preset model training time, model distillation is performed on the preset world model of the cloud at the preset model training time, to obtain the edge world model corresponding to the target physical site.

[0068] The preset model training time is a time node preset by an implementer and is used to determine the data range for model training. Correspondingly, the first historical context data and the second historical context data stored in the target storage before this time will be used to train the model.

[0069] The cloud is a remote server cluster or computing platform based on cloud computing technology, which has strong computing and storage capabilities and can process large-scale data and complex computing tasks. In the model training process, the cloud is used to provide a preset world model and related computing resources.

[0070] The preset world model is a pre-constructed general model framework that has been pre-trained on a large amount of general data and has learned some general features and patterns. The preset world model can be used as a base model, and based on the first historical context data and the second historical context data stored in the target storage before the preset model training time, the preset world model of the cloud can be post-trained. By combining the general features and patterns learned by the preset world model with the characteristic historical data of the target physical site, the preset world model learns the characteristics, trends and laws of the target physical site, and teaches the simpler and lighter edge world model in a transferable manner, and finally obtains an edge world model that fits the target physical site.

[0071] Correspondingly, the number of parameters of the preset world model is greater than the number of parameters of the edge world model corresponding to the edge world model, and the edge world model can more accurately reflect the characteristics and change rules of the target physical site, and can provide strong support for subsequent environmental monitoring, prediction and decision-making tasks. The edge world model is more suitable for edge devices such as smartphones, Internet of Things sensors and smart cameras, and has advantages in hardware purchase cost and energy consumption. When deploying artificial intelligence applications on a large scale, it can greatly reduce the cost of server hardware and operation.

[0072] The present embodiment migrates the knowledge of the complex and large preset world model to the simpler and lighter edge world model through model distillation. Those skilled in the art know that the specific model distillation process in the prior art falls within the scope of the present application, and will not be described here.

[0073] The target memory stores continuous historical data over time, which facilitates understanding the dynamic changes of the target physical site and the target object in combination with the context data, improves the characterization degree of the target physical site, enables subsequent model distillation to be based on more comprehensive and longer-term data samples, and enables the edge world model trained based on the historical data of the target physical site to better adapt to the characteristics and changes of the target physical site compared to the general preset world model, thereby improving the adaptation degree between the edge world model and the target physical site, making the prediction and analysis results in the target physical site more accurate, and being able to provide more reliable basis for decision-making.

[0074] In a specific embodiment, as shown in Figure 6 S5 includes the following steps: S51, according to the business scene type corresponding to the target physical site, obtaining the basic scene feature corresponding to the target physical site.

[0075] S52, according to the basic scene feature, reducing the dimension of the first historical context data and the second historical context data stored by the target memory before the preset model training time, obtaining the first target context data, the second target context data and the target scene feature corresponding to the target physical site.

[0076] S53, taking the first target context data, the second target context data and the target scene feature corresponding to the target physical site as training data, performing model distillation on the preset world model of the cloud at the preset model training time, obtaining the edge world model corresponding to the target physical site, wherein the parameter quantity of the preset world model is greater than the parameter quantity corresponding to the edge world model.

[0077] The basic scene feature is a key feature extracted for a specific business scene type, which can reflect the basic scene and main attributes of the environment. For example, in an intelligent factory, the basic scene feature may include the device operating parameter range, the key nodes of the production process, etc. In an intelligent home, it may be the normal range of indoor temperature and humidity, the common working mode of household appliances, etc.

[0078] Different business scene types have different characteristics and operation rules. By classifying and identifying the business scene types, the corresponding basic scene features are extracted, which can provide targeted guidance for subsequent data processing and model training, make the processing process more consistent with the actual situation of the target physical site, and improve the data utilization efficiency and the accuracy of the model.

[0079] The first historical context data and the second historical context data can contain a large amount of redundant information and noise, and high-dimensional data not only increases the computing cost, but also can affect the training effect of the model. By dimension reduction, the key information in the data can be highlighted, and the data is more concise and easy to process. At the same time, combined with the basic scene features for dimension reduction, important information related to the target physical place can be ensured to be retained, and the processed data is more in line with the needs of actual application.

[0080] The first target context data, the second target context data and the target scene feature obtained after dimension reduction are integrated as training data and input into the preset world model of the cloud, and by model distillation on the preset world model, an edge world model that can accurately reflect the characteristics of the target physical place is finally obtained.

[0081] In a specific embodiment, as shown in Figure 7 S52 includes the following steps: S521, for any preset index corresponding feature value in the first historical context data and the second historical context data stored by the target memory before the preset model training time, calculate the feature difference value between the current preset index corresponding feature value and the basic scene feature.

[0082] S522, statistics of the feature difference values corresponding to all preset indexes in the first historical context data and the second historical context data stored by the target memory before the preset model training time are obtained.

[0083] S523, according to the feature difference distribution diagram, all preset indexes are screened to obtain a plurality of target indexes.

[0084] S524, the target memory stores the first historical context data, the second historical context data and the basic scene feature before the preset model training time, and determines the target index corresponding data as the first target context data, the second target context data and the target scene feature corresponding to the target physical place.

[0085] Among them, for each preset index, the difference value between the corresponding feature value and the basic scene feature is calculated. For example, in the intelligent factory scene, the preset indexes can be device temperature, production efficiency, etc., and the normal temperature range of the device in the basic scene feature is an interval. The minimum difference value between the current device temperature feature value and the upper and lower limits of the normal temperature range is the corresponding feature difference value.

[0086] The feature difference value can find the part deviating from the typical feature in the first historical context data and the second historical context data, and the feature difference value often contains special information of the target physical site, such as abnormal situation, change trend, etc., and the feature difference distribution diagram can intuitively show the distribution of the difference, providing a basis for subsequent index screening.

[0087] According to the result of the feature difference distribution diagram, all preset indexes are screened. Specifically, the embodiment selects the index with a larger feature difference value or a special distribution rule as a target index, because the selected target index can better reflect the uniqueness and change of the target physical site, and is more valuable for subsequent model training and analysis. For example, in the intelligent agricultural scene, if the feature difference value of the soil humidity index is large, it means that the index changes significantly at different times or in different environments, and is selected as a target index.

[0088] The first historical context data, the second historical context data, and the data corresponding to the target index in the basic scene feature are extracted to determine the first target context data, the second target context data, and the target scene feature, respectively, realizing dimension reduction of the data, removing some unimportant information for describing the characteristics of the target physical site, making the data more refined and targeted, and thus improving the adaptability between the edge world model and the target physical site.

[0089] S6, according to the first historical context data and the second historical context data stored in the target memory and the edge world model, calling the target application interface to execute the target application task corresponding to the target application interface.

[0090] The target application interface is a set of pre-defined functions or methods for interacting with the target application, providing a standardized way for external systems to request the target application to perform specific tasks.

[0091] According to the information provided by the first historical context data, the second historical context data and the edge world model in the target memory, the specific task to be executed is determined, and the corresponding operation is triggered by calling the target application interface to execute the corresponding target application task to achieve a specific business goal.

[0092] In a specific embodiment, the target application task corresponding to the target application interface includes a behavior prediction control task, and the edge world model includes an edge prediction model, as shown in Figure 8 S6 includes the following steps: S611, receiving a behavior prediction control request through the target application interface, wherein the behavior prediction control request includes a target control object and a prediction control requirement.

[0093] S612, acquire the first target data corresponding to the behavior prediction and control request from the first historical context data and the second historical context data stored in the target storage according to the behavior prediction and control request.

[0094] S613, input the first target data into the edge prediction model to acquire the behavior prediction data corresponding to the behavior prediction and control request, wherein the behavior prediction data is used to control the behavior of the target control object.

[0095] The behavior prediction and control request is received through the target application interface, so as to explicitly indicate that the operation related to the behavior prediction and control is needed. The target control object is specified in the behavior prediction and control request, i.e., the object that needs to be specifically predicted and controlled, such as a robot, a person or an intelligent household appliance, etc. Meanwhile, the specific requirements of the behavior prediction and control of the target control object are indicated in the behavior prediction and control request, for example, predicting and controlling the position, moving track, running state, etc. of the target control object at a certain time in the future.

[0096] The first target data corresponding to the behavior prediction and control request is filtered from the large amount of first historical context data and second historical context data stored in the target storage. The edge prediction model analyzes the first target data according to the learned rules and patterns, thereby predicting the future behavior of the target control object under the condition of meeting the prediction and control requirements, and outputting the behavior prediction data for controlling the future behavior of the target control object. For example, for a robot, the edge prediction model can predict the moving speed, position change, etc. of the robot in the next period of time according to the past motion data and the environmental information of the target physical space, so as to control the moving speed and track of the robot.

[0097] Based on the rich first historical context data and second historical context data in the target storage, the potential rules and influencing factors behind the behavior of the target control object are mined through the edge prediction model, so that the prediction and control process of the target control object is more scientific and reasonable.

[0098] In a specific embodiment, the target application task corresponding to the target application interface further includes a target query task, as shown in Figure 9 As shown in FIG. 6, S6 further includes the following steps: S621, receive a target query request through the target application interface, wherein the target query request includes a target query object, a target query time and a target query condition.

[0099] S622, index the first historical context data and the second historical context data stored in the target storage.

[0100] S623, obtaining target query data corresponding to the target query request from the first historical context data and the second historical context data stored in the target memory according to the index.

[0101] The target query object indicates a specific object that the user wants to query, for example, in a company, it can be a certain employee or a certain product. The target query time limits the time range of the query data, for example, the user may want to query the work record of a certain employee in the past month, or the sales data of a certain product in a certain quarter, etc. The target query condition further refines the query requirements, for example, when querying the work record of an employee, the condition may be that the working time exceeds a certain standard, the performance score reaches a certain level, etc. When querying product sales data, the condition may be that the sales amount is within a certain interval, the sales area meets a certain range, etc.

[0102] Since the amount of the first historical context data and the second historical context data stored in the target memory can be very large, in order to improve the efficiency of data query, it is necessary to index these data to record the storage location of the data in the memory and some key feature information of the data. Among them, those skilled in the art know that the index establishment method in the prior art falls within the protection scope of the present application. For example, for time series data, an index can be established according to time order, so as to quickly locate the storage location of the related data when querying the data in a certain time range. For classification data, an index can be established according to categories, so as to query data of a certain category.

[0103] After the index is established, according to the target query object, the target query time and the target query condition in the target query request, the index is used to quickly locate and filter the data meeting the requirements, which can avoid traversing the entire data set, and greatly improves the speed of data retrieval.

[0104] As described above, by establishing an index, the entire data set can be avoided to be traversed, and an efficient and accurate data query function is realized, which can quickly respond to the query requirements of the user and provide accurate query results.

[0105] In a specific embodiment, the target application interface corresponds to a target application task, and the edge world model further includes an edge language interaction model, as shown in Figure 10 S6 further includes the following steps: S631, receiving a natural language text input by a target user through the target application interface.

[0106] S632, obtaining a target problem corresponding to the target user according to the natural language text.

[0107] S633, obtaining, from the target memory, second target data corresponding to the target request from the first historical context data and the second historical context data stored in the target memory according to the target question.

[0108] S634, inputting the second target data into the edge language interaction model to obtain a target reply corresponding to the target question, wherein the target reply corresponds to a natural language form.

[0109] The natural language text can be text input by a user through a keyboard, or text information converted after voice recognition.

[0110] The received natural language text is processed and analyzed through technologies such as word segmentation, part-of-speech tagging, syntax analysis, and semantic understanding, the core question that the target user really wants to ask or express, i.e., the target question, is extracted from the natural language text, and the data related to the target question, i.e., the second target data, is filtered from the large amount of first historical context data and second historical context data stored in the target memory. The second target data can include solutions to similar questions as the target question in the past, user feedback information, product-related instructions, etc. For example, if the target question is about the solution to product failure, relevant information such as the processing records of other users encountering the same or similar failure in the past, the troubleshooting manual of the product, etc. can be searched from the historical data. By obtaining the second target data, the user's question can be answered using past experience and knowledge.

[0111] The edge language interaction model combines the language patterns and knowledge learned by itself to understand and analyze the input second target data, and generates a target reply in the natural language form corresponding to the target question. For example, the edge language interaction model can generate a reply similar to "You can first check whether the power connection of the product is normal, if the power supply is not a problem, you can try to restart the product. If the problem still exists, please contact our after-sales service personnel, the contact information is XXX" according to the previous failure processing records, and provide it to the target user to complete a natural language interaction task.

[0112] The above, by processing the natural language text input by the user, accurately understanding the user's question, and generating a suitable reply by using historical data and an edge language interaction model, the function of natural language interaction is realized, and the communication efficiency and experience between the user and the system are improved.

[0113] As described above, by assigning a unique ID and a corresponding storage location to each target physical location and target object, data can be organized in an orderly manner in the target memory, improving the efficiency and accuracy of subsequent data storage, querying, and updating. By storing the initial data within the current time period together with the historical data in the target memory, the continuity and integrity of the data in the time dimension are guaranteed, which helps to establish an association between current data and past environmental states. The fusion of data from different sources and different times can fully leverage the advantages of various types of data and help to explore potential patterns and trends in the data, thereby more comprehensively and accurately grasping the state of the target physical location and each target object at the end of the current time period. Furthermore, the target memory stores continuous historical data over time, facilitating the understanding of the dynamic changes of the target physical location and target objects in combination with contextual data, improving the representation of the target physical location, and enabling subsequent model distillation to be based on more comprehensive and long-term data samples. The edge world model trained based on the historical data of the target physical location can better adapt to the characteristics and changes of the target physical location compared to the general preset world model, thereby improving the adaptability between the edge world model and the target physical location, and thus improving the completion efficiency and effectiveness of the target application task.

[0114] Example 2 This embodiment 2 provides an environment context processing and model optimization system based on edge computing, such as Figure 11 As shown, the edge computing-based environmental context processing and model optimization system includes: The ID generation module 111 is used to generate an ID corresponding to the target physical place and each target object in the target physical place, and allocate a corresponding target storage location for each ID in the target memory.

[0115] The first data storage module 112 is used to store the initial data corresponding to each target object in the target physical place within the current time period in the corresponding target storage location, wherein the target memory also stores the first historical context data corresponding to the target physical place before the start of the current time period and the second historical context data corresponding to each target object. The current time period refers to a continuous time period with the current time as the end point and a time length of T.

[0116] The data processing module 113 is used to obtain the first reference context data corresponding to the target physical place at the end of the current time period and the second reference context data corresponding to each target object based on the initial data corresponding to each target object in the current time period, the first historical context data corresponding to the target physical place before the start of the current time period, and the second historical context data corresponding to each target object.

[0117] The second data storage module 114 is configured to store the first reference context data corresponding to the target physical site and the second reference context data corresponding to each target object at the end of the current time period as the first historical context data corresponding to the target physical site and the second historical context data corresponding to each target object before the start of the next time period, and store the first historical context data and the second historical context data in the corresponding target storage location.

[0118] The model distillation module 115 is configured to perform model distillation on the preset world model of the cloud at a preset model training time according to the first historical context data and the second historical context data stored in the target storage before the preset model training time, and obtain the edge world model corresponding to the target physical site.

[0119] The task execution module 116 is configured to invoke the target application interface to execute the target application task corresponding to the target application interface according to the first historical context data and the second historical context data stored in the target storage and the edge world model.

[0120] In an embodiment, the target physical site is provided with a plurality of data collection devices, each data collection device corresponding to a plurality of target objects in the target physical site, and the first data storage module 112 includes: The data collection sub-module is configured to obtain initial collection data of each target object corresponding to each data collection device in the target physical site within the current time period.

[0121] The vector conversion sub-module is configured to perform vectorization processing on the initial collection data of each target object in the target physical site within the current time period, and obtain initial vector features of each target object within the current time period.

[0122] The semantic summary sub-module is configured to perform semantic summarization on the initial collection data of each target object in the target physical site within the current time period, and obtain initial semantic descriptions of each target object within the current time period.

[0123] The rule summary sub-module is configured to perform rule summarization on the initial collection data, the initial vector features, and the initial semantic descriptions of each target object within the current time period, and obtain local rule data of each target object within the current time period.

[0124] The initial data acquisition sub-module is configured to store the initial collection data, the initial vector features, the initial semantic descriptions, the local rule data, and the current time period of each target object within the current time period as initial data of each target object within the current time period.

[0125] In an embodiment, the data collection sub-module includes: The extraction rule acquisition unit is configured to acquire a preset data extraction rule corresponding to the target physical site according to a business scenario type corresponding to the target physical site.

[0126] The data screening unit is configured to screen original data collected by each data collection device in a current time period according to the preset data extraction rule, and acquire initial collection data of each target object corresponding to each data collection device in the current time period.

[0127] In an embodiment, the data processing module 113 includes: The first initial feature acquisition submodule is configured to perform feature extraction on initial data corresponding to a current target object in a current time period for any target object in the target physical site, and acquire a first initial feature corresponding to the current target object in the current time period.

[0128] The first historical feature acquisition submodule is configured to perform feature extraction on second historical context data corresponding to the current target object before the start of the current time period, and acquire a first historical feature corresponding to the current target object before the start of the current time period.

[0129] The first feature fusion submodule is configured to perform feature fusion on the first initial feature corresponding to the current target object in the current time period and the first historical feature corresponding to the current target object before the start of the current time period, and acquire second reference context data corresponding to the current target object at the end of the current time period.

[0130] In an embodiment, the data processing module 113 includes: The second initial feature acquisition submodule is configured to perform feature extraction on initial data corresponding to all target objects in the target physical site in the current time period, and acquire a second initial feature corresponding to the target physical site in the current time period.

[0131] The second feature fusion submodule is configured to perform feature fusion on the second initial feature corresponding to the target physical site in the current time period and the first historical context data corresponding to the target physical site before the start of the current time period, and acquire first reference context data corresponding to the target physical site at the end of the current time period.

[0132] In an embodiment, the model distillation module 115 includes: The basic scene feature acquisition submodule is configured to acquire a basic scene feature corresponding to the target physical site according to a business scenario type corresponding to the target physical site.

[0133] The data dimension reduction submodule is configured to reduce dimensions of the first historical context data and the second historical context data stored by the target memory before the preset model training time according to the basic scene feature, and obtain the first target context data, the second target context data and the target scene feature corresponding to the target physical site.

[0134] The model distillation submodule is configured to perform model distillation on the preset world model of the cloud at the preset model training time according to the first target context data, the second target context data and the target scene feature corresponding to the target physical site as training data, and obtain the edge world model corresponding to the target physical site, where a number of parameters of the preset world model is greater than a number of parameters corresponding to the edge world model.

[0135] In an embodiment, the data dimension reduction submodule includes: The feature difference value acquisition unit is configured to calculate a feature difference value between a feature value corresponding to a preset index and the basic scene feature for any feature value corresponding to the preset index in the first historical context data and the second historical context data stored by the target memory before the preset model training time.

[0136] The feature difference distribution diagram acquisition unit is configured to statistically acquire feature difference values corresponding to all preset indexes in the first historical context data and the second historical context data stored by the target memory before the preset model training time, and obtain a feature difference distribution diagram.

[0137] The target index screening unit is configured to screen all preset indexes according to the feature difference distribution diagram, and obtain a plurality of target indexes.

[0138] The data screening unit is configured to determine data corresponding to the target indexes in the first historical context data, the second historical context data and the basic scene feature stored by the target memory before the preset model training time as the first target context data, the second target context data and the target scene feature corresponding to the target physical site.

[0139] In an embodiment, the target application interface corresponds to a behavior prediction control task, the edge world model includes an edge prediction model, and the task execution module 116 includes: The first request receiving submodule is configured to receive a behavior prediction control request through the target application interface, where the behavior prediction control request includes a target control object and a prediction control requirement.

[0140] The first target data acquisition submodule is configured to acquire first target data corresponding to the behavior prediction control request from the first historical context data and the second historical context data stored by the target memory according to the behavior prediction control request.

[0141] The behavior prediction submodule is configured to input the first target data into the edge prediction model to obtain behavior prediction data corresponding to the behavior prediction control request, wherein the behavior prediction data is used to control the behavior of the target control object.

[0142] In an embodiment, the target application interface corresponds to a target application task, and the task execution module 116 further includes: The second request receiving submodule is configured to receive a target query request through the target application interface, wherein the target query request includes a target query object, a target query time, and a target query condition.

[0143] The index establishing submodule is configured to establish an index for the first historical context data and the second historical context data stored in the target storage.

[0144] The target query data obtaining submodule is configured to obtain target query data corresponding to the target query request from the first historical context data and the second historical context data stored in the target storage according to the index.

[0145] In an embodiment, the target application interface corresponds to a target application task, and the task execution module 116 further includes: The third request receiving submodule is configured to receive natural language text input by the target user through the target application interface.

[0146] The target question obtaining submodule is configured to obtain a target question corresponding to the target user according to the natural language text.

[0147] The second target data obtaining submodule is configured to obtain second target data corresponding to the target request from the first historical context data and the second historical context data stored in the target storage according to the target question.

[0148] The target reply obtaining submodule is configured to input the second target data into the edge language interaction model to obtain a target reply corresponding to the target question, wherein the target reply corresponds to a natural language form.

[0149] The data screening submodule is configured to determine data corresponding to the target index in the first historical context data, the second historical context data, and the basic scene feature as first target context data, second target context data, and a target scene feature corresponding to the target physical site.

[0150] It should be noted that the information interaction, execution process and the like between the above modules are based on the same concept as the method embodiments of the present application, and the specific functions and the resulting technical effects can be referred to the method embodiments part, which will not be described here.

[0151] The above is only a preferred embodiment of the present application, and does not limit the present application in any form. Although the present application has been disclosed as above with a preferred embodiment, it is not intended to limit the present application. Any person skilled in the art can make some changes or modifications to the above disclosed technical content without departing from the scope of the technical solution of the present application, and any simple modification, equivalent change and modification of the above embodiments according to the technical essence of the present application are still within the scope of the technical solution of the present application.

Claims

1. A method for environmental context processing and model optimization based on edge computing, characterized in that: The method comprises the following steps: S1, generating an ID corresponding to a target physical place and each target object in the target physical place, and allocating a corresponding target storage location for each ID in a target memory; S2, storing initial data corresponding to each target object in the target physical location within a current time period into a corresponding target storage location, wherein the target storage location also stores first historical context data corresponding to the target physical location and second historical context data corresponding to each target object before the start of the current time period, where the current time period refers to a continuous time period with a duration of T and ending at the current time; S3, obtaining, based on the initial data corresponding to each target object in the current time period, the first historical context data corresponding to the target physical place before the start of the current time period, and the second historical context data corresponding to each target object, the first reference context data corresponding to the target physical place and the second reference context data corresponding to each target object at the end of the current time period; S4, storing the first reference context data corresponding to the target physical place and the second reference context data corresponding to each target object at the end of the current time period as the first historical context data corresponding to the target physical place and the second historical context data corresponding to each target object before the start of the next time period in the corresponding target storage location; S5, performing model distillation on a preset world model in the cloud at the preset model training time based on the first historical context data and the second historical context data stored in the target memory before the preset model training time, to obtain an edge world model corresponding to the target physical place; S6 , calling a target application interface to execute a target application task corresponding to the target application interface according to the first historical context data and the second historical context data stored in the target memory and the edge world model.

2. The edge computing-based environmental context processing and model optimization method according to claim 1, characterized in that: The target physical location is provided with a plurality of data acquisition devices, each of which corresponds to a plurality of target objects in the target physical location. S2 includes the following steps: S21, obtaining the initial collected data of each target object corresponding to each data collection device in the current time period; S22, performing vectorization processing on the initial collected data corresponding to each target object in the target physical place within the current time period, and obtaining the initial vector features corresponding to each target object within the current time period; S23, performing semantic summarization on the initial collected data corresponding to each target object in the target physical place within the current time period, and obtaining an initial semantic description corresponding to each target object within the current time period; S24, summarizing the regularity of the initial collected data, initial vector features, and initial semantic description corresponding to each target object in the current time period, and obtaining local regularity data corresponding to each target object in the current time period; S25, taking the initial collected data, initial vector features, initial semantic description, local regularity data and current time period corresponding to each target object in the current time period as the initial data corresponding to each target object in the current time period.

3. The edge computing-based environmental context processing and model optimization method according to claim 2, characterized in that: S21 includes the following steps: S211, acquiring a preset data extraction rule corresponding to the target physical location according to the business scenario type corresponding to the target physical location; S212 , screening the original data collected by each data collection device in the current time period according to the preset data extraction rule, and obtaining the initial collected data of each target object corresponding to each data collection device in the current time period.

4. The edge computing-based environmental context processing and model optimization method according to claim 1, characterized in that: S3 includes the following steps: S31, for any target object in the target physical place, extracting features from initial data corresponding to the current target object in the current time period to obtain a first initial feature corresponding to the current target object in the current time period; S32, performing feature extraction on the second historical context data corresponding to the current target object before the start of the current time period, to obtain the first historical feature corresponding to the current target object before the start of the current time period; S33, performing feature fusion on the first initial feature corresponding to the current target object in the current time period and the first historical feature corresponding before the start of the current time period, and obtaining second reference context data corresponding to the current target object at the end of the current time period.

5. The edge computing-based environmental context processing and model optimization method according to claim 1, characterized in that: S3 includes the following steps: S34, performing feature extraction on initial data corresponding to all target objects in the target physical place within the current time period to obtain a second initial feature corresponding to the target physical place within the current time period; S35, perform feature fusion on the second initial features corresponding to the target physical place in the current time period and the first historical context data corresponding before the start of the current time period, and obtain the first reference context data corresponding to the target physical place at the end of the current time period.

6. The edge computing-based environmental context processing and model optimization method according to claim 1, characterized in that: S5 includes the following steps: S51, acquiring basic scenario features corresponding to the target physical place according to the business scenario type corresponding to the target physical place; S52, performing dimensionality reduction on the first historical context data and the second historical context data stored in the target memory before the preset model training time according to the basic scene features, to obtain the first target context data, the second target context data, and the target scene features corresponding to the target physical place; S53, based on the first target context data, the second target context data and the target scene features corresponding to the target physical place as training data, the preset world model in the cloud is subjected to model distillation at the preset model training time to obtain the edge world model corresponding to the target physical place, wherein the number of parameters of the preset world model is greater than the number of parameters corresponding to the edge world model.

7. The edge computing-based environmental context processing and model optimization method according to claim 6, characterized in that: S52 includes the following steps: S521, calculating a feature difference value between the feature value corresponding to the current preset indicator and the basic scene feature for a feature value corresponding to any preset indicator in the first historical context data and the second historical context data stored in the target memory before the preset model training time; S522: Counting feature difference values ​​corresponding to all preset indicators in the first historical context data and the second historical context data stored in the target memory before the preset model training time, and obtaining a feature difference distribution map; S523, screening all preset indicators according to the characteristic difference distribution map to obtain several target indicators; S524, the first historical context data, the second historical context data and the data corresponding to the target indicators in the basic scene features stored in the target memory before the preset model training time are determined as the first target context data, the second target context data and the target scene features corresponding to the target physical place.

8. The edge computing-based environmental context processing and model optimization method according to claim 1, characterized in that: The target application task corresponding to the target application interface includes a behavior prediction control task, the edge world model includes an edge prediction model, and S6 includes the following steps: S611, receiving a behavior prediction control request through the target application interface, wherein the behavior prediction control request includes a target control object and a prediction control requirement; S612, according to the behavior prediction control request, obtaining first target data corresponding to the behavior prediction control request from the first historical context data and the second historical context data stored in the target memory; S613: Input the first target data into the edge prediction model to obtain behavior prediction data corresponding to the behavior prediction control request, wherein the behavior prediction data is used to control the behavior of the target control object.

9. The edge computing-based environmental context processing and model optimization method according to claim 8, characterized in that: The target application task corresponding to the target application interface also includes a target query task, and S6 further includes the following steps: S621, receiving a target query request through the target application interface, wherein the target query request includes a target query object, a target query time, and a target query condition; S622, indexing the first historical context data and the second historical context data stored in the target memory; S623 : Acquire target query data corresponding to the target query request from the first historical context data and the second historical context data stored in the target memory according to the index.

10. The edge computing-based environmental context processing and model optimization method according to claim 8, characterized in that: The target application task corresponding to the target application interface further includes a natural language interaction task, the edge world model further includes an edge language interaction model, and S6 further includes the following steps: S631, receiving a natural language text input by a target user through the target application interface; S632, obtaining a target question corresponding to the target user according to the natural language text; S633 , according to the target question, obtaining second target data corresponding to the target request from the first historical context data and the second historical context data stored in the target memory; S634: Input the second target data into the edge language interaction model to obtain a target response corresponding to the target question, wherein the target response corresponds to a natural language form.

11. An environmental context processing and model optimization system based on edge computing, characterized in that: The edge computing-based environmental context processing and model optimization system includes: An ID generation module, configured to generate an ID corresponding to a target physical location and each target object in the target physical location, and to allocate a corresponding target storage location in a target memory to each ID; a first data storage module for storing initial data corresponding to each target object in the target physical location within a current time period in a corresponding target storage location, wherein the target storage also stores first historical context data corresponding to the target physical location and second historical context data corresponding to each target object before the start of the current time period, where the current time period refers to a continuous time period with a duration of T ending at the current time; a data processing module, configured to obtain, based on initial data corresponding to each target object in the current time period, first historical context data corresponding to the target physical place before the start of the current time period, and second historical context data corresponding to each target object, first reference context data corresponding to the target physical place and second reference context data corresponding to each target object at the end of the current time period; a second data storage module for storing the first reference context data corresponding to the target physical place and the second reference context data corresponding to each target object at the end of the current time period as the first historical context data corresponding to the target physical place and the second historical context data corresponding to each target object before the start of the next time period in the corresponding target storage location; a model distillation module, configured to perform model distillation on a preset world model in the cloud at a preset model training time based on first historical context data and second historical context data stored in the target memory before a preset model training time, to obtain an edge world model corresponding to the target physical location; A task execution module is used to call a target application interface to execute a target application task corresponding to the target application interface based on the first historical context data and the second historical context data stored in the target memory and the edge world model.

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