Control method, device, equipment and storage medium for distributed processing of restaurant kitchen waste

Abstract

The present application provides a control method, device, equipment and storage medium for the distributed processing of food waste, wherein the method comprises: obtaining the first operating data of at least one food waste processing device at the current moment; predicting whether the target device is abnormal based on the historical operating data of the target device within a preset time period and the first operating data corresponding to the target device, and obtaining a prediction result, wherein the target device is any one of the at least one food waste processing device; and performing a fault prediction alarm when the prediction result shows that an abnormality exists. The present application realizes the distributed processing of food waste, which can significantly reduce transportation costs, improve resource utilization efficiency, and reduce environmental pollution and pressure, and performs data processing and prediction based on operating data, which can uniformly supervise and standardize the food waste processing equipment, improve data processing effect and data utilization, and improve food waste processing efficiency and resource utilization.

Description

Technical Field

[0001] The present application relates to the field of smart environmental protection technology, and in particular to a control method, device, equipment and storage medium for distributed processing of food waste. Background Art

[0002] The traditional method of kitchen waste treatment requires that the waste be transported from scattered production points to centralized treatment points, which involves a large number of transportation vehicles and manpower, resulting in energy waste and traffic congestion. At the same time, due to the long-distance garbage transportation, the large amount of kitchen waste accumulation will cause environmental pollution, and improper treatment will also cause secondary pollution. In addition, centralized treatment points require large-scale investment and construction, as well as maintenance and operation, which also increases the investment of funds. Moreover, the existing centralized kitchen waste treatment mainly adopts a manual management mode, and the level of equipment networking and intelligence is low. Although there are some intelligent management solutions for kitchen waste equipment on the market, such as using sensors and Internet of Things technology to remotely monitor and manage equipment. However, these solutions are usually based on the automatic control system of the kitchen waste treatment center, which can only monitor some data of the treatment center, and most of these data are only monitored as the operating status of the equipment, but there is no in-depth analysis and processing of these data, and it is impossible to predict faults and optimize equipment based on the processing data of kitchen waste. Therefore, how to realize the on-site treatment and recycling of kitchen waste, as well as the intelligent adjustment of garbage treatment equipment, has become a research direction in this field. Summary of the invention

[0003] The technical purpose to be achieved by the embodiments of the present application is to provide a control method, device, equipment and storage medium for distributed processing of food waste, so as to solve the problem that the current centralized food waste processing method requires long-distance waste transportation and has low data utilization.

[0004] In order to solve the above technical problems, the embodiment of the present application provides a control method for distributed processing of food waste, including:

[0005] Acquire first operation data of at least one food waste treatment device at a current moment;

[0006] Predicting whether the target device is abnormal according to historical operation data of the target device within a preset time period and the first operation data corresponding to the target device, and obtaining a prediction result, wherein the target device is any one of the at least one food waste treatment device;

[0007] When the prediction result shows that there is an abnormality, a fault prediction alarm is issued.

[0008] Specifically, in the method described above, the first operating data and the historical operating data include: at least one of temperature, humidity, oxygen concentration, pH, garbage filling degree, ammonia concentration and hydrogen sulfide concentration.

[0009] Specifically, in the method described above, predicting whether the target device is abnormal based on the historical operation data of the target device within a preset time period and the first operation data corresponding to the target device to obtain the prediction result includes:

[0010] Respectively obtaining a first Mahalanobis distance corresponding to the operation data at each moment in the historical operation data and a second Mahalanobis distance corresponding to the first operation data;

[0011] determining a prediction threshold according to an average value of the first Mahalanobis distance and an operation mode of the target device;

[0012] When the second Mahalanobis distance is greater than the prediction threshold, it is determined that the prediction result is abnormal.

[0013] Preferably, the method as described above, obtaining the first Mahalanobis distance corresponding to the operation data at each moment in the historical operation data, comprises:

[0014] Performing normalization preprocessing on the historical operation data to obtain preprocessed transition data;

[0015] Performing principal component analysis on the transition data to obtain a principal component eigenvector;

[0016] The first Mahalanobis distance corresponding to the operating data at each moment in the historical data is obtained according to the principal component eigenvector.

[0017] Preferably, in the method as described above, after obtaining the first operating data of at least one food waste treatment device at the current moment, the method further comprises:

[0018] Determine at least one alarm policy according to the device type and / or policy group information of the target device, wherein each of the alarm policies includes at least one monitoring parameter and at least one alarm parameter range corresponding thereto;

[0019] When any of the alarm strategies is triggered, a real-time fault alarm is performed according to the alarm parameter range and / or the number of target monitoring parameters when the alarm strategy is triggered, wherein the target monitoring parameter is the monitoring parameter within the corresponding alarm parameter range.

[0020] Specifically, in the method as described above, the alarm strategy includes at least one of the following:

[0021] In the case where there is only one monitoring parameter, determining whether the alarm strategy is triggered according to whether the monitoring parameter is within the alarm parameter range;

[0022] In the case where the monitoring parameters include at least two, determining whether the alarm strategy is triggered according to whether any one of the monitoring parameters is within the corresponding alarm parameter range;

[0023] In the case where the monitoring parameters include at least two, whether the alarm strategy is triggered is determined according to whether the at least two monitoring parameters are both within the corresponding alarm parameter range.

[0024] Furthermore, in the above method, the real-time fault alarm is performed according to the alarm parameter range and / or the number of target monitoring parameters when the alarm strategy is triggered, including:

[0025] Determining an alarm level according to the alarm parameter range and / or the number of target monitoring parameters when the alarm strategy is triggered;

[0026] Perform real-time fault alarm according to the alarm strategy and alarm level;

[0027] Wherein, the alarm level is determined according to at least one of the following:

[0028] Determining the alarm level according to the highest level in the alarm parameter range where the target monitoring parameter is located when the alarm strategy is triggered;

[0029] Determine the alarm level according to the number of the target monitoring parameters when the alarm strategy is triggered, wherein the greater the number of the target monitoring parameters, the higher the alarm level;

[0030] The alarm level is determined according to the number of the target monitoring parameters and the level of each target monitoring parameter in the alarm parameter range.

[0031] Preferably, the method as described above further comprises:

[0032] If the target device satisfies a first condition, a maintenance work order is generated, wherein the first condition is satisfied including: at least one of: detecting a work order input, reaching a preconfigured maintenance time point, detecting an alarm, or detecting that at least one preset parameter reaches a threshold;

[0033] Distributing the maintenance work order to at least one maintenance terminal;

[0034] Receive the maintenance report uploaded by the maintenance terminal, and store and / or display it.

[0035] Preferably, the method as described above further comprises:

[0036] According to the pre-created component library, the first operating data pair is visualized in three dimensions.

[0037] Another embodiment of the present application further provides a control device for distributed processing of food waste, comprising:

[0038] A first processing module, used for obtaining first operation data of at least one food waste treatment device at a current moment;

[0039] A second processing module, configured to predict whether the target device is abnormal according to historical operation data of the target device within a preset time period and the first operation data corresponding to the target device, and obtain a prediction result, wherein the target device is any one of the at least one food waste treatment device;

[0040] The third processing module is used to alarm when the prediction result is abnormal.

[0041] Another embodiment of the present application further provides an electronic device for distributed processing of food waste, wherein the electronic device is connected to at least one food waste processing device via Internet of Things communication, wherein the electronic device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, and when the computer program is executed by the processor, the steps of the control method for distributed processing of food waste as described above are implemented.

[0042] Yet another embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the computer program implements the steps of the control method for distributed processing of food waste as described above.

[0043] Compared with the prior art, the control method, device, equipment and storage medium for distributed processing of food waste provided in the embodiments of the present application have at least the following beneficial effects:

[0044] This application realizes the distributed treatment of food waste through food waste treatment equipment, which can significantly reduce transportation costs, improve resource utilization efficiency, bring cost-effectiveness to the overall treatment process, and reduce environmental pollution and pressure, which is beneficial to environmental protection. And based on the current operating data and historical operating data, data processing and prediction can be carried out to uniformly supervise and standardize the food waste treatment equipment, and timely adjust possible abnormal equipment parameters and the entire operation plan, improve data processing effect and data utilization, and improve the food waste treatment efficiency, resource utilization and environmental protection effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is one of the flow charts of the control method for distributed processing of food waste of the present application;

[0046] Figure 2 This is the second flow chart of the control method for distributed processing of food waste of the present application;

[0047] Figure 3 This is the third flow chart of the control method for distributed processing of food waste of the present application;

[0048] Figure 4 This is a fourth flow chart of the control method for distributed processing of food waste of the present application;

[0049] Figure 5 This is the fifth flow chart of the control method for distributed processing of food waste of the present application;

[0050] Figure 6 This is the sixth flow chart of the control method for distributed processing of food waste of the present application;

[0051] Figure 7 This is a schematic diagram of the structure of the control device for distributed processing of food waste in the present application. DETAILED DESCRIPTION

[0052] In order to make the technical problems, technical solutions and advantages to be solved by the application clearer, the following will be described in detail in conjunction with the accompanying drawings and specific embodiments. In the following description, specific details such as specific configurations and components are provided only to help fully understand the embodiments of the application. Therefore, it should be clear to those skilled in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the application. In addition, for clarity and brevity, the description of known functions and structures has been omitted.

[0053] It should be understood that the references to "one embodiment" or "an embodiment" throughout the specification mean that the specific features, structures, or characteristics associated with the embodiment are included in at least one embodiment of the present application. Therefore, the references to "in one embodiment" or "in an embodiment" appearing throughout the specification do not necessarily refer to the same embodiment. In addition, these specific features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0054] In the various embodiments of the present application, it should be understood that the size of the serial numbers of the following processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0055] It should be understood that the term "and / or" in this article is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0056] In the embodiments provided in the present application, it should be understood that "B corresponding to A" means that B is associated with A, and B can be determined according to A. However, it should also be understood that determining B according to A does not mean determining B only according to A, and B can also be determined according to A and / or other information.

[0057] See also Figure 1 A preferred embodiment of the present application provides a control method for distributed processing of food waste, comprising:

[0058] Step S101, obtaining first operation data of at least one food waste treatment device at the current moment;

[0059] Step S102, predicting whether the target device is abnormal according to the historical operation data of the target device within a preset time period and the first operation data corresponding to the target device, and obtaining a prediction result, wherein the target device is any one of the at least one food waste treatment device;

[0060] Step S103: When the prediction result shows abnormality, an alarm is issued.

[0061] In this embodiment, a parameter garbage treatment device is used to treat food waste, so that the parameter garbage treatment device can be set near the source of food waste generation to achieve distributed treatment of food waste, thereby reducing or even avoiding the transportation of food waste, and further reducing the secondary pollution of food waste, thereby achieving specific treatment of food waste and recycling of organic fertilizer.

[0062] During operation, the control device will obtain the first operation data of at least one food waste treatment device at the current moment, wherein the specific acquisition method includes but is not limited to data transmission through various methods such as industrial Internet of Things, mobile network or wired network. In a specific embodiment, based on convenience, practicality, etc., the Internet of Things is used to transmit data. Specifically, multiple sensors are set in the food waste treatment device to collect the operation data of the food waste treatment device during operation, and the data is transmitted to the control device through the Internet of Things protocol, preferably the data access platform of the control device, so as to ensure the real-time and reliability of the operation data.

[0063] After the operation data is acquired, it will be stored and processed. During the processing, it is preferred to make a prediction based on the historical data of the corresponding target device within a preset time period (such as a week or a month, etc.) and the first operation data at the current moment to predict whether the target device has a trend toward abnormality. If the predicted result is that there is an abnormality, it is determined that the target device will be abnormal in the subsequent period of time. At this time, in order to avoid abnormal operation of the equipment, an alarm will be issued in advance so that the operating parameters of the target device can be adjusted by the system self-adjustment or maintenance personnel to ensure the efficient and normal operation of the target device and improve the utilization of data and resources. Among them, when the system is adjusted by self-adjustment, the control device will determine the optimal parameter ratio based on the historical data of other devices of the same type as the target device, through model construction methods such as training, and determine the adjustment strategy based on the parameters of the current target device and the correlation between the parameters, and make adjustments according to the adjustment strategy.

[0064] In summary, the present application realizes the distributed treatment of food waste through food waste treatment equipment, which can significantly reduce transportation costs, improve resource utilization efficiency, bring cost-effectiveness to the overall treatment process, and reduce environmental pollution and pressure, which is beneficial to environmental protection. And based on the current operating data and historical operating data for data processing and prediction, it is possible to uniformly supervise and standardize the food waste treatment equipment, and timely adjust possible abnormal equipment parameters and the entire operation plan, improve data processing effect and data utilization, and improve the food waste treatment efficiency, resource utilization and environmental protection effect.

[0065] It should be noted that the food waste treatment equipment is a device for ecological treatment of food waste, including but not limited to biodegradable self-circulating treatment equipment.

[0066] It should be noted that the above and below alarm methods include but are not limited to at least one of sending an alarm message, automatically stopping the equipment, and notifying maintenance personnel.

[0067] Specifically, in the method described above, the first operating data and the historical operating data include: at least one of temperature, humidity, oxygen concentration, pH, garbage filling degree, ammonia concentration and hydrogen sulfide concentration.

[0068] In this embodiment, the parameters included in the operating data (including the current moment, the first operating data and the historical operating data) are exemplified, which may include: parameters related to the equipment operating environment such as temperature, humidity, oxygen concentration and pH, and cluster parameters related to the equipment operating status or operating level such as garbage filling degree, ammonia concentration and hydrogen sulfide concentration.

[0069] It should be noted that the above parameters are only examples. Those skilled in the art may increase, decrease or replace the parameters according to the equipment type or other conditions, restrictions or requirements, for example, replacing the ammonia concentration with the ammonia production, etc., which all fall within the scope of protection of this application.

[0070] See also Figure 2 Specifically, the method as described above, predicting whether the target device is abnormal based on the historical operation data of the target device within a preset time period and the first operation data corresponding to the target device, and obtaining the prediction result includes:

[0071] Step S201, respectively obtaining a first Mahalanobis distance corresponding to the operation data at each moment in the historical operation data and a second Mahalanobis distance corresponding to the first operation data;

[0072] Step S202, determining a prediction threshold according to an average value of the first Mahalanobis distance and an operation mode of the target device;

[0073] Step S203: when the second Mahalanobis distance is greater than the prediction threshold, determining that the prediction result is abnormal.

[0074] In this embodiment, an example of how to make a prediction is given, wherein a first Mahalanobis distance corresponding to the operating data at each moment in the historical data and a second Mahalanobis distance corresponding to the first operating data are obtained. Since the Mahalanobis distance is not affected by the dimension and reduces the influence of the correlation between variables, the accuracy of subsequent predictions and judgments based on the Mahalanobis distance can be guaranteed.

[0075] Furthermore, when making a prediction, a prediction threshold is determined based on the average value of the first Mahalanobis distance and the operating mode of the target device. In a specific embodiment, if the operating mode is an extreme operating mode, the prediction threshold is set to the first multiple of the average value of the first Mahalanobis distance; if the operating mode is not an extreme operating mode, the prediction threshold is set to the second multiple of the average value of the first Mahalanobis distance, wherein the first multiple is greater than the second multiple. In a specific embodiment, the first multiple is set to 3 times, and the second multiple is set to 2 times. It should be noted that the determined prediction threshold is the limit value of the Mahalanobis distance corresponding to the operating data when the target device is working normally. If the second Mahalanobis distance is greater than the prediction threshold, it can be determined that the target device may be abnormal. At this time, through an alarm, the system or maintenance personnel can be reminded in time to adjust the parameters of the target device in time to ensure the subsequent normal operation of the device.

[0076] It should be noted that the parameters of the target device are parameters used to operate the target device, including current, voltage, etc.

[0077] See also Figure 3Preferably, the method as described above, obtaining the first Mahalanobis distance corresponding to the operation data at each moment in the historical operation data, comprises:

[0078] Step S301, performing normalization preprocessing according to the historical operation data to obtain preprocessed transition data;

[0079] Step S302, performing principal component analysis on the transition data to obtain a principal component feature vector;

[0080] Step S303: acquiring the first Mahalanobis distance corresponding to the operation data at each moment in the historical data according to the principal component eigenvector.

[0081] In this embodiment, the step of obtaining the first Mahalanobis distance corresponding to the operation data at each moment in the historical operation data is exemplified, wherein the historical operation data is normalized to effectively avoid the influence of accidental errors such as noise on the accuracy of the data, and then the amount of data during data processing can be reduced by principal component analysis, thereby facilitating the improvement of data processing efficiency and ensuring the timeliness of the data. Further, based on the principal component eigenvector, the first Mahalanobis distance corresponding to the operation data at each moment in the historical data can be obtained, wherein, since the calculation method of the Mahalanobis distance is well known, it will not be described in detail here.

[0082] See also Figure 4 Preferably, according to the method described above, after obtaining the first operating data of at least one food waste treatment device at the current moment, the method further comprises:

[0083] Step S401, determining at least one alarm policy according to the device type and / or policy group information of the target device, wherein each of the alarm policies includes at least one monitoring parameter and at least one alarm parameter range corresponding thereto;

[0084] Step S402, when any one of the alarm strategies is triggered, a real-time fault alarm is performed according to the alarm parameter range and / or the number of target monitoring parameters when the alarm strategy is triggered, wherein the target monitoring parameter is the monitoring parameter within the corresponding alarm parameter range.

[0085] In this embodiment, when the device is registered, the device will be strategically grouped based on the device type or the environment in which the device is located, so that the alarm strategies between devices of different device types or in different strategy groups are different. Therefore, after obtaining the first operating data of the food waste treatment device, in addition to predicting the device based on the operating data, at least one alarm strategy will be determined based on the device type and / or policy group information of the target device, and the target device will be monitored in real time based on the alarm strategy and operating data to determine whether an alarm is triggered, as well as the specific alarm method and content when an alarm is triggered.

[0086] Specifically, each alarm strategy includes at least one monitoring parameter and at least one corresponding alarm parameter range. When any alarm strategy is triggered, a real-time fault alarm will be issued according to the alarm parameter range and / or the number of target monitoring parameters when the alarm strategy is triggered, so that the system or maintenance personnel can determine the relevant parameters that need to be adjusted according to the alarm information. The target monitoring parameter is a monitoring parameter whose value in the first operating data is within a corresponding alarm parameter range.

[0087] Specifically, in the method as described above, the alarm strategy includes at least one of the following:

[0088] In the case where there is only one monitoring parameter, determining whether the alarm strategy is triggered according to whether the monitoring parameter is within the alarm parameter range;

[0089] In the case where the monitoring parameters include at least two, determining whether the alarm strategy is triggered according to whether any one of the monitoring parameters is within the corresponding alarm parameter range;

[0090] In the case where the monitoring parameters include at least two, whether the alarm strategy is triggered is determined according to whether the at least two monitoring parameters are both within the corresponding alarm parameter range.

[0091] In this embodiment, based on the different monitoring parameters corresponding to the alarm strategy, an example is given of how to determine whether the alarm strategy is triggered. In the case of only one monitoring parameter, it is only necessary to determine whether the monitoring parameter is within the alarm parameter range. When it is within the alarm parameter range, it can be determined that the alarm strategy is triggered. For example: in a specific embodiment, the monitoring parameter corresponding to the alarm strategy is temperature, and the alarm parameter range is temperature above 65 degrees Celsius or temperature below 30 degrees Celsius. At this time, if the temperature in the operating data is higher than 65 degrees Celsius (for example, 70 degrees Celsius) or the temperature is lower than 30 degrees Celsius (for example, 20 degrees Celsius), it can be determined whether the alarm strategy is triggered. In the case where the monitoring parameters include at least two, in one embodiment, it can be determined whether the alarm strategy is triggered based on whether any one of the monitoring parameters is within the corresponding alarm parameter range. For example, the monitoring parameters corresponding to the alarm strategy are temperature and humidity. When at least one of the temperature and humidity in the operating data is within the corresponding alarm parameter range, it can be determined whether the alarm strategy is triggered. In another embodiment, it is also possible to determine whether the alarm strategy is triggered based on whether all monitoring parameters are within the corresponding alarm parameter range. For example, the monitoring parameters corresponding to the alarm strategy are temperature and humidity. When the temperature and humidity in the operating data are within the corresponding alarm parameter range, it is possible to determine whether the alarm strategy is triggered. In yet another embodiment, it is also possible to determine whether the alarm strategy is triggered based on whether a preset number of monitoring parameters are within the corresponding alarm parameter range. For example, the monitoring parameters corresponding to the alarm strategy are temperature, humidity, and oxygen concentration. When any two of the temperature, humidity, and oxygen concentration in the operating data are within the corresponding alarm parameter range, it is possible to determine whether the alarm strategy is triggered.

[0092] See also Figure 5 Further, in the method as described above, the real-time fault alarm is performed according to the alarm parameter range and / or the number of target monitoring parameters when the alarm strategy is triggered, including:

[0093] Step S501, determining an alarm level according to the alarm parameter range and / or the number of target monitoring parameters when the alarm strategy is triggered;

[0094] Step S502, performing real-time fault alarm according to the alarm strategy and alarm level;

[0095] Wherein, the alarm level is determined according to at least one of the following:

[0096] Determining the alarm level according to the highest level in the alarm parameter range where the target monitoring parameter is located when the alarm strategy is triggered;

[0097] Determine the alarm level according to the number of the target monitoring parameters when the alarm strategy is triggered, wherein the greater the number of the target monitoring parameters, the higher the alarm level;

[0098] The alarm level is determined according to the number of the target monitoring parameters and the level of each target monitoring parameter in the alarm parameter range.

[0099] In this embodiment, the alarm steps after the alarm strategy is triggered are implemented, wherein the alarm can be divided into multiple alarm levels (for example, slight, medium, emergency, etc.) according to the degree of impact on the equipment. After determining that the alarm strategy is triggered, the alarm level can be determined based on the alarm parameter range and / or the number of target monitoring parameters when the alarm strategy is triggered, and then real-time fault alarm is performed based on the alarm level, so that the system or maintenance personnel can reasonably arrange the alarm processing sequence based on the alarm level.

[0100] In a specific embodiment, the alarm parameter range can be associated with the alarm level. When determining the alarm level, the alarm level can be determined according to the highest level in the alarm parameter range where the target monitoring parameter is located. For example, when the monitoring parameter corresponding to the alarm strategy is temperature, and the alarm parameter ranges are respectively temperature above 65 degrees Celsius, temperature above 70 degrees Celsius, and temperature above 75 degrees Celsius, the corresponding alarm levels are slight, moderate, and urgent. When the temperature in the operating data is 72 degrees Celsius, the corresponding alarm parameter range is temperature above 70 degrees Celsius, and the corresponding alarm level is moderate, and a moderate alarm is subsequently performed. Optionally, when there are multiple target monitoring parameters, the final alarm level can be determined by weighted averaging of the alarm levels corresponding to the target monitoring parameters.

[0101] In a specific embodiment, the number of target monitoring parameters can be associated with the alarm level. When determining the alarm level, the alarm level can be determined based on the number of target monitoring parameters. For example, when the total number of monitoring parameters corresponding to the alarm strategy is 3, and from 1 to 3, the corresponding alarm levels are mild, moderate and urgent, then when the number of target monitoring parameters is 2, the corresponding alarm level is moderate, and a moderate alarm is performed subsequently.

[0102] In another specific embodiment, the alarm level can be associated with the alarm parameter range and the number of target monitoring parameters. When determining the alarm level, the corresponding pre-selected alarm level can be determined according to the alarm parameter range and the target monitoring parameter, and then the alarm level can be determined by weighted average calculation or size comparison.

[0103] See also Figure 6 Preferably, the method as described above further comprises:

[0104] Step S601, generating a maintenance work order when the target device meets a first condition, wherein the first condition is met including: detecting at least one of a work order input, reaching a preconfigured maintenance time point, detecting an alarm, or detecting that at least one preset parameter reaches a threshold;

[0105] Step S602, distributing the maintenance work order to at least one maintenance terminal;

[0106] Step S603, receiving the maintenance report uploaded by the maintenance terminal, and storing and / or displaying it.

[0107] In another embodiment of the present application, work orders will be distributed when the first condition is met so that maintenance personnel can inspect and maintain the equipment in a timely manner to ensure the normal operation of the equipment. When distributing work orders, appropriate maintenance personnel and quantity will be determined based on factors such as the technical difficulty, maintenance items, and equipment location in the maintenance work order, and the maintenance work order will be distributed to the maintenance terminal of the determined maintenance personnel. After receiving the maintenance work order through the maintenance terminal, the maintenance personnel can prepare accessories, perform tasks, and other maintenance work according to the content of the maintenance work order, and upload the maintenance report through the maintenance terminal after completing the maintenance work. After receiving the maintenance report uploaded by the maintenance terminal, it will be stored, such as generating a maintenance record, and displayed when needed, so that managers and users can view the maintenance records and understand the equipment maintenance situation.

[0108] Specifically, in this embodiment, examples are also given of situations where the first condition is met, such as: detecting at least one of the following: work order input, reaching a preconfigured maintenance time point, detecting an alarm, or detecting at least one preset parameter reaching a threshold. Among them, work order distribution after work order input is detected is conducive to timely notification of maintenance personnel for maintenance when sudden failures or events occur; work order distribution after reaching a preconfigured maintenance time point is conducive to preventive maintenance of equipment according to a preset cycle or preset time point; work order distribution after an alarm is detected or at least one preset parameter reaches a threshold is conducive to automatic maintenance in the event of equipment failure or abnormality of a certain parameter or change in the life cycle status of the equipment.

[0109] In summary, by distributing maintenance work orders when the first condition is met, the occurrence rate of sudden failures can be effectively reduced, the overall stability of the equipment can be improved, and the depreciation cost of the equipment can be reduced.

[0110] Preferably, the method as described above further comprises:

[0111] The first operating data is displayed in three dimensions according to the pre-created component library.

[0112] In this embodiment, in order to facilitate the management of the food waste treatment equipment, after obtaining the first operating data, the first operating data will be displayed in three dimensions based on a pre-created component library, so that the staff can intuitively determine whether the equipment is abnormal based on the display content.

[0113] It should be noted that in order to achieve three-dimensional visualization, when the equipment registration information of the food waste treatment equipment is received, the equipment will be configured with a three-dimensional model, gateway configuration, three-dimensional configuration design, and three-dimensional remote operation and maintenance.

[0114] Specifically, the 3D model configuration includes:

[0115] Create a component library, that is, upload components for making 3D scenes. Maintain the component library and configure any scene in the components.

[0116] Creating a physical model means creating the point table information of the equipment, which needs to be input according to the actual point table content of the equipment to ensure that the data can be obtained in a timely and accurate manner.

[0117] Create a calculation model, which supports adding certain operation rules to the original point table information to generate information data. By setting calculation models for different components, it is convenient to monitor the key information of each component during configuration.

[0118] The gateway configuration includes:

[0119] Create a gateway, that is, through user-defined gateways or automatically assigned gateways, you can manage gateway devices in the gateway list to enable device data to be uploaded to the cloud.

[0120] Gateway activation is to start and configure the gateway device to ensure normal communication between it and the IoT devices.

[0121] Gateway management means setting the gateway's monitoring and management parameters, hardware version number, software version number, and updating the gateway.

[0122] Three-dimensional configuration design includes:

[0123] Low-code configuration design uses a visual interface to drag various low-code components, such as fans, pH testers, etc., in a three-dimensional model to build a user-friendly operation interface. At the same time, it can quickly customize and modify system functions to improve development efficiency and adaptability.

[0124] Equipment connection and configuration, that is, connecting the selected components with the equipment and parts in the physical model to achieve real-time data display and equipment control.

[0125] Three-dimensional remote operation and maintenance includes:

[0126] Scene switching, that is, through the three-dimensional visualization interface, the working status of different equipment and components can be viewed in real time to quickly identify problems.

[0127] Adjust parameters in real time, that is, remotely adjust equipment parameters based on real-time monitoring data to optimize the processing process and improve efficiency.

[0128] Panoramic monitoring and fault warning, that is, using the panoramic monitoring function to comprehensively monitor the processing equipment and environment, and setting up a fault warning system to detect and deal with potential problems in advance.

[0129] Remote control and command debugging, that is, remote device control through a visual interface, sending commands to enable the device to perform specific tasks, and debugging to ensure the normal operation of the device.

[0130] See also Figure 7 Another embodiment of the present application further provides a control device for distributed processing of food waste, comprising:

[0131] The first processing module 701 is used to obtain first operation data of at least one food waste treatment device at a current moment;

[0132] A second processing module 702 is used to predict whether the target device is abnormal according to the historical operation data of the target device within a preset time period and the first operation data corresponding to the target device, and obtain a prediction result, wherein the target device is any one of the at least one food waste treatment device;

[0133] The third processing module 703 is used to issue a fault prediction alarm when the prediction result shows that an abnormality exists.

[0134] Specifically, in the device as described above, the first operating data and the historical operating data include: at least one of temperature, humidity, oxygen concentration, pH, garbage filling degree, ammonia concentration and hydrogen sulfide concentration.

[0135] Specifically, in the above-mentioned device, the second processing module includes:

[0136] A first processing unit, configured to respectively obtain a first Mahalanobis distance corresponding to the operation data at each moment in the historical operation data and a second Mahalanobis distance corresponding to the first operation data;

[0137] a second processing unit, configured to determine a prediction threshold according to an average value of the first Mahalanobis distance and an operation mode of the target device;

[0138] The third processing unit is configured to determine that the prediction result is abnormal when the second Mahalanobis distance is greater than the prediction threshold.

[0139] Preferably, in the above device, the first processing unit comprises:

[0140] A first sub-processing unit, configured to perform normalization preprocessing according to the historical operation data to obtain preprocessed transition data;

[0141] A second sub-processing unit is used to perform principal component analysis on the transition data to obtain a principal component feature vector;

[0142] The third sub-processing unit is used to obtain the first Mahalanobis distance corresponding to the operating data at each moment in the historical data according to the principal component eigenvector.

[0143] Preferably, the device as described above further comprises:

[0144] A fourth processing module, configured to determine at least one alarm policy according to the device type and / or policy group information of the target device, wherein each of the alarm policies includes at least one monitoring parameter and at least one alarm parameter range corresponding thereto;

[0145] The fifth processing module is used to perform real-time fault alarm when any one of the alarm strategies is triggered according to the alarm parameter range and / or the number of target monitoring parameters when the alarm strategy is triggered, wherein the target monitoring parameter is the monitoring parameter within the corresponding alarm parameter range.

[0146] Specifically, in the device as described above, the alarm strategy includes at least one of the following:

[0147] In the case where there is only one monitoring parameter, determining whether the alarm strategy is triggered according to whether the monitoring parameter is within the alarm parameter range;

[0148] In the case where the monitoring parameters include at least two, determining whether the alarm strategy is triggered according to whether any one of the monitoring parameters is within the corresponding alarm parameter range;

[0149] In the case where the monitoring parameters include at least two, whether the alarm strategy is triggered is determined according to whether the at least two monitoring parameters are both within the corresponding alarm parameter range.

[0150] Further, in the above device, the fifth processing module includes:

[0151] a fourth processing unit, configured to determine an alarm level according to the alarm parameter range and / or the number of the target monitoring parameters when the alarm strategy is triggered;

[0152] A fifth processing unit, configured to perform real-time fault alarm according to the alarm strategy and alarm level;

[0153] Wherein, the alarm level is determined according to at least one of the following:

[0154] Determining the alarm level according to the highest level in the alarm parameter range where the target monitoring parameter is located when the alarm strategy is triggered;

[0155] Determine the alarm level according to the number of the target monitoring parameters when the alarm strategy is triggered, wherein the greater the number of the target monitoring parameters, the higher the alarm level;

[0156] The alarm level is determined according to the number of the target monitoring parameters and the level of each target monitoring parameter in the alarm parameter range.

[0157] Preferably, the device as described above further comprises:

[0158] A sixth processing module, configured to generate a maintenance work order when the target device satisfies a first condition, wherein the first condition is satisfied including: detecting at least one of a work order input, reaching a preconfigured maintenance time point, detecting an alarm, or detecting that at least one preset parameter reaches a threshold;

[0159] A seventh processing module, used for distributing the maintenance work order to at least one maintenance terminal;

[0160] The eighth processing module is used to receive the maintenance report uploaded by the maintenance terminal, and store and / or display it.

[0161] Preferably, the device as described above further comprises:

[0162] A ninth processing module is used to perform a three-dimensional visual display of the first operation data pair according to a pre-created component library.

[0163] The control device for distributed processing of food waste of the present application is a device corresponding to the embodiment of the control method for distributed processing of food waste mentioned above. All implementation means in the embodiment of the above method are applicable to the embodiment of the device and can achieve the same technical effect.

[0164] Another embodiment of the present application further provides an electronic device for distributed processing of food waste, wherein the electronic device is connected to at least one food waste processing device via Internet of Things communication, wherein the electronic device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, and when the computer program is executed by the processor, the steps of the control method for distributed processing of food waste as described above are implemented.

[0165] Yet another embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the computer program implements the steps of the control method for distributed processing of food waste as described above.

[0166] In addition, the present application may repeat reference numerals and / or letters in different examples. This repetition is for the purpose of simplicity and clarity, and does not in itself indicate the relationship between the various embodiments and / or settings discussed.

[0167] It should also be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusions.

[0168] The above is a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles described in the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A control method for distributed processing of kitchen waste, characterized in that: include: Acquire first operation data of at least one food waste treatment device at a current moment; Predicting whether the target device is abnormal according to historical operation data of the target device within a preset time period and the first operation data corresponding to the target device, and obtaining a prediction result, wherein the target device is any one of the at least one food waste treatment device; When the prediction result shows that there is an abnormality, a fault prediction alarm is issued; The first operation data and the historical operation data include: at least one of temperature, humidity, oxygen concentration, pH, garbage filling degree, ammonia concentration and hydrogen sulfide concentration; The step of predicting whether the target device is abnormal based on the historical operation data of the target device within a preset time period and the first operation data corresponding to the target device to obtain a prediction result includes: Respectively obtaining a first Mahalanobis distance corresponding to the operation data at each moment in the historical operation data and a second Mahalanobis distance corresponding to the first operation data; determining a prediction threshold according to an average value of the first Mahalanobis distance and an operation mode of the target device; When the second Mahalanobis distance is greater than the prediction threshold, determining that the prediction result is abnormal; Obtaining the first Mahalanobis distance corresponding to the operation data at each moment in the historical operation data includes: Performing normalization preprocessing on the historical operation data to obtain preprocessed transition data; Performing principal component analysis on the transition data to obtain a principal component eigenvector; According to the principal component eigenvector, obtaining the first Mahalanobis distance corresponding to the operation data at each moment in the historical operation data; After obtaining the first operation data of at least one food waste treatment device at the current moment, the method further includes: Determine at least one alarm policy according to the device type and / or policy group information of the target device, wherein each of the alarm policies includes at least one monitoring parameter and at least one alarm parameter range corresponding thereto; When any of the alarm strategies is triggered, a real-time fault alarm is performed according to the alarm parameter range and / or the number of target monitoring parameters when the alarm strategy is triggered, wherein the target monitoring parameter is the monitoring parameter within the corresponding alarm parameter range.

2. The method according to claim 1, characterized in that: The alarm strategy includes at least one of the following: In the case where there is only one monitoring parameter, determining whether the alarm strategy is triggered according to whether the monitoring parameter is within the alarm parameter range; In the case where the monitoring parameters include at least two, determining whether the alarm strategy is triggered according to whether any one of the monitoring parameters is within the corresponding alarm parameter range; In the case where the monitoring parameters include at least two, whether the alarm strategy is triggered is determined according to whether the at least two monitoring parameters are both within the corresponding alarm parameter range.

3. The method according to claim 2, characterized in that The real-time fault alarm is performed according to the alarm parameter range and / or the number of target monitoring parameters when the alarm strategy is triggered, including: Determining an alarm level according to the alarm parameter range and / or the number of target monitoring parameters when the alarm strategy is triggered; Perform real-time fault alarm according to the alarm strategy and alarm level; Wherein, the alarm level is determined according to at least one of the following: Determining the alarm level according to the highest level in the alarm parameter range where the target monitoring parameter is located when the alarm strategy is triggered; Determine the alarm level according to the number of the target monitoring parameters when the alarm strategy is triggered, wherein the greater the number of the target monitoring parameters, the higher the alarm level; The alarm level is determined according to the number of the target monitoring parameters and the level of each target monitoring parameter in the alarm parameter range.

4. The method according to claim 1, characterized in that: Also includes: If the target device satisfies a first condition, a maintenance work order is generated, wherein the first condition is satisfied including: at least one of: detecting a work order input, reaching a preconfigured maintenance time point, detecting an alarm, or detecting that at least one preset parameter reaches a threshold; Distributing the maintenance work order to at least one maintenance terminal; Receive the maintenance report uploaded by the maintenance terminal, and store and / or display it.

5. The method according to claim 1, characterized in that Also includes: The first operating data is displayed in three dimensions according to the pre-created component library.

6. A control device for distributed processing of kitchen waste, characterized in that: include: A first processing module, used for obtaining first operation data of at least one food waste treatment device at a current moment; A second processing module, configured to predict whether the target device is abnormal according to historical operation data of the target device within a preset time period and the first operation data corresponding to the target device, and obtain a prediction result, wherein the target device is any one of the at least one food waste treatment device; A third processing module is used to issue a fault prediction alarm when the prediction result shows that there is an abnormality; The first operation data and the historical operation data include: at least one of temperature, humidity, oxygen concentration, pH, garbage filling degree, ammonia concentration and hydrogen sulfide concentration; The second processing module comprises: A first processing unit, configured to respectively obtain a first Mahalanobis distance corresponding to the operation data at each moment in the historical operation data and a second Mahalanobis distance corresponding to the first operation data; a second processing unit, configured to determine a prediction threshold according to an average value of the first Mahalanobis distance and an operation mode of the target device; A third processing unit, configured to determine that the prediction result is abnormal when the second Mahalanobis distance is greater than the prediction threshold; The first processing unit comprises: A first sub-processing unit, configured to perform normalization preprocessing according to the historical operation data to obtain preprocessed transition data; A second sub-processing unit is used to perform principal component analysis on the transition data to obtain a principal component feature vector; A third sub-processing unit, configured to obtain, according to the principal component eigenvector, the first Mahalanobis distance corresponding to the operation data at each moment in the historical operation data; The device further comprises: A fourth processing module, configured to determine at least one alarm policy according to the device type and / or policy group information of the target device, wherein each of the alarm policies includes at least one monitoring parameter and at least one alarm parameter range corresponding thereto; The fifth processing module is used to perform real-time fault alarm when any one of the alarm strategies is triggered according to the alarm parameter range and / or the number of target monitoring parameters when the alarm strategy is triggered, wherein the target monitoring parameter is the monitoring parameter within the corresponding alarm parameter range.

7. An electronic device for distributed processing of kitchen waste, characterized in that: The electronic device is connected to at least one food waste treatment device via Internet of Things communication, wherein the electronic device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, and when the computer program is executed by the processor, the steps of the control method for distributed food waste treatment as described in any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the computer program implements the steps of the control method for distributed processing of food waste according to any one of claims 1 to 5.

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