Sanitation big data cloud platform collaboration method and system, storage medium and program product

Abstract

The invention discloses an environmental sanitation big data cloud platform collaboration method and system, a storage medium and a program product, and relates to the technical field of environmental sanitation data processing. In the method, under the condition that a cloud platform receives current environmental sanitation data and corresponding identification information sent by a current subsystem, the cloud platform extracts fields and numerical values in the current environmental sanitation data; the cloud platform converts the data format of the current environmental sanitation data into a general format according to a preset mapping rule; and the cloud platform stores the converted current environmental sanitation data in a central database, and determines an index of the converted current environmental sanitation data according to the identification information, so that an island of the environmental sanitation data is broken, the integration difficulty of the environmental sanitation data is reduced, and intelligent decision making and optimal scheduling of subsequent intelligent environmental sanitation multi-type operation equipment are facilitated.

Description

Technical Field

[0001] The present application relates to the field of sanitation data processing technology, and in particular to a sanitation big data cloud platform collaboration method, system, storage medium and program product. Background Art

[0002] With the rapid development of smart cities, the sanitation industry is rapidly transforming from the traditional manual operation mode to the information and intelligent direction. In sanitation management, it is necessary to access and integrate a large amount of sanitation business data, including data generated by different operating equipment (such as sweepers, sprinklers, etc.) and data generated by different environmental sensors (such as PM2.5 detectors, noise detectors, etc.). These data come from a wide range of sources and in various formats, which brings huge challenges to the information management of sanitation.

[0003] At present, the information construction of the sanitation industry is scattered and independent. Different sanitation equipment manufacturers and management departments have built independent data management systems and adopted different data formats. This has led to the sanitation data being scattered in various isolated systems, making it impossible to achieve effective sharing and comprehensive utilization.

[0004] Under the existing decentralized sanitation information management model, it is often necessary to extract and integrate data from multiple systems to solve a certain business problem, which results in a complex analysis process and poor timeliness. Summary of the invention

[0005] The present application provides a collaborative method, system, storage medium and program product for a sanitation big data cloud platform, which are used to break the silos of sanitation data and reduce the difficulty of integrating sanitation data.

[0006] In the first aspect, the present application provides a sanitation big data cloud platform collaboration method, which is applied to a sanitation big data cloud platform collaboration system. The sanitation big data cloud platform collaboration system includes a cloud platform, a central database, and several subsystems. The method includes: When the cloud platform receives the current sanitation data and corresponding identification information sent by the current subsystem, the cloud platform extracts the fields and values ​​in the current sanitation data; The cloud platform converts the data format of the current sanitation data into a common format according to preset mapping rules; the preset mapping rules include the cloud platform converting fields into fields in the common format, the cloud platform renaming fields according to preset mapping rules, the cloud platform converting the data type of the field into the data type in the common format, and the cloud platform converting the unit of the value; the common format is determined by the majority of the historical sanitation data of all current subsystems; The cloud platform saves the converted current sanitation data into the central database and determines the index of the converted current sanitation data according to the identification information.

[0007] In the above embodiment, the cloud platform receives the current sanitation data and the corresponding identification information sent by the current subsystem, and extracts the fields and values ​​in the current sanitation data. In this way, the cloud platform can obtain the original sanitation data generated by each subsystem, laying the foundation for subsequent data processing. The cloud platform converts the data format of the current sanitation data into a common format according to the preset mapping rules. The preset mapping rules include field conversion, renaming, data type conversion, and unit conversion, and the common format is determined by the majority of the historical sanitation data of all current subsystems. This process realizes the unified conversion of heterogeneous sanitation data, eliminates the differences in data formats between different subsystems, enables sanitation data to be managed and applied under a unified format system, and greatly improves the accessibility and availability of data. The cloud platform saves the converted current sanitation data to the central database, and determines the index of the converted current sanitation data according to the identification information. This enables data to be efficiently retrieved and accessed, promotes the integrated application of sanitation big data, breaks the island of sanitation data, reduces the difficulty of integrating sanitation data, and facilitates the subsequent intelligent sanitation multi-type operation equipment intelligent decision-making and optimization scheduling.

[0008] In combination with some embodiments of the first aspect, in some embodiments, when the cloud platform receives the current sanitation data and corresponding identification information sent by the current subsystem, before the step of extracting the fields and values ​​in the current sanitation data by the cloud platform, the method further includes: When the cloud platform receives the information sending request sent by the current subsystem, the cloud platform detects the current processing load capacity; If the current processing load capacity is not lower than the processing load threshold, the cloud platform receives the current sanitation data and corresponding identification information sent by the current subsystem; If the current processing load capacity is lower than the processing load threshold, the cloud platform replies to the current subsystem with a waiting notification, which is used to notify the current subsystem to save the current sanitation data into its own database; The cloud platform sends data update notifications to other subsystems, where other subsystems are subsystems other than the current subsystem; Upon receiving an information acquisition request sent by a response subsystem, the cloud platform obtains the storage address of the current sanitation data from the current; the response subsystem is the subsystem that requests to obtain the current sanitation data; The cloud platform sends the storage address to the response subsystem, so that the response subsystem extracts the current sanitation data from the database of the current subsystem according to the storage address.

[0009] In the above embodiment, before the cloud platform receives the data request of the current subsystem, it first detects its own processing load capacity. If the load capacity is insufficient, the current subsystem is notified to temporarily store the data in its own database, and other subsystems are notified that the data has been updated. This method can prevent the cloud platform from being overloaded and affecting the data processing efficiency, ensuring the stable operation of the system. When other subsystems (response subsystems) request to obtain data from the current subsystem, if the cloud platform has insufficient load capacity at this time, it does not directly process and transmit the data, but sends the storage address to the response subsystem, so that it can directly obtain data from the database of the current subsystem. This point-to-point data interaction method reduces the processing pressure of the cloud platform and improves the real-time and flexibility of data sharing. Each subsystem can conveniently obtain data from other subsystems according to its own needs to support business collaboration.

[0010] In combination with some embodiments of the first aspect, in some embodiments, after the step of the cloud platform sending the storage address to the response subsystem so that the response subsystem extracts the current sanitation data from the database of the current subsystem according to the storage address, the method further includes: When the current processing load capacity changes from below the processing load threshold to not below the processing load threshold, the cloud platform sends instructions to the current subsystem and the response subsystem, causing the current subsystem and the response subsystem to send the current sanitation data; if any subsystem responds to the sending request, the cloud platform will reject the sending request of the other subsystem.

[0011] In the above embodiment, when the cloud platform load capacity recovers to above the threshold, it will instruct the current subsystem and the response subsystem to send data, but only accept the sending request of one subsystem for the same data. This ensures the consistency of data on the cloud platform side, avoids repeated storage and redundant processing, and improves system efficiency. At this time, the cloud platform becomes the center of data interaction again, coordinating the flow of data between subsystems. It ensures that the system can operate efficiently under different load conditions, maximizing the data processing capacity and scheduling function of the cloud platform.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, the step of extracting fields and values ​​in the current sanitation data by the cloud platform specifically includes: The cloud platform obtains the average data access time of the same type of historical data based on the type of current sanitation data: If the average value of the data access time is less than the preset access time threshold, the current sanitation data is determined to be real-time processing data; If the average value of the data access time is not less than the preset access time threshold, the current sanitation data is determined to be batch data; For real-time data processing, the cloud platform extracts fields and values ​​from current sanitation data; For batch data, the cloud platform waits for the same batch of data to reach the preset upper limit or reach the preset time threshold, and then extracts the fields and values ​​in the current sanitation data for the same batch of current sanitation data.

[0013] In the above embodiment, the cloud platform obtains the average data access time of the same type of historical data according to the type of current sanitation data, and then determines whether the current data is real-time processing data or batch processing data. It can flexibly adopt different processing strategies according to the characteristics of the data to adapt to the diversity of sanitation scenarios. For data with high real-time requirements, the cloud platform extracts and processes it immediately; for batch data with low real-time requirements, the cloud platform waits for the data to accumulate to a certain amount or a certain period of time before batch processing. This differentiated processing method can reduce frequent data extraction and conversion operations while meeting the real-time requirements of data, improve the overall processing efficiency of the system, and achieve optimal task scheduling and execution under limited computing resources.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, the step of the cloud platform saving the converted current sanitation data to the central database specifically includes: For batch data, the cloud platform saves the converted current sanitation data to the low-speed storage medium of the central database; For real-time data processing, the cloud platform saves the converted current sanitation data to the high-speed storage medium of the central database. The reading speed of the high-speed storage medium is greater than that of the low-speed storage medium.

[0015] In the above embodiment, for batch data with low access frequency and low real-time requirements, the use of low-speed storage media can reduce storage costs; while for real-time processing data with high access frequency and high real-time requirements, the use of high-speed storage media can speed up data reading and writing speeds and shorten data access delays. This hierarchical storage mechanism realizes the rational use and dynamic optimization of storage resources, and while meeting different data access requirements, it also improves the cost-effectiveness of the system.

[0016] In combination with some embodiments of the first aspect, in some embodiments, after the cloud platform saves the converted current sanitation data to the central database and determines the index of the converted current sanitation data according to the identification information, the method further includes: The cloud platform determines whether the value after unit conversion exceeds the maximum length specified by the central database; If it exceeds the maximum length specified by the central database, the cloud platform performs unit conversion on the converted value to make it smaller than the maximum length specified by the central database; The cloud platform adds an identifier, which is used to indicate that the unit of the converted value is inconsistent with the unit of other values ​​when displaying data.

[0017] In the above embodiment, by adopting the above technical solution, when the cloud platform stores the converted sanitation data into the central database, it will determine whether the value after unit conversion exceeds the specified maximum length. This step can detect the overlong values ​​that may cause display abnormalities in advance before the data is stored, providing a basis for subsequent processing. By adopting the above technical solution, for values ​​that exceed the maximum length, the cloud platform will perform unit conversion so that its length meets the requirements. Unit conversion can compress the value to an appropriate length by adjusting the representation of the value while keeping the essence of the value unchanged. This method cleverly solves the problem of overlong values ​​and ensures that the data can be displayed normally. For values ​​that have undergone unit conversion, the cloud platform will add a special identifier. This identifier serves as a prompt when the data is displayed, informing the user that the unit of the value is inconsistent with other values.

[0018] In combination with some embodiments of the first aspect, in some embodiments, after the cloud platform saves the converted current sanitation data to the central database and determines the index of the converted current sanitation data according to the identification information, the method further includes: The cloud platform notifies all subscribers of the current subsystem to let them know that the data of the subscribed current subsystem has been updated. The subscribers are other subsystems that have subscribed to the current subsystem.

[0019] In the above embodiment, a subscription-based data dependency relationship is established between the subsystems. Each subsystem can subscribe to the data of other subsystems according to its own business needs. When the subscribed data is updated, the cloud platform acts as an information intermediary and pushes the update notification to the subscriber in a timely manner. The collaboration efficiency between subsystems is improved and the data sharing process is simplified. The subsystem does not need to actively query and extract data, but only needs to passively receive updates, which greatly saves time and resource expenses.

[0020] In a second aspect, an embodiment of the present application provides a sanitation big data cloud platform collaboration system, the system comprising: one or more processors and a memory; The memory is coupled to the one or more processors, and the memory is used to store computer program code, which includes computer instructions. The one or more processors call the computer instructions to enable the sanitation big data cloud platform collaborative system to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product comprising instructions, which, when executed on a server, enables the server to execute the method described in the first aspect and any possible implementation of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium comprising instructions. When the above instructions are run on a collaborative system of a sanitation big data cloud platform, the above collaborative system of a sanitation big data cloud platform executes the method described in the first aspect and any possible implementation method in the first aspect.

[0023] It can be understood that the sanitation big data cloud platform collaborative system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the sanitation big data cloud platform collaborative method provided in the embodiment of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, and will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. The sanitation big data cloud platform collaboration method provided by the present application, the cloud platform receives the current sanitation data and the corresponding identification information sent by the current subsystem, and extracts the fields and values ​​in the current sanitation data. In this way, the cloud platform can obtain the original sanitation data generated by each subsystem, laying the foundation for subsequent data processing. The cloud platform converts the data format of the current sanitation data into a common format according to the preset mapping rules. The preset mapping rules include field conversion, renaming, data type conversion and unit conversion, and the common format is determined by the mode in the historical sanitation data of all current subsystems. This process realizes the unified conversion of heterogeneous sanitation data, eliminates the differences in data formats between different subsystems, enables sanitation data to be managed and applied under a unified format system, and greatly improves the accessibility and availability of data. The cloud platform saves the converted current sanitation data to the central database, and determines the index of the converted current sanitation data according to the identification information. The data can be efficiently retrieved and accessed, which promotes the integrated application of sanitation big data, thereby breaking the island of sanitation data, reducing the difficulty of integrating sanitation data, and facilitating the subsequent intelligent sanitation multi-type operation equipment intelligent decision-making and optimization scheduling.

[0025] 2. The collaborative method of the sanitation big data cloud platform provided in this application first detects its own processing load capacity before the cloud platform receives the data request of the current subsystem. If the load capacity is insufficient, the current subsystem is notified to temporarily store the data in its own database, and other subsystems are notified that the data has been updated. This method can avoid the cloud platform being overloaded and affecting the data processing efficiency, ensuring the stable operation of the system. When other subsystems (response subsystems) request to obtain data from the current subsystem, if the cloud platform has insufficient load capacity at this time, it does not directly process and transmit the data, but sends the storage address to the response subsystem, so that it can directly obtain data from the database of the current subsystem. This point-to-point data interaction method reduces the processing pressure of the cloud platform and improves the real-time and flexibility of data sharing. Each subsystem can easily obtain data from other subsystems according to its own needs to support business collaboration.

[0026] 3. The sanitation big data cloud platform collaboration method provided in this application, the cloud platform obtains the average data access time of the same type of historical data according to the type of current sanitation data, and then determines whether the current data is real-time processing data or batch processing data. It can flexibly adopt different processing strategies according to the characteristics of the data to adapt to the diversity of sanitation scenarios. For data with high real-time requirements, the cloud platform extracts and processes it immediately; for batch data with low real-time requirements, the cloud platform waits for the data to accumulate to a certain amount or a certain period of time before batch processing. This differentiated processing method can reduce frequent data extraction and conversion operations while meeting the real-time requirements of data, improve the overall processing efficiency of the system, and achieve optimal task scheduling and execution under limited computing resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a structural diagram of an applicable system architecture for collaboration with the sanitation big data cloud platform in the embodiment of the present application.

[0028] Figure 2 A flow chart of the collaborative method of the sanitation big data cloud platform provided in this application.

[0029] Figure 3 A timing diagram of the collaborative method of the sanitation big data cloud platform provided in this application.

[0030] Figure 4 Another flowchart of the sanitation big data cloud platform collaboration method provided for this application.

[0031] Figure 5 A schematic diagram of the physical device of the sanitation big data cloud platform collaborative system provided in this application. DETAILED DESCRIPTION

[0032] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more listed items.

[0033] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0034] Figure 1 It is a structural diagram of an applicable system architecture for collaboration with the sanitation big data cloud platform in the embodiment of the present application.

[0035] like Figure 1 As shown, the collaborative system of the sanitation big data cloud platform includes a cloud platform, a central database, multiple subsystems, and multiple databases.

[0036] The cloud platform is the core of the collaborative system of the environmental sanitation big data cloud platform. It is mainly responsible for unifying the multi-source heterogeneous data uploaded by each subsystem into a standard format and storing it in the central database. The cloud platform also undertakes the scheduling function of data interaction between subsystems, and coordinates data access and sharing between subsystems based on the requests of each subsystem and its own load. At the same time, the cloud platform also provides data application services such as data calculation, analysis, and visualization.

[0037] The central database is the central warehouse of sanitation big data. It adopts a distributed storage architecture and provides unified storage management for massive structured, semi-structured and unstructured data. It provides a unified data access interface to the outside world and supports flexible access to the cloud platform and various subsystems.

[0038] In some embodiments, the central database is highly scalable and can meet the growing demands for sanitation data scale and processing performance through horizontal expansion.

[0039] It should be noted that in some embodiments, the subsystem can also be directly connected to the central database, so there is no need for cloud platform scheduling, reducing frequent data conversion operations.

[0040] Subsystems are application systems for various business areas of sanitation management, such as sanitation vehicle management system, sanitation operation management system, environmental monitoring system, etc. Each subsystem generates and manages sanitation data within its own field, and uploads the data to the central database through interaction with the cloud platform to achieve data sharing; at the same time, the subsystem can also access the central database or the database of other subsystems to obtain the data required for its own business.

[0041] The database is the local data storage of the subsystem, mainly storing data that has not yet been uploaded to the cloud platform or synchronized from the cloud platform and other systems, providing data support for the business operations of this subsystem.

[0042] It should be noted that, in some embodiments, when the cloud platform load capacity is insufficient, the subsystem may also directly connect to the database of other subsystems.

[0043] The following describes the collaborative method of the sanitation big data cloud platform in this embodiment: like Figure 2 As shown, Figure 2 A flow chart of the collaborative method of the sanitation big data cloud platform provided in this application.

[0044] A sanitation big data cloud platform collaboration method is applied to a sanitation big data cloud platform collaboration system. The sanitation big data cloud platform collaboration system includes a cloud platform, a central database, and several subsystems. The method includes: S201. When the cloud platform receives the current sanitation data and corresponding identification information sent by the current subsystem, the cloud platform extracts the fields and values ​​in the current sanitation data.

[0045] Identification information refers to the metadata uploaded together with the current sanitation data, which is used to identify the source, type, time and other attributes of the data.

[0046] The current subsystem refers to any subsystem, which is used to understand the technical solution. Similarly, the current sanitation data refers to any sanitation data.

[0047] Specifically, step S201 describes the triggering conditions and subsequent operations for the cloud platform to receive data. When the cloud platform detects a data upload request from the current subsystem, it will start the receiving program and obtain the identification information of the batch of data. After receiving, the cloud platform will parse the current sanitation data and extract the various fields and corresponding values. At the same time, the cloud platform will also record the identification information of the current data for subsequent data indexing and tracing.

[0048] In some embodiments, step S201 can be implemented in a variety of ways: Optionally, the cloud platform provides a standard data interface, the current subsystem encapsulates the sanitation data and identification information according to the interface specification, and actively initiates an upload request to the cloud platform. After receiving the request, the cloud platform calls the corresponding interface function to parse the request parameters, obtains the current sanitation data and identification information, and then extracts the field name and field value from the current sanitation data through the predefined data format template; Optionally, the cloud platform regularly scans the data storage location of the subsystem, and when new or updated sanitation data is found, it actively pulls these data and associated identification information, and calls the data parsing module to automatically separate the fields and their corresponding values ​​according to the preset field extraction rules. It is understandable that other methods can also be used to implement the cloud platform to receive and extract the current sanitation data and identification information, such as using a message queue for data transmission, or converting the data into a standardized file format and then uploading it to the cloud platform, etc., which are not limited here.

[0049] S202. The cloud platform converts the data format of the current sanitation data into a universal format according to preset mapping rules; the preset mapping rules include the cloud platform converting fields into fields in the universal format, the cloud platform renaming fields according to the preset mapping rules, the cloud platform converting the data type of the field into the data type in the universal format, and the cloud platform converting the unit of the numerical value; the universal format is determined by the majority of the historical sanitation data of all current subsystems.

[0050] The preset mapping rules refer to a set of conversion rules pre-defined by the cloud platform for converting the sanitation data uploaded by each subsystem into a unified format.

[0051] The universal format refers to the standardized data format defined by the cloud platform and applicable to all subsystem data, including unified regulations on field naming, data type, value unit, etc.

[0052] In some embodiments, the common format selects the most frequently occurring field names, data types, and units as the standard. This "majority"-based common format formulation method can minimize the workload of data conversion and improve data compatibility between subsystems.

[0053] The cloud platform converts fields into fields in a common format, which means that the presentation of each field in the original data, such as font, font size, alignment, color, etc., is standardized to a unified standard to ensure consistent display and processing of data across various cloud platform systems. Usually, the cloud platform predefines a set of field display formats that match the sanitation business, covering various descriptive attributes of the field.

[0054] The cloud platform renames the fields according to the preset mapping rules, which means converting the original field names into standard field names with unified semantics based on the industry standards for sanitation data management and the field naming specifications of the cloud platform, so as to achieve consistent understanding of the field meaning among various subsystems.

[0055] The cloud platform converts the data type of the field into a data type in a universal format. This means converting technical types in some original fields that are not related to sanitation business, such as character dates, integers of different bit widths, high-precision floating points, etc., into a set of standard types defined in the cloud platform data processing specifications. This can avoid data processing errors caused by inconsistent types and reduce unnecessary storage overhead.

[0056] The cloud platform converts the units of values, which means identifying and extracting the measurement units of the numerical fields in the original data, and performing necessary mathematical conversions on the values ​​with reference to the standard measurement units specified in the environmental sanitation indicator system, and finally unifying them into units generally accepted by all subsystems. For example, the mass unit "gram" is standardized to "kilogram", and the speed unit "mile / hour" is standardized to "kilometer / hour".

[0057] In some embodiments, step S202 can be implemented in a variety of ways: Optionally, a system administrator or data expert manually formulates mapping rules in advance based on the data characteristics and business needs of each subsystem, and enters them into the cloud platform. When the data is uploaded, the cloud platform reads the mapping rules and processes the original fields one by one using the conversion method defined in the rules. It is understandable that other methods can also be used to achieve sanitation data format conversion, such as establishing a data dictionary between the cloud platform and the subsystem, or using an artificial intelligence algorithm to automatically learn conversion rules, etc., which are not limited here.

[0058] S203: The cloud platform saves the converted current sanitation data into the central database, and determines the index of the converted current sanitation data according to the identification information.

[0059] There are many ways to save the converted sanitation data to the central database and create an index: Optionally, the cloud platform splits the converted data according to the predefined database table structure, inserts the values ​​of different fields into the corresponding data tables through SQL statements, and then extracts the index key in the identification information according to the index creation rules of the database, and uses the index creation interface or command provided by the database to create an index on the corresponding data table; Optionally, the cloud platform serializes the converted data together with the identification information into a structured data format, such as JSON or XML, and then writes the serialized data packet as a whole into a table in the central database that specifically stores serialized data, and then creates a combined index on the table based on the identification information in the serialized data packet, and the content of the index is multiple attribute values ​​of the identification information. It is understandable that other methods can also be used to save and index sanitation data, such as using a non-relational database to store data and create an index based on memory, or saving data to a distributed file system and managing the index through an external index service, etc., which are not limited here.

[0060] It can be seen that the cloud platform receives the current sanitation data and corresponding identification information sent by the current subsystem, and extracts the fields and values ​​in the current sanitation data. In this way, the cloud platform can obtain the original sanitation data generated by each subsystem, laying the foundation for subsequent data processing. The cloud platform converts the data format of the current sanitation data into a common format according to the preset mapping rules. The preset mapping rules include field conversion, renaming, data type conversion, and unit conversion. The common format is determined by the majority of the historical sanitation data of all current subsystems. This process realizes the unified conversion of heterogeneous sanitation data, eliminates the differences in data formats between different subsystems, enables sanitation data to be managed and applied under a unified format system, and greatly improves the accessibility and availability of data. The cloud platform saves the converted current sanitation data to the central database, and determines the index of the converted current sanitation data according to the identification information. This enables data to be efficiently retrieved and accessed, promotes the integrated application of sanitation big data, breaks the island of sanitation data, reduces the difficulty of integrating sanitation data, and facilitates the subsequent intelligent decision-making and optimized scheduling of various types of smart sanitation equipment.

[0061] During actual use, a problem may occur: although the data retrieved in the subsystem is displayed normally, the data exceeds the display border when retrieved on the cloud platform. This is because the cloud platform converts the units of the values.

[0062] S204, the cloud platform determines whether the value after the unit conversion exceeds the maximum length specified by the central database; The maximum length specified by the central database refers to the maximum number of characters or bytes predefined for a specific field (such as a numeric type) when the database table is designed.

[0063] Specifically, step S204 describes that in the process of converting sanitation data to a universal format, the cloud platform needs to unify the units of measurement values ​​and determine whether the length of the converted value is compatible with the database field. Since the sanitation data uploaded by different subsystems may use different units (such as meters, kilometers, etc.), the cloud platform will first refer to a global measurement unit mapping table to convert these heterogeneous units into unified standard units (such as all converted to meters). However, the unification of units may cause some values ​​to become very large (such as kilometers in length becoming meters), which exceeds the length limit of the corresponding field when the central database table is designed. Therefore, the cloud platform also needs to check the length of the value after the unit conversion. If it is found to be too long, it is necessary to enter step S205 for secondary unit conversion (such as meters to kilometers) to adapt to the database field. Only when the length of the converted value meets the field limit can the subsequent warehousing operation be performed.

[0064] S205. If the maximum length specified by the central database is exceeded, the cloud platform performs unit conversion on the converted value so that it is less than the maximum length specified by the central database; S206. The cloud platform adds an identifier, where the identifier is used to indicate that the unit of the converted value is inconsistent with the unit of other values ​​when displaying data.

[0065] The role of the identifier is to remind users when the system displays sanitation data that some numerical fields have undergone unit conversion and may be inconsistent with other data of the same measurement. The process of adding identifiers means that after the cloud platform performs a secondary unit conversion on an extra-long value, it needs to explicitly identify the converted field to remind system users of the change in numerical units. For example, different colors are used to display the converted numerical value.

[0066] It can be seen that by adopting the above technical solution, when the cloud platform stores the converted sanitation data into the central database, it will determine whether the value after unit conversion exceeds the specified maximum length. This step can detect the overlong values ​​that may cause display abnormalities in advance before the data is stored, providing a basis for subsequent processing. By adopting the above technical solution, for values ​​that exceed the maximum length, the cloud platform will perform unit conversion so that its length meets the requirements. Unit conversion can compress the value to an appropriate length by adjusting the representation of the value while keeping the essence of the value unchanged. This method cleverly solves the problem of overlong values ​​and ensures that the data can be displayed normally. For values ​​that have undergone unit conversion, the cloud platform will add a special identifier. This identifier serves as a prompt when the data is displayed, informing the user that the unit of the value is inconsistent with other values.

[0067] S207. The cloud platform notifies all subscribers of the current subsystem to let the subscribers know that the data of the subscribed current subsystem has been updated. The subscribers are other subsystems that have subscribed to the current subsystem.

[0068] Subscribers are other business subsystems that declare that they need to obtain the latest data of certain subsystems in real time when registering on the cloud platform. Other subsystems that have subscribed to the current subsystem are those that have set the current subsystem as their upstream data source in the subscription relationship configuration of the cloud platform. The process of the cloud platform notifying subscribers means that after the latest data of the current subsystem is stored in the database, the cloud platform needs to actively send a data update event notification to all other subsystems that have declared their dependence on the data.

[0069] In some embodiments, subscriber notifications of the cloud platform can be implemented in a variety of ways: Optionally, the cloud platform uses an independent message queue service as a publish-subscribe channel, writes messages to the corresponding queue when each data table is updated, and the subscriber system listens to its respective queue to receive data update events; it is understandable that other notification mechanisms can also be used, which are not limited here.

[0070] It can be seen that a subscription-based data dependency relationship is established between subsystems. Each subsystem can subscribe to the data of other subsystems according to its own business needs. When the subscribed data is updated, the cloud platform acts as an information intermediary and pushes the update notification to the subscriber in a timely manner. This improves the collaboration efficiency between subsystems and simplifies the data sharing process. Subsystems do not need to actively query and extract data, but only need to passively receive updates, which greatly saves time and resource expenses.

[0071] In the above embodiments, the sanitation data islands are broken and the difficulty of integrating sanitation data is reduced. In practical applications, when executing the above sanitation big data cloud platform collaboration method, different sanitation subsystems need to quickly exchange data, but the cloud platform has limited processing load capacity, resulting in slow data exchange between different sanitation subsystems, affecting usage. Figure 3 The timing diagram of the collaborative method of the sanitation big data cloud platform shown in the figure specifically describes the collaborative method of the sanitation big data cloud platform in the embodiment of the present application: like Figure 3 As shown, Figure 3 A timing diagram of the collaborative method of the sanitation big data cloud platform provided in this application.

[0072] S301. The current subsystem sends an information sending request to the cloud server.

[0073] The information sending request refers to the data upload request initiated by the current subsystem to the cloud platform, which is used to indicate that the current subsystem wishes to submit the latest collected sanitation data to the cloud platform for processing.

[0074] S302: The cloud platform detects whether the current processing load capacity is lower than the processing load threshold.

[0075] If the current processing load capacity is lower than the processing load threshold, execute step S303; If the current processing load capacity is not lower than the processing load threshold, step S313 is executed.

[0076] The processing load capacity refers to the remaining capacity of computing resources and storage resources that the cloud platform can currently use to process data upload requests. The processing load threshold refers to a resource utilization limit pre-set by the cloud platform. When the actual resource utilization exceeds the threshold, it means that the cloud platform has difficulty in taking on new data processing tasks and needs to take measures such as flow control and queuing to control the load.

[0077] Specifically, step S302 describes the process of the cloud platform performing a load check after receiving the information sending request. The cloud platform monitors its own key indicators such as CPU usage, memory usage, disk space usage, etc. in real time, and calculates a comprehensive processing load capacity indicator based on this.

[0078] S303: The cloud platform replies to the current subsystem with a waiting notification.

[0079] Among them, reply waiting notification means that the cloud platform sends a waiting upload message to the current subsystem, informing the current subsystem that due to insufficient load capacity of the cloud platform, it is temporarily unable to receive new sanitation data. The current subsystem needs to cache the data locally and wait for subsequent upload notifications from the cloud platform.

[0080] S304. The current subsystem saves the current sanitation data into its own database.

[0081] Among them, the own database refers to the local data storage independently maintained by the current subsystem, which is used to store the original data generated during the business process.

[0082] S305: The cloud platform sends a data update notification to other subsystems.

[0083] The data update notification refers to a notification message sent by the cloud platform to other subsystems except the current subsystem, which is used to inform them that the current subsystem has new sanitation data that needs to be synchronized. Other subsystems refer to all subsystems in the entire cloud platform collaborative system except the subsystem that currently uploads data.

[0084] S306. The response subsystem sends an information acquisition request to the cloud server.

[0085] The responding subsystem refers to the non-current subsystem that needs to actively request data synchronization after receiving the data update notification sent by the cloud platform. The information acquisition request refers to the data acquisition request initiated by the responding subsystem to the cloud platform in order to obtain the latest sanitation data uploaded by the current subsystem.

[0086] S307: The cloud platform obtains the storage address of the current sanitation data.

[0087] The storage address indicates the storage location of the current sanitation data in the local database of the current subsystem, which is usually composed of database connection information and data table name, etc. The process of obtaining the storage address from the current subsystem refers to the cloud platform learning the specific location of the latest sanitation data through interaction with the current subsystem.

[0088] S308: The cloud platform sends the storage address to the response subsystem.

[0089] S309. The response subsystem extracts current sanitation data from the database of the current subsystem according to the storage address.

[0090] S310: The cloud platform detects whether the current processing load capacity is lower than the processing load threshold.

[0091] If the current processing load capacity is lower than the processing load threshold, execute step S310; If the current processing load capacity is not lower than the processing load threshold, step S311 is executed.

[0092] S311, the cloud platform sends instructions to the current subsystem and the response subsystem, so that the current subsystem and the response subsystem send the current sanitation data; if any subsystem responds to the sending request, the cloud platform will reject the sending request of the other subsystem.

[0093] S312. The current subsystem or the response subsystem sends the current sanitation data to the cloud platform.

[0094] It can be seen that when the cloud platform load capacity recovers to above the threshold, it will instruct the current subsystem and the response subsystem to send data, but only accept the sending request of one subsystem for the same data. This ensures the consistency of data on the cloud platform side, avoids repeated storage and redundant processing, and improves system efficiency. At this time, the cloud platform becomes the center of data interaction again, coordinating the flow of data between subsystems. It ensures that the system can operate efficiently under different load conditions and maximizes the data processing capacity and scheduling function of the cloud platform.

[0095] S313. The cloud platform extracts fields and values ​​from the current sanitation data.

[0096] This step has been described in detail in the above step S101. The relevant process and principle can be referred to step S101 and will not be repeated here.

[0097] S314. The cloud platform converts the data format of the current sanitation data into a universal format according to preset mapping rules.

[0098] This step has been described in detail in the above step S102. The relevant process and principle can be referred to step S102, which will not be repeated here.

[0099] S315. The cloud platform saves the converted current sanitation data into the central database, and determines the index of the converted current sanitation data according to the identification information.

[0100] This step has been described in detail in the above step S103. The relevant process and principle can be referred to step S103, which will not be repeated here.

[0101] It can be seen that before the cloud platform receives the data request of the current subsystem, it first detects its own processing load capacity. If the load capacity is insufficient, the current subsystem is notified to temporarily store the data in its own database, and other subsystems are notified that the data has been updated. This method can prevent the cloud platform from being overloaded and affecting the data processing efficiency, ensuring the stable operation of the system. When other subsystems (response subsystems) request to obtain data from the current subsystem, if the cloud platform has insufficient load capacity at this time, it does not directly process and transmit the data, but sends the storage address to the response subsystem, so that it can directly obtain data from the database of the current subsystem. This point-to-point data interaction method reduces the processing pressure of the cloud platform and improves the real-time and flexibility of data sharing. Each subsystem can easily obtain data from other subsystems according to its own needs to support business collaboration.

[0102] In the above embodiments, each subsystem can conveniently obtain data from other subsystems according to its own needs to support business collaboration. In actual applications, when executing the above sanitation big data cloud platform collaboration method, the sanitation management system may need to process ultra-large-scale data, including real-time data streams and historical data storage. As the size of cities expands and the number of sensors increases, the amount of data may grow exponentially, making it difficult for traditional data management methods to cope with it. Figure 4 Another flowchart of the collaborative method of the sanitation big data cloud platform shown in FIG. 1 specifically describes the collaborative method of the sanitation big data cloud platform in the embodiment of the present application: like Figure 4 As shown, Figure 4 Another flowchart of the sanitation big data cloud platform collaboration method provided for this application.

[0103] S401. The cloud platform obtains the average data access time of the same type of historical data according to the type of current sanitation data.

[0104] The same type of historical data refers to the sanitation data of the same type as the current sanitation data that has been previously stored on the cloud platform. The average value of data access time is used to measure the frequency of use of this type of data, that is, how long it takes on average to access it. The preset access time threshold is a time length pre-set by the cloud platform to determine whether the data is frequently accessed.

[0105] Specifically, step S401 describes that after the cloud platform receives new sanitation data, it first needs to decide how to handle this batch of new data based on the access frequency of similar data in the past. Based on the data classification, the cloud platform will count the access time intervals of each type of historical data, such as the average number of reads per day in the past month. Then, the cloud platform will take the average access time of each type of data. If the average access time is less than a preset threshold, it means that this type of data is usually accessed frequently and needs to be processed in real time to ensure timeliness. At this time, the process enters step S402 to mark the current data as real-time processing data. If the average access time is greater than or equal to the threshold, it means that this type of data is rarely used and does not need to be processed immediately. At this time, the process enters step S406 to mark the current data as batch processing data.

[0106] S402: Determine that the current sanitation data is real-time processing data.

[0107] S403. For real-time data processing, the cloud platform extracts fields and values ​​from current sanitation data.

[0108] This step has been described in detail in the above step S101. The relevant process and principle can be referred to step S101 and will not be repeated here.

[0109] S404. The cloud platform converts the data format of the current sanitation data into a universal format according to preset mapping rules; the preset mapping rules include the cloud platform converting fields into fields in the universal format, the cloud platform renaming fields according to the preset mapping rules, the cloud platform converting the data type of the field into the data type in the universal format, and the cloud platform converting the unit of the numerical value; the universal format is determined by the majority of the historical sanitation data of all current subsystems.

[0110] This step has been described in detail in the above step S102. The relevant process and principle can be referred to step S102, which will not be repeated here.

[0111] S405. For real-time processing of data, the cloud platform saves the converted current sanitation data to the high-speed storage medium of the central database, and determines the index of the converted current sanitation data according to the identification information.

[0112] This step has been described in detail in the above step S103. The relevant process and principle can be referred to step S103, which will not be repeated here.

[0113] S406: If the average value of the data access time is not less than the preset access time threshold, it is determined that the current sanitation data is batch data.

[0114] S407. For batch data, the cloud platform waits for the same batch of data to reach a preset upper limit or a preset time threshold, and then extracts fields and values ​​from the current sanitation data for the same batch of current sanitation data.

[0115] The same batch of data refers to a group of sanitation data of the same data type that are uploaded continuously within a certain period of time. The preset upper limit indicates the maximum amount of data that the cloud platform specifies for processing batch data in batches. The preset time threshold is used to indicate the maximum waiting time for caching batch data on the cloud platform, that is, the latest time point when processing must begin.

[0116] Specifically, step S407 describes that after the cloud platform finds that the current data belongs to the batch processing type, it will not process it immediately, but will temporarily store it and wait for more similar data to arrive before processing it together to improve efficiency. The cloud platform will set up a buffer for each type of batch data. When the same batch of sanitation data arrives one after another, the cloud platform will store them in the buffer of the corresponding category. The cloud platform will not batch process the data in the entire buffer until the amount of data in the buffer reaches the preset upper limit, or the earliest received data has been waiting for a long enough time.

[0117] S408. The cloud platform converts the data format of the current sanitation data into a universal format according to preset mapping rules; the preset mapping rules include the cloud platform converting fields into fields in the universal format, the cloud platform renaming fields according to preset mapping rules, the cloud platform converting the data type of the field into the data type in the universal format, and the cloud platform converting the unit of the numerical value; the universal format is determined by the majority of the historical sanitation data of all current subsystems.

[0118] This step has been described in detail in the above step S102. The relevant process and principle can be referred to step S102, which will not be repeated here.

[0119] S409. For batch data, the cloud platform saves the converted current sanitation data to the low-speed storage medium of the central database and determines the index of the converted current sanitation data according to the identification information.

[0120] This step has been described in detail in the above step S103. The relevant process and principle can be referred to step S103, which will not be repeated here.

[0121] It can be seen that the cloud platform obtains the average data access time of the same type of historical data according to the type of current sanitation data, and then determines whether the current data is real-time processing data or batch processing data. It can flexibly adopt different processing strategies according to the characteristics of the data to adapt to the diversity of sanitation scenarios. For data with high real-time requirements, the cloud platform extracts and processes it immediately; for batch data with low real-time requirements, the cloud platform waits for the data to accumulate to a certain amount or a certain period of time before batch processing. This differentiated processing method can reduce frequent data extraction and conversion operations while meeting the real-time requirements of data, improve the overall processing efficiency of the system, and achieve optimal task scheduling and execution under limited computing resources.

[0122] It can be seen that for batch data with low access frequency and low real-time requirements, using low-speed storage media can reduce storage costs; while for real-time processing data with high access frequency and high real-time requirements, using high-speed storage media can speed up data reading and writing and shorten data access latency. This hierarchical storage mechanism realizes the rational use and dynamic optimization of storage resources, and improves the cost-effectiveness of the system while meeting different data access requirements.

[0123] The following are device embodiments of the present application, which can be used to execute the method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.

[0124] This application also discloses a sanitation big data cloud platform collaborative system. Figure 5 , which is a schematic diagram of a physical device of the sanitation big data cloud platform collaborative system provided by the present application. The computer 500 may include: at least one processor 501, at least one network interface 504, a user interface 503, a memory 505, and at least one communication bus 502.

[0125] The communication bus 502 is used to realize the connection and communication between these components.

[0126] The user interface 503 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 503 may also include a standard wired interface and a wireless interface.

[0127] The network interface 504 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0128] Among them, the processor 501 may include one or more processing cores. The processor 501 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 505, and calling data stored in the memory 505. Optionally, the processor 501 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 501 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processor (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 501, and it can be implemented separately through a chip.

[0129] Among them, the memory 505 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 505 includes a non-transitory computer-readable storage medium. The memory 505 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 505 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store data involved in the above-mentioned method embodiments, etc. The memory 505 may optionally be at least one storage device located away from the aforementioned processor 501. Refer to Figure 5 , the memory 505 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program that collaborates with the sanitation big data cloud platform.

[0130] exist Figure 5In the computer 500 shown, the user interface 503 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 501 can be used to call the collaborative application of the sanitation big data cloud platform stored in the memory 505. When executed by one or more processors 501, the computer 500 executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited to the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0131] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0132] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0133] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0134] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0135] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0136] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0137] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A collaborative method for a sanitation big data cloud platform, characterized in that: Applied to a sanitation big data cloud platform collaborative system, the sanitation big data cloud platform collaborative system includes a cloud platform, a central database and several subsystems, the method includes: When the cloud platform receives the current sanitation data and corresponding identification information sent by the current subsystem, the cloud platform extracts fields and values ​​in the current sanitation data; The cloud platform converts the data format of the current sanitation data into a universal format according to a preset mapping rule; the preset mapping rule includes the cloud platform converting the field into a field in the universal format, the cloud platform renaming the field according to the preset mapping rule, the cloud platform converting the data type of the field into a data type in the universal format, and the cloud platform converting the value into a unit; the universal format is determined by the mode of the historical sanitation data of all current subsystems; The cloud platform saves the converted current sanitation data into the central database, and determines the index of the converted current sanitation data according to the identification information.

2. The collaborative method of the sanitation big data cloud platform according to claim 1 is characterized in that: When the cloud platform receives the current sanitation data and the corresponding identification information sent by the current subsystem, before the step of extracting the fields and values ​​in the current sanitation data by the cloud platform, the method further includes: When the cloud platform receives the information sending request sent by the current subsystem, the cloud platform detects the current processing load capacity; If the current processing load capacity is not lower than the processing load threshold, the cloud platform receives the current sanitation data and corresponding identification information sent by the current subsystem; If the current processing load capacity is lower than the processing load threshold, the cloud platform replies a waiting notification to the current subsystem, and the waiting notification is used to notify the current subsystem to save the current sanitation data into its own database; The cloud platform sends a data update notification to other subsystems, where the other subsystems are subsystems other than the current subsystem; Upon receiving an information acquisition request sent by a response subsystem, the cloud platform obtains the storage address of the current sanitation data from the current sanitation data; the response subsystem is the subsystem requesting to obtain the current sanitation data; The cloud platform sends the storage address to the response subsystem, so that the response subsystem extracts the current sanitation data from the database of the current subsystem according to the storage address.

3. The collaborative method of the sanitation big data cloud platform according to claim 2 is characterized in that: After the step of the cloud platform sending the storage address to the response subsystem so that the response subsystem extracts the current sanitation data from the database of the current subsystem according to the storage address, the method further includes: When the current processing load capacity changes from lower than the processing load threshold to not lower than the processing load threshold, the cloud platform sends instructions to the current subsystem and the response subsystem, causing the current subsystem and the response subsystem to send the current sanitation data; if any subsystem responds to the sending request, the cloud platform will reject the sending request of the other subsystem.

4. The sanitation big data cloud platform collaboration method according to claim 1 is characterized in that: The step of extracting fields and values ​​from the current sanitation data by the cloud platform specifically includes: The cloud platform obtains the average data access time of the same type of historical data according to the type of the current sanitation data: If the average value of the data access time is less than the preset access time threshold, the current sanitation data is determined to be real-time processing data; If the average value of the data access time is not less than the preset access time threshold, determining that the current sanitation data is batch processing data; For the real-time processed data, the cloud platform extracts fields and values ​​in the current sanitation data; For the batch data, the cloud platform waits for the same batch of data to reach a preset upper limit or a preset time threshold, and then extracts the fields and values ​​in the current sanitation data for the same batch of current sanitation data.

5. The sanitation big data cloud platform collaboration method according to claim 4 is characterized in that: The step of the cloud platform saving the converted current sanitation data to the central database specifically includes: For the batch data, the cloud platform saves the converted current sanitation data to the low-speed storage medium of the central database; For the real-time processing data, the cloud platform saves the converted current sanitation data to the high-speed storage medium of the central database, and the reading speed of the high-speed storage medium is greater than that of the low-speed storage medium.

6. The collaborative method of the sanitation big data cloud platform according to claim 1, characterized in that: After the cloud platform saves the converted current sanitation data to the central database and determines the index of the converted current sanitation data according to the identification information, the method further includes: The cloud platform determines whether the value after unit conversion exceeds the maximum length specified by the central database; If it exceeds the maximum length specified by the central database, the cloud platform performs unit conversion on the converted value so that it is less than the maximum length specified by the central database; The cloud platform adds an identifier, and the identifier is used to indicate that the unit of the numerical value after unit conversion is inconsistent with the unit of other numerical values ​​when the data is displayed.

7. The sanitation big data cloud platform collaboration method according to claim 1 is characterized in that: After the cloud platform saves the converted current sanitation data to the central database and determines the index of the converted current sanitation data according to the identification information, the method further includes: The cloud platform notifies all subscribers of the current subsystem, so that the subscribers know that the data of the subscribed current subsystem has been updated, and the subscribers are other subsystems that have subscribed to the current subsystem.

8. A sanitation big data cloud platform collaborative system, characterized in that: include: one or more processors and memory; The memory is coupled to the one or more processors, and the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the sanitation big data cloud platform collaborative system to execute the method described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instruction is executed on the collaborative system of the environmental sanitation big data cloud platform, the collaborative system of the environmental sanitation big data cloud platform executes the method as described in any one of claims 1-7.

10. A computer program product comprising instructions, characterized in that When the computer program product runs on the environmental sanitation big data cloud platform collaborative system, the environmental sanitation big data cloud platform collaborative system executes the method as described in any one of claims 1-7.

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